Computational_Drug_Discovery_TB.ipynb

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        "# Computational Drug Discovery in *Mycobacterium tuberculosis*\n",
        "\n",
        "###Project by Murrel Saldanha\n",
        "\n",
        "###Derived from code from [Data Professor](https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb//) \n",
        "\n",
        "\n",
        "---\n",
        "\n"
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        "#Introduction\n",
        "\n",
        "###Tuberculosis caused by the elusive Mycobacterium tuberculosis is responsible for 10 million cases and 1.5 million deaths around the world making it one of the deadliest pathogens known to man. Patient symptoms include persistent cough, chest pain, weakness and fatigue with around 25% of the world population carrying the bacteria but only 5-15% manifesting the disease and its symptoms [WHO 2020 Report](https://www.who.int/health-topics/tuberculosis). \n",
        "\n",
        "\n",
        "###The BCG vaccine is known to have an efficacy of only 50% and the drugs currently available require long dosage periods, have multiple side effects and presence of multidrug and total drug resistant strains have rendered most of them ineffective ([Colditz et al](https://jamanetwork.com/journals/jama/article-abstract/366365)). MDR strains or multi-drug resistant strains are those that are resistant to first line agents- Isoniazid (INH) and Rifampicin (RIF) and XDR strains are extensively resistant strains that are not only MDR are also resistant to any fluoroquinolone and to at least one of the injectable second-line drugs: kanamycin, capreomycin or amikacin ( [Zhang et al.](https://onlinelibrary.wiley.com/doi/abs/10.1128/9781555817657.ch8)). Moreover, total drug resistant strains are those that show resistance to almost all laboratory strains tested are emerging all over the world, especially in TB endemic regions (World Health Organization (2010). Treatment of Tuberculosis: Guidelines. Geneva.).\n",
        "\n",
        "\n",
        "\n",
        "\n",
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ncxxXM5YQxy438HHFAHM6HeXllDqlysLzywWivGjE/M3OaSbxfqcUEDRXcEwaMSCTyDiVyQPJA7H39q6QeIrc65daatuyJbKGmuGIVU71PHrGhtHAyXNptdiYhx94dfoaAOYuPG2o7/s0Fugu18wyRNGTsUZ2mmf2/rY1GzFzc7FilkSRUiws/wAmVHtzXWw6rpFwslxFc2zLt3s4xyM4BJ+vFZt74nsoPEkOi+TFI5USyPIwATP3cccmgDnrnxJrdnbw3rwCa7e3DsQhVIlJ6kdyKv2vijUo77TY9Slt/s90jAvbxlskdGI/hBFdLb6tpd5PLaQXME0sQ+ePPY8f/Wq8ttApysMfyjaDtHA9KAHW8yXESywnMbrlTip6YiqBtUbVHG0DgU+gAooooAKKKKAOW+JH/JNvEf8A14S/+g0UfEj/AJJt4j/68Jf/AEGigBPht/yTbw5/14Rfyrqu1cr8Nv8Akm3hz/rwi/lXVdqACiiigApvbApx6VyXj7V9S0XQFutI+e/EoEUGzd5vqPbigDqiSOnU+tGa8ouvGGuRatELW+M58q3aK0Nsf9JZz84z22/0q94T8SeItS8W6lYajuitP3nkM0JGSrY+U98d80Aek859qUDn39q8f1HxJ4s0rQNSujfNNNDqRtUP2c4CD+L3zXpXhi+udS8O2N3eKVuJYgXyu3n6UAa+BS4oooATA9KMClooATAowKWigAxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSUtNPXPagDm9c8Ntql/NMlwkaXFobaRCoyecgg1mah4GkvCYor/y7d5hK8IjGCRjGPfim6/qeoW3iW6Nqf30MUKW0bDK4dwHfHfGap3Hi3X7a71WJ4beOOyBX50IbIxiQdiDmgDoIfDLxadqli1wrQXrblIQAr0zn1qO18IQwanFdNKkgjdmVCmByu3p7Vgnxlq3lqiyQ+WpYrf8AkHy5yAPkUdjzjNSXuua/e6Re3SSrp6xTwKoaEkopI3k+vWgDRs/B1xZpbMt3GWgvWuUjKDYinqopb/wW95qlzKLxVtLmTzZE2/OGxjhvT2rLHjPXTcakRaQiC2BVAynfgYxLjupBJxRYX92PD+q3DTPeN9uUJIqlAy/7I9KANKz8DJBZwQSSxZgkLB1jyXUqVw3vzRH4DgSBomuz8zxsGC/MAueB6dafoes6zeaj5F2I/Jmt5JImSMgxlTjB9a5y21bXkt7O8W+E1xBbztMrQnacOMKff3oA2rbwI1pZRpbzwLcW0mYJCmQy4xhx3q9J4RF34etNEuLotaKSZ1Vcebkkj6YP8qq6F4m1jVPEc9tPbQw2aKx8sgiRcchvcGual1bX2s7qyimuUe5uPtkMoU/u4A2GT6nH60AdXJ4V1Fpfk1ULHLbrbTHZy6qTj8ccU228ERafrY1S0nAnV1+8vWMDBX6+9ZVx441Nr29gs490aQr5TSQEGN++fX1p+qazqt1banYzXCRXFuqokSRENIcj94D/AHfagDpr/wAP/bby+n+0FPtNp9n24zj3qDwt4YfQBL5lwr70Vdka7V44z9aseGLy+u7e4i1Habi1l8oui7Q/yg5/Wt0ADpQAYFLRRQAn5UjDkexrD8Ra2+iLaSnaIJJCsjkZ28Ej9a5aXxh4gGn3N9FbxMkaxqqeWQQWP3j7AUAdVqGg/b7y6n88oLixNptx90ZzmsO98ARzOs8Use5NgWMphGCgg5Hvmiw8Tas+qaXb6h5Ahu42Aa3QtubPBz/CKq3vjDVE1zUbKyjSRYlKRs8RARwwByf4hzQB1Nzoi3FrpsCSeWlnIr7V6HAxj9a5HVfB99DYXHkXIuJI8JbRoBuhBfcWPqQO1aGm+INcn8XPpc8Vv9nhO2RiCGcYzvX2rN0ybWbHUJpIyJ5ru9n3s8Zz5aDKqPTNAEmk+C3uo7W8ubeOCVEMMkMi53qOkg9GJ5q+PA039oadO+pGSGzk8wRlAMncTWfH4t1ueEpbiCWaUrhvKIFsTnKMO54zmtTQde1e4nmGphDGLd5QUhKlNrlfxz1oAtX3g61vn1d5ZCJdR2jzB1XaOn0qDTvBy2jwyu0IkWGWJticEsAA31wKg0DxXdajpurXF3JHDHatmKdoiBtI4JXvWcvi/VZbO2bzreIujyLcmElZwCcLj+EmgC5d+FWfUNAsYUZI7KMfaJwuFkjH8BHfnmtXVfCyalf3F2zqu9UUJtGfl569s1jjxZq0t7APIWJ2aNBYPGd8gYDc4bsBn9Kt6fr+sSQ3UcloLnUYZtklsP3flKSdrZ/iyKAM7wroN2L+8j1Ox/cNyrt8rLhiQox1Ar0AAYAAB9q4u/8AFOqQaw0CxJG0Toq2bIS84I5Kt2qHwhrF3d67cR36vJcy72YhjtgVWwEI7HFAHfDrS0gpaACiiigAooooA5b4kf8AJNvEf/XhL/6DRR8SP+SbeI/+vCX/ANBooAT4bf8AJNvDn/XhF/Kuq7Vyvw2/5Jt4c/68Iv5V1XagAooooARvumsPxFrsPh+1huZbdp3klESKhA5Pua3D0NYHifw82v2VvEsyxvBMJl3jIJHYigCQatpsUfmzeTBcRR72i4LrwTjimWPiPQ722sZobyFRdLmBZDhj26H8s1Tfwm0+sRapNcqLhbb7MypHhfLIOR9eapQeAytzZT3FxDOYI/IdXi/gBypX0I4oA2v+Eg8PyxTH7daukLASDdnDZ9Mdc1Pb63Zvc3EJkSMxE9W+8AM5A/KsC48BLJZLAlyu8ReVuKdDuzu46mpZ/A4ubZUlv5Gm+0LK823DOoAyn0OKANK+8WabawSvBMl1NHtJgiYbsE4zWjBq1nciHyrmNjKhdRu5Kjqfwrl08AwRS36pKiQ3DiSN1T94nzbmXPpT9B8Nt9q1q5uYnt47smGCJvvRx4xkemetAGjF4y0iW9ngW7j8qFVLTk/KWJxt+taJ1vT1i81ruIR4JDFvSubtfBEiXEEl3exTLAUCIsIUFUPAPqfequq+DbxIbyaG6NwuHNvbrGAVLnJOfb0oA6y217TbyzlvILyJreIkSSA/dx1pieJNIkaILqEGZlLJlsZFc7oXhlrnw5Na31sIJ/nRLj+KQMBliPU4/Spm8CwDV470NC6+SkbpJHnlRgFfT3oA2D4m0/8AdyLPGbZlZjPvAAC8U1PFelPfyWQuR5sduLkkjA8s981hx+A5FtDHLepKQJAkTR/IgY5wB6Cph4PvooiINWImeyFtJM0QLkhsjB9Oo+lAG3/wk+jbYG/tK3K3AzEQ33ucfzNR23inTLqwvL2OdvIs5DHM237pHWsKy8AJbWhhku/MZlYb/LHykyBsj06YrYsfD7WlnqNoZ0eG8JZf3QBXK4OfWgCSHxdo072iJeANdjMAZSN1bYPrXG3XgcTalpNyl4FWxjRNvlj5tpyCPSuyUcCgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKac806mn/IoAyrufSI9YtxcPCt+V2w7vvEegpbu20vVGuLCfyZZQo86NT8wHUZxziqGr6Ffajr1lei6t1tbXLLC8eSX/vZrG03w34j0zXNTvlu7a4e5gUCZ0wWbPf6DigDqZbjS7We206ZreN25hgfAzj0HSmprGj3C3Ki8tnWHmYE8Dnv+NZuq6Bf6rqNnK91bi2hX95EY+WcjBYN178VQHgUyJiW+XEMYjttkeMAHPz/3qANW78RaPb2sV3AUvJLh/IhSDDM7f3fb8a0bieOw0s3BtgiqgYwrjr6AjjNcxP4GkuC11LdRNfvIzuTH+7XIwdo7HHetNNCvJdHudGmux9kEaR20v/LTAHO71NAF8a3pKxmRrqBFQhGO4YVj/D9fan/bNMww862Xkg89+pH5c1zc3gaaRLxYruKNZ2Ro1EQIiZerj/aps3gSS7uHW41NntW3MBt/ebmVQST/AMBoA6WLU9Jkt5dQjubdok/1kwI+X2JqjL4msX1TTrCyCXTXqsyyIQAqDr/+qqVv4P8AK8Oy6Y00SSvIsgmiTgspyCV/nU2k+FP7P1KLUZbhHuFMjSBE2qWbHQdsY6UAMl8YaFDPgFGX7SbWeYptEbgd89fStf8AtLSXvoYhc2xuZk3R5IJZSMjH4VjjweG1CSZ7hGg+0PPHG0Q4Zhg59ag07wLFp2sQXglScKihxInzAquBtPYc0AdkgCkjGCeT7mn0wcHOetKGBOM5oAdQaBRQBn39zY2qKt68KpI+EWXnc3oAepqraa5pV888ENxETDIYXVxgEgZI9+Kj8R6K+uW0UMcqQSxSiRZmXcyY/u+9Zdz4Pu5A7QajGknnGWN2hHBZdrZHfPWgC9e+IdG064skQwyTTsI4xHgbEP8AFnsK0ludOdpJVkgOEEjsMfdPQ59K5hPBUli0TpdrLbRuk8sRiBeQouMA9gfSnaJ4UZ/Deo2t0ZoJL92ZAx+aGMH5EP05/OgDQuvFGmpd6clksd3cXsnkoykAqB15rXivrOe4nhiuYmlt/wDWqDynHU1gab4PNpf299NdRyXCPl9seFIxgYHarN9oVwmo3d/ZOh+1FEuIiv3owPmx/tUAXF1nRUgSUXdsiSvgODgM9Twahp893Lbw3ET3EQ+dFwSM1xZ0LVj4ebT0tZfL+1iSyikYEwIGBHmeq9elXdK8NXMHiWe8XMVlAkixRMRl5HB3HI7Z6A9KAN+LWNDkiuPJvLVo4OZVXGB9eKqS67oiTPZwLFcSJEtysSKNpVjgMD0rA0/wZqUun757pLW4CtFHGsYPyFsnd6mr1v4FktbQWqagDG1qsEhMfzcPuBB9O1AG82r6OZpme8tVnt0/eneC0YPUE9qz9P1Hw5ptxDBZ3KmS+3z+ZuL7gvXLH09Kzofh/BG14izRGKaQSrIUy4JbcVJ7jNXk8JNDeR3ENxF96QyRvECCjgAgflQBpz6nYm1h1CELco0gjR0I7+hNS2GpaVez3H2K4gkmQjzjHjPpyaxbTwoV8PLpjX+9VuPNRlHCAZ+Ue3NTaZ4XGlyh4p1Di2a3DJHggs27dQBt2Op2eoBzZ3EcwjOxwhztPvV2uZ8L+GpdCnvbia9+0y3ZDN8uBkdT+NdN2oAKKKKACiiigDlviR/yTbxH/wBeEv8A6DRR8SP+SbeI/wDrwl/9BooAT4bf8k28Of8AXhF/Kuq7Vyvw2/5Jt4c/68Iv5V1XagAooooAKMZoooATaPSlxRRQAYpMClooATA9KBjFLRQAYprAEU6igBBS4FFFACEA9qXFFFABgelJgUtFACYHpS0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJS000AcXrUyP4s+z6veT2lgsKm12EhXctg5I7+1YjXVyuq6jLaX10+rLqGy3tiTtaLA6jGMdea63V9ejs9atNMl0ia6e4y0TjBUYGSeafpniPRtQs3vkmt4nQfvskApg45NAGX4ZltrjUHe7v7g6vvcSWzkgKM9h0xis4eM9U+03JHkTxI7hkjjOYAr4yx78V2LanpMNumovcWyxyfKtxkYPtmqsU+haPZ3MkUkAhYvJLt+YscZP145oA5G98U3s+pQ6hbTpEkbTRxBo223GNvy49eTimx6zqVg090tvNJLukkKNkiMnt9BXTWvifQ7y8tbaNolSaJZ7Z2AAJJOAPQ8VonWdGAuCb21AgP77LDC545/GgDl7rxXfQXWl2lvdR3T3C/vZEhIU5HDA+x4qhb+IdVt7Ozlkl+03XkSq85QhYzv8AvMO4wK7u2vtJnkMdrJbPJEucLj5R1/Cmw6lpM8LNBcWzRvuJYEYIGA317UAcf/wlWvSREW4t2WGN5TcGFgtwqnGFHarXiTXLy31PSrm3WYW9uFluo1U/P5gwq/nXSRavoxlFvFd224BRsDABQeg/+tT31jSPMCPe24ZgTgsO1AHnp1XXLaxR57m4aWDUDd3CqhP7kHAjH5ipm1fWoWuL2Z7kvJqSIsCqflR0IUfQd67ybVdJtxma8tlDEfeYcn/OKrz+IdNi1uz0tGjmurhiMIQdgAzk0AcG9z4gm0e5tklnmuLW4+xL1UTOSSxz6AY5rR0G9m02802ae8ldJ4pPPick+SFPC/geM1vSeMtDjdB5q+Wt0baST7ojkFaCS6L/AGquJrU3dxGAvzAl168D9aANZHDAFScEZBqSmDjHbPanmgDm/Fmr3mj2ltPaiPaZ1WUldzbfYetc9N4m8RtNJLB5At9zlFaA7tqkcfU5rtdRvbCyiD6hLDHGW+Qy9z7e9QvqmnC3edbiCQBNwAYfNkZA/SgDibrxVe6leXVlGpW3CJIpWNlZG7gnuKtJrl3o/glrkO4ujcuqGRC+e4z7YrYh8V6VPY295bQpI9xKsUiLjcmeMn2qe78SaZBpck0ixO6R+a1sCCQM4zQBzs3i7XTYT3kUMKRxrGgBQnBYcuT6CpY/F2prq+kWcoidpyFuAkZ2nPRlb0NdNFrmizafHc/bLXyJjtwSMMfSoH1zSk12z0uDyZbmQMB5eD5QUZ6+lAGDq3iTVRfX1pD5aYkkt44fLPmBQpPnA+me1VbyGaw8EaM0mobWQCaeMkobgnkjPUHmuqTXdJDl7qaC3lLvEvmMCWAOPyqLXtbg0u6srSXTJLv7S2IjGoKqR9aAOcfxhqkU+Le0woTCWbxkykbc7y3pT7nxRfX6WxtrxLeGG6gWeZYiQQQdyn8eK6LTvEmj39vLetJDA0OUYyMMqOn5VPJqWmK9jbxxpMl+58oxKCpxySaAOf8ACt/qcF4bW9YPZymeWNihDJtcjGfTA4+tYb61rFy2quVu7eDUYZPsjOmUQqcDGORnmvQjrOjxTzRPe2yyQLmQFvu84/nTRrGjyLbqLy2KzZMIBBDY7j0oA4+O71O1037PsBtrR4URUVgzkrliT35pbnxdqFv4fmvRcRfa3kKR2ogJMOAfkc+vFdQ3iXRft1raR3Ucs10xWMRjOSvqa1/s0GWYwRkudzHaOT60AUtH1SHU7VSrqLkRqZox/ASM1qVXhtLeCaSaKJUklxvYDk/WrFABRRRQAUUUUAct8SP+SbeI/wDrwl/9Boo+JH/JNvEf/XhL/wCg0UAJ8Nv+SbeHP+vCL+VdV2rlfht/yTbw5/14Rfyrqu1ABRRRQAUUUUAFFFIWxQAtBqjqGq2Wk2r3V/dRW8KjlpGx+XrXJHx3qOsEp4W0G4vY+gu7n9zF9Rnk0Ad0WHvSZP0rhJtI8fahA8t14gtNPYKSIbODcc9hk15PYax4i1CyW4uPE+oGQsyOqYABViP6UAfSYJNHzelfOqah4hikzD4p1FD1AcBhWraeO/G+nsC15Zaig/gmj2M34igD3YdTS15ppPxg06SVLfXrObSpTgeY3zRZ/wB4dK9CtbyC9gWe2mjnhcZWSNgVI+ooAs0U0HNOoAKKM0maAFoppbAyTj60Z/8A15oAdSZrkNV8XXFhqF9arbBvLVfIbn5j/F+QpE8V3LagyiKF4d4QIpO8ZGQ30oA7DNFcx4f1++1tLtWhijeL/Vtg4z6Y7/Ws+48WapbaVBdvBbbppnQAZ+UKcGgDtx1parWNx9rs4bgDAkQNj0zVmgAooooAKKSloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiikz270AZOoaQbvWtP1ETmM2aygIB97euK5u9+HkFzYpbrc7PLj2L8mNx3bst61d17xFcaf4ksbG1mgkaRWZ7dlIYgd93T2FZEfjLVhBLKUhm3xl2CIf9DO7GH9eKANbUPCMt14dtdJgu4rVI2LSqifI+faoIfA00U0cZ1ENZICfL8r5ixTaefSqTeLNU8ow+dbiMSMo1IxnynwMgAe/Sui03WNSvbKxnfSiBOoMrF8bM+1AGZ/whJmtPLuLpDILeO3V0jxhVJOR6ZzUSeAmaVRPeq0UP+pVYsEjdu+f+9+NQW/jK5jvvMvvK+ykSebAiHfZ7DgFj3zU2l+L7vU/C+qahGlu15bSsiRpkqo/h3e/PNAGtaeHza6/e6jHMgt7mHY1siYVm6ZNY48C3qWZhh1RIzmVVQQ5SON8ZCjt0pg8Sa9/bEtl5FoBFCCfMJXflc7x7Z7VDF46vFsY3dFeRofkJjI82XdgqPbFAGr/AMITD/ZF/Zpd7ZLkoy3CoAY2UYBFVJPA9rZag+pFFuIRCBLEy5csByynsTVHT9R1a11SBRd4gb7WRFMDiRw3yjPYVLeajf674LtZFmaO4kvFjnKoVyA3IHtQBHaeAmu9Ks5bplN4sjSOk53LsJ+VTjuBitmy8ISWOsW9zHdobOCZpkj8r58kYK7uu32rNm8Rata2sTO9tbwPNIiTMjEII+in3bnmmHxN4juTGyRQWwYrGY5IySpMZbOfqKANdvCBlupne6RoWuWuUj8oE7iuMH1qvp3gKLT9YgvzKs+xQD5i8qR0KntUOr+Lryx8PaVeLGFurg5aLyyQxBwfp61Y0nxBrF3rcCXMcQtJ3kjVFUhk2jIJPvQB2QoNIvrTqAOZ8V6Zf6k2mCwWLdDcl3aVdyquCM471k/8IDMrrHFqIW2Yq8w8vLMwB6eg56VpeNf7Qa30+PTLh4Lh7oEMvQgDOD7Guah8V6orXj/NZ7rl9zXSkqjIB+7X/e7UAa9t4HkV45p71DJDtWPyo8AqpyM+pobwIxdVF/5cPkCFwiczYOfmNVZfGOtxrNGNKAmgg+1SLtOBGw4GfXNRyeKPEP2OJ4hZswiaVnVWZXAZQF9jg0AXLzwBb3N2tyJo2ZZNxhdcR7cAEADvxVzTfCUunaxBcrdRtaWzO8UXlDed4wQW6kVzl14h1iPVYLiSWKOWAPGAFbZcE4IA9+cVbTxfr8pv5Fs4YfKUqsD53xHsx9RQBpnwJGzzs11vaRZApZfulmDZH0xW1eaQ13fWNwZ9v2WN1CgZ3MVxn8KyPEnimXw1oETSPDJqUibhuBWPryx9gDWr/bdrLpcs0d5EkkVuJXOCQgIyCR6UAc7dfDmKW1jihuxGyAAgp8snPJati68PXLXmlzWNxFbpZRPEYzF8pDYyV9Dwa5tPGeozabBKtxbR+YkkiTlGKybTwmOzGrB8Yas95H/o6QvvRBYshMkoIyXU9MUATReAS03mXV+sqK2fL8vAI37sn1JxUg8CvCLmK3vgkVzEyPmL5k5yNnoO1VfDGvSjVLx9X3+dJu3SByVgG/CxkdjXfAZAPX3oA42z8I3Ol/ZZ7OW2+0W0LoR5XDMeh+vrXW2fni0iF0VNxtHmbOm7HOKnAoxQAuBRRRQAUUUUAFFFFAHLfEj/AJJt4j/68Jf/AEGij4kf8k28R/8AXhL/AOg0UAJ8Nv8Akm3hz/rwi/lXVdq5X4bf8k28Of8AXhF/Kuq7UAFFFFABRRSbhnGaAGlxzXJ+IvFxs9RTRdFtDqOtyjPlKcJAv96Ruw9uppPF/iO8s5YNC0NFl16/4izytuneR/Yc498VoeF/C9p4asTFEWnu5m33V3J9+d+7H+g9KAMjTPASz3a6p4quf7Y1PO4K4/cQeyJ/U12aRqiBEUKo6ADGKeOM8d6WgBCOK+a4YTZahq9iRt+z6hKAPqc/1r6VNfPniSA2nxF8RQtwJmjuFH1GKAKeSO1KuRRg96PagBHCuuxwCh7EZz7UaVean4XuxdaBcGNc5ksZDmGT2A/hPvS+o9OaP8KAPY/B/jbT/FlswjU2+oRD9/ZyH5k9x6iumkniiUtI4QDkknFfOLLcwXcWpadKbbUoB+5lXIB9Qw7g1674I1vTPFelG5MW3UIX23cMrEmNx3HseooA6ZtVhOfIjlnP+wvH500tqc/3Fit1P975jV4LswEUBfbinDNAGeNL8x99xcSyt/dzhavqgSPaAOOgp9I1AGFcaro0epG1nRfO3BWdk+UFhwM+9XFj02NpJlW2RkGHfjKj0NZreG0m1u7vrl96SYMUeeAwGMke3Wsy18HTpC8c0kbkzKzyFyd6g56UAdTE9gsmImgD7ASAQCF/wpj/ANmNFEJDbGNmPl5xgnvisO48NXco1GCN4hFctvjmyQ68j5PpxUH/AAiEjWccRaNCsLrt3EhXPRh9KAOkh1Ow/fRx3EQFthZADgJ6VOb22VkUzxguMqC3WuYl8NX6x30cElsou9m9yOeBiseTSbq11WztpLWW5VEUNtz8xyfmDeg9KAPQkuIpHdI5FZkOGAblfrVK/wBcsbCykunmV0jbYQjZO70rH0XwzNp73hlmBeVCizKx3MCc5b3qnD4PuTA0c8sPAVAqjIkwclm96AOtS9hKRGRlieQAhGYZqpa69aXdwIU3AkMQW6YU4NZ+uaHd6lqNpNBLFHDAQcEcjB7U/TdCms9QS4klVkCyKVx13NmgDVGraeVd1vISqHDHfwDUsl3bxRebJNGsf94tgc1y934Tml064ghaBZJb03AwuAFx93696nk8O3As9JVHimeyJ3xy/dkyMZPuKAOg+2225l+0R7kG5hvHA9aVby2YxgTxkyfcG4fN9K5WXw3fTahdXM0kTrNG0exDgYPQfhUcfhO8W5s5bmZZ/KQIRv2iPHTHrxQB1j39qkE05nQxQ58xg2duPWq0Ou2E94tsk6kvGJEfPDg+lZenaBcW2m6naSyR+XdFjEnXbkdz3zVP/hE7l0RpDAssMMcUezgLtPXFAHTXGq2NrDJLNcxqkZ2ud33SfWpWu7dERnmQCT7hJ+99K5FvCd9O+ovLNA32gDYMcZBz81Xtb0O/1WCyijeCNIVHmDnG4YxtoA6Bry2UsGnjUr97LAYqVHV1DKwZT0IOQa5G68JzSWNwqSqLiS4EpwSA6gfdJ7V0Wl2g0/Tobfp5YxjOce2aALwpaaGXoD0pwNABRRRQAUUUUAFFFFABRRRQAU09adTWwePWgDHvdS062v5VvFijEMYdpnA7nAA7+9RRa9oZv/sSXEBllhNwzKo2sg6knvUOp+F11LWDfNcYU+XmMjOdrZ/XpWVP4F325t/tyJFJG8L5j+Yhm3Lg9sHt6UAdANW0JoII/tdmYpgWiXjBx14py+IdH+yG4XUIPJ37C+7gMex9KxLPwWkCKWeBJPIeJhFFhST/ABD0qC98ByS2U1ra6iIIpgiSIIhhlC4/PvQBrtr+jy3N3awrHNNHOkM6lQOWGQSe4os7/wAN2EN7Ja3NnFEsm64IIA3njJrOk8FNI0qG9CwSvFIQqfMGQEHn3pkPgeR5IZLu7jfyNqwqsIA2A5+cfxNQBu3GsaPGqymeCR3hZ4gCMuoGTg1GmtaT/ZVrfXnk20UmTEsoGR9KwrnwHNcXNuRqhS2hZmEXlDIJ9D2HNaj+Fkkt9MgeUMLKORPmXO7cMZ/CgC7eaxo1tvFxcWxaOMzbRgnbjJIH0pbPWtIubGGWC5gWJwCit8vXkVz1p4Ait7mRjOk0c0O12kXLq4UqGU+hB6Ul18PzqMNubu+2zRW/k5hjwu4H5Xx6gUAdIdW0aaVbQ3dq8jAusZYdu9VYvEmlTailtFJE8bxb/PBG0ndtAHqeay08DMkwRb3FoWWZxsHmeaFxkN2U9SKe3gj/AEjSLmG88qbTIBHHhMoxJyxI75GR+NAG/d6npdvci1uZoFnCmRY2xnArDi8YRt4fOtrpkkkBkKIsRG7HqfSl1Dwe19q81yt0qQXDLJIpTLh1GBtPYe1XT4c3eFW0QT7crt81Vx3z0oAqy+N7W3vobS4srmJ3EbPkg7N/AzjrXUqa4/VPBUmoa2L4XkSIVhU5iy67P7pzxmuvXA4549aABkD4yAcHIyOlRvBE+VeNHBbdhlHX1qbIz70hI+tAHO3Hi3TLa48i4RxKbsWbDZ/FjOT7c1Hb+KtHM0sCqsIijeUbgAGVTyRUWreDYdU1O/vvtDI13afZwo6Rv2lHvjioR4GtC0Xnus6xWH2RVZMYP9/PqaAJm8V6fcxQyWmmzXo8vz32Iv7pf731+lX7nXdIttJGtSSQiKWLerMMNIBzj61lQeEr/TogumamsO+EQz+ZFuBUd1HY9qffeDg2n2Ftp90sRtYmjBmTerBxgkj1oAtXOsaDqelg3/kOHtzcG3kwWK4J/kKq6Lr+iXOlSalKtpZxyBUwzjJTHyhh247VVtvAMVpdGT7RHLG8e1jKm51bGMqfy4qdPBMcU0UhuFcRqgIMfDbU29KANr7ZosbpB5tovmL5qIcAY9ajv9d0qx0a414kTW8A+aSJQzYzjisO28Ax22oLO0sFzGU2utxFlsgcbTngVctfCUq6B/ZV5e+dGbjzWUJhRH3jHtQBNceJ9It72a38svKbI352oMMgPAz/AHjTP+E1tY7Gae5s7m3niRX+zvjLK3Qg9P8ACqMfgEQo+NQZpWEqLI652xMMImM9FGPyqWXwbNqFnImragtxK0SwLsj2osYIyMe+OaAL/wDwltu/h6fV4rSeRLdis0QxuUjrz0IqpJ46tYVtTNZXEZniE+GIyqFsZx3PsKt2/hv7L4butEW4P2eQMkL45jU9s98Vman4Lm1JrMi8iVYLRbZt8W5gVbO5T2oA7RHDqrDOCARkYNPqKFPLijj3FtqAZPU8VLQAUUUUAFFFFAHLfEj/AJJt4j/68Jf/AEGij4kf8k28R/8AXhL/AOg0UAJ8Nv8Akm3hz/rwi/lXVdq5X4bf8k28Of8AXhF/Kuq7UAFFFFAAelZutapbaLo91qV22IbePe2OrY6D8a0T0NcF41J1vxJofhdc+TLJ9svMd406KfqaALHgTSbkx3HiXVVP9r6riRwf+WMQ+5GvsBzXar0FNVQoCgYAGAKeKACiiigBD0rxH4kQm3+JaPjC3mn4+pUn/GvbzzXkXxfh8vW/Dl4B95pICfqB/hQBx1IWCqzZACjLH0FKcuwBP3jXG694vns9TnsUt18jaY33Dls0AdPZapZalK6WlwJHj5Zc4qS0a4MCi8RIp8k+Wh4x2NeR2l1c288hsHlRn4xH1x6V6RoET3lna398S1/GpVfnwdue4oA2+nNLYaxL4V8QW/iGAsYflivoR0eMn7/1XP60mMHoc5xSMiSq0Lj5XUqfxoA+iraeK5top4WDRSKHRh0IIyKlrzj4QaxJdeHZ9GumzcaVKYlz1MR5T9K9GU5UUALSGlpDQByt/wCJL6HU5rW3s4pFSURqzt1JANRJ4vZ1CtAiSMIwAT1ZuuPpXUGztyxYwpnO7OO9R/2dZGRJDaxb0GFO3pQBx9nrOohEjedDC1pLIXc/PlT2obxFdzvYxQt5cccsKylm/eS5APA9Oetdg1hZuVzbRnYCF+XoD1oOnWTSrIbWIuo2glegoA51fFk8VtDNc20SJOjumG7KcYP1pbbxNeXGpR2sdmhQrucAncF254roprCzlRVlt4nVTlQV6GsxfDdoNYGoCSQNu3hAcAH/AAoAqXPiS5iuZ1WyIit7X7TJv+91+7j1qvF4sujZwXEtmkcckwRpN3yhD0OOveurNvEzs7RrucbWOOoqt/ZdiEVBax7VbcBjgN60Ac7F4imgMalQ8c1y8fmyfNt9OlQL4tu7TSUnufIknMzKVB52g44Fdb9gtNyMbePch3L8vAPrUZ0qwJJa0iYk7idvc0Ac+/ie9e6eCK2jRGLpHITyGC5/KnNqt5ceG9MlaVBPdypHJJFwACf510gtLYHd5EYI77agm0qylszZ+UqRZ3AIMYYc5oA5xvEN1YrJHBF9oKyyAmVsEKvFOutfubzT9QkgCQRwqu1t3zljt5A9Oa6UWdv3gQ54ORnOeuaP7Ps2k8w20e8gD7vbtQBzj+LWgBhaBZJFZwQrckKpbd9CRWfDr16b55pZRseWMrHE+VAIJx9fWu0/s+y8wy/ZYd+MFtnOKamm2UXEdrEuWzwvegDmR4uu1tpJZbOJBtDoQxxtJIyfyqzPr99beG11ZraPzN+PJySXXt+PetXUNGtNQsjbbREOMMgwQB2p2n6Ta2FnHbIpkRCWXzOTk0AYN14ou4Z0wtq0fkJMwDHc5bjC/SptK8R3eoa5cWDWqJHHnawJyMevr+Fb/wDZ1l5scotYt8fKNt5WpFtYEfesKBwSQwHegDlLjxHqlrDfySR2jG2mESbdwBJ9RVnStWuLzWLNi37u8ti7RD7qFTjj610D2VtKkiSQRsshy4IyGNRwaba29291FHiQoE9gB6CgC4vYfrTqQd6WgAooooAKKKKACiiigApp606igDjvEXiDVdO1C9FpBHJbWlqJWUqSzMzYyO2B1rAm1TXLybTdQuUjkW2hnnFvC2VmK42nI6YzXphRWYkqpzweKi8uOCFjFAvygkKqgZ9qAOG0/wAUeIbtbRjb2v7xZJjsO7eqrnbx3qG18VeI7vT42SK0jkuJmVCct5Shd2CBzntWpbeM7GNPMk0uWzgFz5BkkCgBj1PHbit6PU9HWaCGO4thJcgvEFx81AHIjxhqrQ3Fx5EaNBbCRbMj57humVPpxUjatquoeEDc3aqkouowHtXPK555H5V1MuqaRDcypLc2oltx84OMp9f8Kzh4l01dW/s63EHkJB9pll4CKpPBHqaAOS1HxRqF/NPbxXSwRW1yjRXZUhUHQh62dP8AFl62p2KaikVvazwswKKSSwJyx/urjmui/tTRJAifaLQidTIFIHzKO5pr65oKwRTvd2gSUFI2IHbgj6UAasEqTIssZDIwDBgcg5qesx9Y0yCSaJruFGt13SDOAg96hutetYV094yJo7yXy1dTwOM5oA2aTvXPz+K9LguLVTOrQTu8Zl7Iy44P51pxanZzSzQw3CPJCu59pzgUAXaWsO08UaPeWcN1HeRrHM7JHvOCSDg0t14n0qzCNJdKVa4FuCoz+8PagDbpDTAcnt+FSUAcx4x1ybQ9MWe2ZRcO+IwyEhsDODj1qloniW8vNTuYLtI1P2UTxRxgnAxzuPrmug1jVtP0xbf+0GULPKI49y5+btSi/wBKRmK3FqjlQWbIBx2zQBxTeK/EatpqC3tS12pmYt8qfex5eT3wM1L/AMJFqN1dPbzxqZ4b0BVQkRhSpxlu/Irq21bSprO6nheC4W0BZkUAlcf/AKqjsda0u5jtxmCKe4jE3kHG7nP60AcvZ+JdSuLzTpri8jihTzlukEZw7L0weh/Cobbxjrl9CY7WOAuJJWEzxnayKu4cdj2rqrvX9Fs9JN6Jbd4VzsVQMlh1wKbbeItJ+yLNOYbRHICh8fNkc0AZWh6rqV7e6x590gVrZJraDYcx5U5PvzWVbeKtXt9PslkuYZ5kj3SboyHuGLY2L6MK7dNU0ptQW0juIDdMMKq43ED0P5Vj6f4m0y/1K7hEVvFBZuw818Btw+8QP60AU/EHie+tNdt9OtAu6WPDoUO5SVyCD7VBHf6i3hLRbqS5e6uxd/vGiG0vgN8v6V0U+v6LDDLeSXELeRF5ruFDMqnpRd65ZWdtYSxwh4ryTC7QBtJGQTQByH/CcayNFuLyaO3hdnBtlUFjnqVcduKkv/FGsXNrfLG8dpMbNJ7fYu7byN27356V1ttqmjTMkPmWqTygSeUduRkZ598c0p1TT5bG6urBIrwwj94sIBJx2oAwtX1DUVn0gx6iot57OXzspxI4XPHoax08T6/pWiw2nkxzTlIglw2QsasufnJ79q2n8aWSafa3c2lXEaTbvLR9o2ovVh+ddXbm3vbSOURKYpVDAMg5HbIoAi0i5nu9Mtri7WNbh0y6xnK5z1HtWhTEUKAFAAA4AFPoAKKKKACiiigDlviR/wAk28R/9eEv/oNFHxI/5Jt4j/68Jf8A0GigBPht/wAk28Of9eEX8q6rtXK/Db/km3hz/rwi/lXVdqACiiigArhfDeNT+IniPU85W2EdnFntgZb9a7quI+HUYMGuXGdxm1SbJ+hxQB29FFFABRRRQAh6V5r8Z4ceGtPvQObW/jbPscj+telnpXE/Fe1N38OtVAHzRqsqnGeQR0oA8oYbXJz3rO1HRLHU4pTNBH9oKHZMf4TVi3vrW6hikju4W3oCQWGc/SrGMn5efagDk7TwNFatbym+bz0bc5ToR6V0rWluyj5COeGU4Iqxhh1G32xSDrnHX8qAKwW7h+6wuEH98YelW8gdjG+Y5P7r5FWOnBwfTnmmuqyDy5Iw69wwzQB0Hw8uzp/xJ8okeVqdoRgcZdDwffgV7cgwtfOOiQy2fjjw9NYzCKRp2jUS5ZOVORjtXuY1yexOzVbJ4QD/AK+Iboz/AIUAbtFVre9t7uLzLeVJUPdGzU+/gcUALijFG6k3jt260ALilppcCjdQAvFGB6UgOeox6U6gBMcUYpaKAExRilooATFGBS0UAJgUYpaKAExRilooATFGAKWigBMUYpaKAExRgelLRQAYooooAKKKKACiiigAooooAKM0UlACMRkZNJn+dcf4h1zWrTWHttP+zpDGsWWmUnczvt/DA5rKm8b6lbzWMTojzF2SeLyyA45wwoA3D4RTyyslzvX7absKUBByOlU7LwGlpqttdiZJ0QhnWVDkEfdK+n0qn/wluuWdq95exwPDHGJ5AisDGhJGPc9KueIIrq/8O6ZeXEtxbXMk8QYWshTajtyDj2oAn1XwUmox35NyqS3N4t0jFMgbRgK3qKjuvA8d1aPEJY490CKvlphVZTnOPQ+lYqXl5b+PHhW6n8iO8SDy/OLsVI4+Q/w+9bFz4ouorrWGnnht47MFI7cxkyMez+49+lADh4GgmtJY3liRpbcQ/ukwAwOcj29qYngVPsoR5YFfyXibZDhMnHzAevFZ9t411R4dPnmgiZJ45P3cQy07qOOOwNJH4x1xdKivp1tRC1yqu0Z3OF7pt/vCgDQuPArz2FxpxvkNsZRPGWjy+/OcMf4l9q0JPDDSaTY2sVxHbyWspk3Qp8uSCOh+tZHim91Aapd20N6EtpNN8yKEKQWl3dQfp2qH/hKdZtbdLOaS2ikh+9dSKSj7VB2D/aPagCeDwHdQIzrqUTTSSuXZ4crtYBTgf3uODWro/hdtG1NriK4AtzAITEBzIR/Gx9ayU8a37Sshs40uQzkwMDuCBA2T+tZUev65qSWWopcwfI8jxEIcFApJDL2PHGaANlPATebaSyXFvN9nLqFaH5TGzZ6f3h61Y/4Q24UXGy7hjD3aXEcQjyibTnP1NRaDq2qahfat9pughe1Sa2t9uDGSvJz35rKtvFus21hZK89vcyqoaQ7ObklsbF/2hQB6UucDcQWxyakqKIl1DMpViASD246VIaAMfXdKbVEtDHIkcttMJVLpuB7EflXOf8K7tv7Qa4a9lKvM7umOCjDhPwPIrW8W6tc6ZbQR2rxwG4Zke4kBKxjBP5npXOaL4i1a10QyN5bW1okQLyA7n3Nhic9gKANzTvCclotz515GzPafY4ykW3Cc4Lf3m5qlB4B+y3bzR3abjEAJXTLxyAEAr7c9Ko3fjXUpjaJafZ0a6lZYXcfLMA2ME9hgda2tD8SPPqV/aanLEskUwSJYxlSCOMN60AZkHw+uLaBmTUo2uJGbeXhymGABwOx4q2vgONY2R7sODG6LuQfKWXGR9KqanqWqwa/IIr8wWw1GONt4yChXoPxpPE+uXx1j7FHIkUVtcREW4B33IPPB9O1AEWj6Nf6b4y82Wy32aswSVlwkahQN4b1OOnt71qTeCY5/MYXSgSPKxIQZIf3rnbzVte8R6KElMMEct2gKpy6r83yOBzxgfnV2HxPrMMUBit4Ps8CjzowhzJlyuFPagDZu/CBuNQnljuUghltPs7IkXLHGAT64qaXwok9paK0yJPEQ0ska8SELjgdq58eMNffTL68MVrFHE+cOw3xDPI29zVrxVql8INLu9JluBPLGDcIqcLbnG58How7D60AWU8Aw/ZWie5JLPGxdVwwCptIzWh4e8N/2JY3EG6IvKu3zI0xnrgnPfmuXmvcalfTWupXT3kckC2MIcsJE2gnKd/c0eGrnVJfGFyLye4jgkmlCCSQskwAGVVTwuDnBoA2NS8ESXen6fbx3UJNosikzQ7g2/vjsR611enWv2KwgtTK0vlIF3v1bAxk15vqkd1YjxU9veahug2JAHuGZU3EZIyeOtdN4FuLiXTbtJ5pJfKuGCFmLgD/ZY8sKAOtxRRRQAUUUUAFFFFAHLfEj/km3iP8A68Jf/QaKPiR/yTbxH/14S/8AoNFACfDb/km3hz/rwi/lXVdq5X4bf8k28Of9eEX8q6rtQAUUZozQAHkVw/w4kCQ67a4IMOqTA/ic13FcJ4eP9mfEnxDphGEvES9i9+zYoA7umlgOtLVTUp5LfTrqaGPzJI4mdF/vEDpQBa3D3o3ivI9M8V6nEujakdaN/cX/AJn2zT9gxAACc4HK4wBzTdb+IOtyeGUxb29nPeQPPHN5hwsasBgerUAevbhjnqe1RzQxXELRSoskbjDKwyCPpXlVv431WyimhtY/tdw04RTdPhFCw7zj6mrrfFCX+09NhS1jeO6tt8keSHifaTg+3FAHSXXw+8IXYJm0GzyecquD+lY83wc8JzZMEd5bHt5Nwwx+dVW8f6vaaJpepX+mRbdTBFuIiTtc/dU/WvRrUu1vG0qhZCoLKOx9KAPMpfgwi5+x+JNQj9BIA9Zs/wAJ/E0IP2bXLK49BLAVP5ivZaKAPCLjwJ45teP7PsLtfWKbaT+BrNn0nxPZZ+1eFr3aP4oSGH86+hyDSNnGQeccUAfPPhtpZ/iD4dt5bK6tnErybbiPYeFPQ19DFQ4IPI7g964eU/2t8XoYxgx6PYl3wP8AlpIcD9K7penHSgDIuvDtlPJ50Ie0n7SQHafy6VCja5YSKsscd/ATgunyyAe471un2pD0oAyde1UaJol1qZhMogTzGjzgkd/xxXJzfEgpZWN5HpUk0N1bNeZ8wBkiBwfqfauv1xdMOlynWHRbJQDIZDhce9Ylv4a8I6zbwXNrBBPBEx8oxyHCk8lcf0oAqaT8RrfWPFY0S3spSGXcsuegxnJHpUd/8Q7jTr7XYZtGcR6TEJXkEn3weF4x3ro4/Dej29+L+Ozihu1beXXg/wD6qW58P6RfNeGe2jdr9Atx83+sUdKAI/CXiJPE+jDUEga3+co0bHOCK36ydNsNL8PWQtrVY7WDfk7n6seOSe9anmJkjcMgZPtQA6imCWMjIdcZxnNKXUYywGTgZNADsiisyLW7G41ebTIZw9zAu6QLzs9jWgJEAB3LgjPWgB9FM86Lbu8xNvruGKGmjQAtIqg9CTigB9FMEsbHAkUnGcA9qUyIpwXUHGeT2oAdRTTIg25dfm+7z1+lHmJgkuoA6knpQA6ikBB6EGloAKKKKACiiigAooooAKKKKACiiigAooooAKaTyRTqaaAMWbXrCPxC2kT7I7jylkUyAYf0GfUVZt7vS7q4mjgkt5Zbc/vAACUrH1rwm2r619ra4VIJEVJF2ZddpyNp7c1Hofg9NEN4YpU3zKUimwd4B/vepoAl1TUvD16LO2nuxsmuPLRIujuOdrDHStK91ewtZfsxZJLiMp+5GMqCeD+Fc9p3gafT3jmOoJLcrdC4LNHkYxg4HbNad74VS7utQm80K93LFJnbkrsxxn3xQBNLrulRXMDL5L+bL5TTrjCnHOTUepa7Y2mtWNj5Mc1xeIXMjADEY6knv9Kxp/h8buB4ZL3y4RdfaYY4lwsZHQD1z3BrX1TwyupvBJNOqyJbGBii45I6j29qALiapopjhnjntiGfyoiAMlvQcVBqGraPpl7aWTJA09xMAI0QZXPO8/lWfYeDTBfW17czxyywy7ykce1MBdowOx75pupeD5rzVZ7hLyNYJpxPIDFlwQuAA3pjtQBuxappN3H5y3NvIqgkMQDxnGefekTUdKu702Sz28two3mPAOAO9YN34Lle3s4LS8jhjitlt5R5eS4DBuD2PHWqHhbSNQtvEd202nmOBlkRJHXHkg9gf4s9aAOlur7Qb2CRJbm2YXGYC6sASf7oNUtOGiaGJRZOJ3lmWOaQsDggcc9OlUJfh/bv/Z+JImNvuEiunyyAnPAHQ+9Tp4JYXVy32tFgkuBOLdE+XIGDmgDpbSexvMzWrwuM7dyADJH86m+ywZXZDEpXlTsHy/SsXQPDY0mELcSiZo5HeEKNqxKf4QO9dADzyRQABWyDT6M0UAUNTuLK0tvMv3jWEsBmUAqD2qOC7025lltopIHMajzEABGPeqPirQp/EGni0iuhAhb5wVzuHpVPTPCkumX1xIt4PKltRAUCY3NjG4mgCPU7TwtrD289xeRhf+PdFjcKrYOdv5961BqWkxMqwCGRRKIXaMDCsB3P4VgS/D633ac6SRsLWPy3R1wrrnJbjo3vU8XgwmaeQXUQtmufOSCNPkHylf1zQBrtr2izXttZmeKWW6LNEdmQxX0NOk1nQwi3kl1bAbygkYDcGHXnqKx7LwddWSWpS/j8y3MgQeVkIj9l9MetVrL4f+Sxa6vFuGYyM3ycEsm3OPbrQB0FtreizXl/bwPH51mPNuCqdsZ3cdaLTxDol5a2c0N1Csd3xDuUAt+HbmquleGm0y4ugJ45Ibi3SM/J824AjOfTmsv/AIQWVoY42vIfLMfkyER87A24bT2b3oA6W+msrLYJbaJvOcA/KME+pp9pqWm3xlNrPDKYhiQrgkD/ACKy59DvtU0y3h1C4ijmhmYqYxnKYwoPvijTvC66Yt0La5Ebz2ywhlUcMufmx360AS3Wv6HZabJqYmtyqAqCijcxX+EVDP4p0uH7FwpuLkbxEANycZ59Kw4vh3cQW7bNRia4dmDmSLKkFSpIHr3qf/hBJM+UL5BbltxLR/vC23GA3pQBtR+JdKunmt4gJJo5ESeEqON/QnPBq7HrejQwztHd2yRW7bZApACtWG3g128xPtaCGRonbamGBRSOvvUMPgXDRLcXMTCHZGgWMDdGrbhv9SSMZoA7G1u4Ly3Se3lWSJxlXU5Bqeue0PSb3R7g2yyxNpxDOAB8wcsT+VdCOBQAUUUUAFFFFAHLfEj/AJJt4j/68Jf/AEGij4kf8k28R/8AXhL/AOg0UAJ8Nv8Akm3hz/rwi/lXVdq5X4bf8k28Of8AXhF/Kuq7UAQXMwgt5ZijOEUnavU/SodNvf7Qs47ryZIt/wDA4wR9aZrKltIuwAzN5TYVTgk49areF3kfw/ZmaIxOFwUY5I/GgDZPIrgvHIbRdY0TxXGpMdpL9mu8f88X4yfoea7w9DVLU9Ot9W0y5sLtN8FxGY3HsaALUciSKrI25WAII7imzOkUbySMqIoyzHsB3rjPA2o3NjNceEdWkLahpoHkyn/l4g/hYe46Gu0kVZImjddysCCD0IoAwoJfCcUk15bz6SjyHbJLG6ZOexOe9T3djob28Bu4bHyY2/ceaFCAn+79a4PU/hfdTeGY9NsmtUnW/kuScYDK3QH1xW1f+EdQMnh6aJrW6/s2LyZbafIjYcfOvuMUAdL5WhfvcCwJhJaTJX5CRgk+nFMFloLXMUnk2BmkQeURt3Oo9PWuCuPh3rl5qGr3t1LayG7h8tIkkKKxDblJx3Hf1qSP4d6vJrdhf394t0Ut0jl2yGPYygjgDqDmgDr7jQNN1jUrC7S6Elvpzfu7WJgYxIO5x3HpXRr05Oa5TwRoF/4fsr20vXh8qSdpIIo+TGp6gt3rq1IK8UAOooooAKq395DYWM93cNshgQyO3oAOaskiuB8X3L+J9ct/BlixMJIn1WVeiQg/6vP95qAJ/h1bzXNjf+I7tCtxrM5nUHqkQ4jX8q7dfu+9Mghjt4I4YlCxooVVHQAdKkoAKDRSGgDmPH2j3uu+EbvTrBUa4lK7d3YAg5964vW/h9q9oLEaTdXEsMk3nX5iZUcy8AOB0wOmK73xfrdx4e8OXOp21t9pkhxiMnAOTjrXMQ+Prm08UXNlqNo4tHaJI5F6QuybsN680AUbvwl4hey1GXfcT3Et4rPGbjaZrYD7i9lJNMm8K+J5fFUV5HNd21gINtvFHMCYePmR8nnPXI6VoTfEySWPbYaWzzx6glpNG78bWz84P4Vua94ztPD2r2FhcQyMLp1QyK2TGWJxke+KAOKfwJrp0uW3mSa53xwSvGbv786tl8Enjima7pnia21PVdZeOWw08WrIBFcby/Tbkdj2Jrfn+KVpb2aTyaXdbpJ3hgTPMmz7zD2q9rnxA0zSjp0MlrPcPfwiURBckJx1B+tAHnenaTrfiHw7CdOnvgY7tmvoVlAO8qNpiYnBA+tdl4s0DxBe/wBli1M93FDamMjzvLZLjI2yt6454qe78fw2aa1Y6fpTC90u2Ny8RARAO3T1pYviTbx3lpY3VjOty9uk1yEGRCXBP4jg0AN0LwpqmkweJZ22vqN+irHMH4kbZgt7c5rAsvCHjWFkS5nWSOCyms4iJfvBlyGPPXdW9H8ULY2lzcPpF6nloJI1IGJELbd3sM05viFb2V9eNfktYqyKrxrlYyybgC3fPQUAc5/wg/im58P3NtboNP8AOaFY7aWcyBWUHfIWB6N6Vu33hPUNQ+HVlpMkcjX9vIuC8x6Bsk5Ht2qZviZZzXb2ENncLceSWZ9uRE5UlQ35UaL8QVeDTrK/glk1O6EZQIuBIGxlh7CgChB4T1y0+JNvqNvEw0qMIAzTjaFC4Ix1yTRc6H4jSXxCxsZb28uXJt7hrnbH5J6IBnhhVrXPHk0PiOGxsIG+y29yYry4I4YhS20CrafEa2Nolxc6fcQpLby3MSvgFkQ4/rQBz1h4Y8XW6aCWE0l1biaKQy3GYo0YnaWwclhkdO1VYfB/iyPw/fRyy389+Z1ZUM67JGDE7jz93GM11lv8RbS81qOwisrgxuuXkx9z5dxLD0wMU+8+Ien211MkdtczWsVoLuW6UYVVPA4POc9qAOr09ZUs7ZbhVSZYlDqp4DY5x7VczXAWvxKtLuG0ddPuIvtE3lFn+VV4yDu6c0mnePAz2kE8TTS3U86RygeWi7GICk9z0oA7/I9aMivOofiVJb+GYNV1TTXjMt08ASNwTtVtu7FT3XxJjF1eW1pptw5hRxFMfuO4Xdt/KgDv6KxfCerz694ZstTubb7NLPHuMYOQK2qACiiigAooooAKKKKACiiigAppzzinUhNAHHaxrFzpfii5Mt3ss108yxw+XktIG7f1rItfGGu3mnx3ESQqYw7y7k4lVSoGPQnJrubq806O+itrl4BcyKfLWQDJHeqa63oQjmC3VoqW5AlAAwue344oA5e68Z6nHZT3MawiQyiMWzLg26/32PpXRWWtXFzocjs1r/asduZCiv8AJyMg/Sr0d3pdxdNBC1vLNJGHdVUEunYn1FVhqGgwG5uBNaIykRTHAHsFI/pQByX/AAnOotY28iGJZFjJkMkZxcuGwVTHbB7Vd8R3+orqNzbrdGGGbTC8duU5Mu7+92PtXTLfaVNI1vA9tJLbL5giUKSox2HaqVr4m03U7JLqIJJIHCNGQC0eTjnNAHMy+Ldfs47ey8qBpQ4RrtxtQ4HCn3q54h1vUNN1vTr5GkFnaWxkvbeNcht/A/I/zrX1LxPoVsoDyW9wBKFkAUN5ZP8AERWkdU0ktDG11bZuVzGCR865/WgDz+G6uZIba11/ULiD/iYStNKrGPaGQsijHYcVW1K91qZNPkSW6MSCTbPuZWEYIAkcfxH2r0C+1zQo9OfULqWCS0SURtIU3gMTj/61JZeIdMv7m6td8SSWxKlGAyy4znHpQBi654mvNLvrW3t7mBx9lWXbIp3XDZxhapR+MNemOoOtrbRiEhI4ZAd8Z3AAsOuMHNdHJd+HTe2uqSXFsZCvlQs2OOe3pUmo61pOl3UtvKI2vRbNc7NvzOi9ye9AHP33ijVrL+0D+4kntSI1tdmC2cHzc/3RmmJ4n15vJYi12pGHlKDd52WxxjpXRxa9oc1imotc23lyqFLvgnLDO0/nVpdR0mNpIkmtlEKBnVcYVex+lAGJ4Y1LVri4uIrrF0BdyRuwIU26jG0Y7114/pVOxuLO7iNxZPC6OeXjA5Pv71c9KAOa8Z6/LoOmCaB1SZmwm5CwY4zisaPxLrt3JFLFFbiKU+UsJU7lbbu3HFdfqF3Y2sIlv2hWHdgNIMjNZk2raVqNvq1nBPmSyQm48sYZRtzkH1xQBhXet6hr3gTUL+A/ZA7BISqknAwG/DOaq2muyeGbeW3HywtLLsMuT84C4A+uTXVaXqGjR2FpZQSwxbrcSrA5G7aRk8VMdX0a4SJhPbSCV2EeVByw6/jQBybeNdWEkjLBFu2Y+ylTui4yHPtU8/inV7W7jglWGaVBHugRDuuA45KfSujstb0e+sZ9Tgmh8mNmilnZQCCpwQfUU6TWtFX7NO93a7peInYjJz6HqKAORtfHV8WMkr27RgxtKqqR9nViRhvpgfnTm8T6lPItxZxIJpgI1ZslMeYVBA+neuot4tCl1u7aFrdr4xhbiIY5UdCw71KNY0WO38/7TbLAj+WGwAA3oKAOTbxlqySxxskKPGwQqVOblicHb6YqrY+JNT0+2jgTgqTIEmBLXRL4Kqe2P6V28up6QkxWSe1EtunmZwPkU9/amPrGiCO1uHu7UpKSbdmxye5B7UAcrN4v12CC4uGtYpFcSGGJVOYgr7ct+FXbnxNfWngz+07h4lu2fbG0Q3q/cZ9K2pNc01dch0hSkk8wYMVwQgA3Yb1zUMeu6e11LYNaIkMMpQsVUIMDOcUAYCeL9akaK4+yx/ZlCLJAEO+RmBPB9sVN4Q1W61bxNeTzXKzKbRDsQECJic7T7iukGt6ILT7SLy1WAPt3gjAatG3jhVd8Mca7xklAAG9+KAJgAOg6806iigAooooAKKKKAOW+JH/JNvEf/XhL/wCg0UfEj/km3iP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)\n",
        "###*Img 1. The image above respresents epidemiology of drug resistant strains in the world. (Yale Alumni Magazine, 2010)*\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "###Drug discovery is a long and arduous process that takes decades to bring a new lead compound from bench to bedside. New effective drugs become increasingly important with rising antibiotic resistant strains. The goal of this project is to provide a bioinformatic tool to aid in discovering a potent drug molecule against a protein of interest using the ChEMBL database. The project will report the Lipinski descriptors of all target compounds and using these descriptors identify and classify the active from molecules with high statistical significance.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "### The mycolyl-arabinogalactan-peptidoglycan (mAGP) complex represents the cell wall core structure that is a unique distinguishing feature of the Mycobacterial bacilli. Our protein of interest, the 3-oxoacyl-[acyl-carrier-protein] synthase III catalyzes the condensation reaction of fatty acid synthesis by the addition to an acyl acceptor of two carbons from malonyl-ACP. It catalyzes the first condensation reaction which initiates fatty acid synthesis and may therefore play a role in governing the total rate of fatty acid production. Its substrate specificity is critical for the biosynthesis of mycolic acid fatty acid chain and it presents itself as an attractive protein to target for identifying new drug molecules. (National Center for Biotechnology Information (2022) https://pubchem.ncbi.nlm.nih.gov/protein/P9WNG3., Scarsdale et. al.)\n",
        "\n"
      ],
      "metadata": {
        "id": "h8DCR7sora3L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Methodology\n",
        "\n",
        "###The ChEMBL database is our main source of list of potential drug compounds against the target molecule. This database has been accessed via the official ChEMBL websource client. Query searches can be screened specifically for accessing all target proteins listed for *Mycobacterium tuberculosis*. We then identify ChEMBL ID of our protein of interest and retrieve bioactivity data filtering searches to keep only those molecules that have a reported IC50. IC50 is the half maximally inhibitory concentration of the drug that is a standard in determining potency of drug moleclues. \n",
        "\n",
        "###The drug molecules are classified into active, inactive and intermediate based on their IC50. These values can be adjusted based on the determined IC50 threshold for active compounds. In our case study, compounds that have IC50 less than 1000nM is considered to have a bioactive IC50. Then a new dataframe was made with the canonical smiles, bioactivity and IC50 values giving us a list of potential drug targets. We then list the Lipinski discriptors for each molecule which consist of the molecular weight, IC50, LogP, Number of H donors and number of H acceptors. IC50 values go upto 10^-9 M and have large variation. To normalize values we convert IC50 to pIC50 and eleminate compounds with intermediate IC50 from our dataframe to simplify further analysis.\n",
        "\n",
        "###Exploratory data analysis in the chemical space is performed in the next part. The discriptors are plotted to analyse features of each discriptor corresponding to the active and inactive compounds. We then perform the Mann-Whitney U test to determine statistic significance of bioactivity in active compounds. The test is repeated for each individual discriptor to see if individual discriptors also significantly contribute to activity. This excerxise finally gives us a list of compounds that should show activity against 3-oxoacyl-[acyl-carrier-protein] synthase III based on its lipinski discriptors"
      ],
      "metadata": {
        "id": "ybacx4WNoaHv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Part 1) Compiling dataset of target compounds"
      ],
      "metadata": {
        "id": "I5lN1hb6-pxY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Libraries"
      ],
      "metadata": {
        "id": "-VMs9j3Rr1m5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The ChEMBL websource client is developed by the ChEMBL group and allows users to directly access all of ChEMBL data and cheminformatic using python.(Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP. (2015) 'ChEMBL web services: streamlining access to drug discovery data and utilities.' Nucleic Acids Res., 43(W1) W612-W620.)"
      ],
      "metadata": {
        "id": "tWBDI65gtUs8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "9x7bpPUcY96f",
        "outputId": "8d59147e-df9f-4684-edd1-0768097de330"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting chembl_webresource_client\n",
            "  Using cached chembl_webresource_client-0.10.8-py3-none-any.whl (55 kB)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/site-packages (from chembl_webresource_client) (1.25.8)\n",
            "Collecting requests-cache~=0.7.0\n",
            "  Using cached requests_cache-0.7.5-py3-none-any.whl (39 kB)\n",
            "Collecting easydict\n",
            "  Downloading easydict-1.9.tar.gz (6.4 kB)\n",
            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.7/site-packages (from chembl_webresource_client) (2.22.0)\n",
            "Collecting url-normalize<2.0,>=1.4\n",
            "  Using cached url_normalize-1.4.3-py2.py3-none-any.whl (6.8 kB)\n",
            "Collecting itsdangerous>=2.0.1\n",
            "  Using cached itsdangerous-2.1.2-py3-none-any.whl (15 kB)\n",
            "Collecting attrs<22.0,>=21.2\n",
            "  Using cached attrs-21.4.0-py2.py3-none-any.whl (60 kB)\n",
            "Collecting pyyaml>=5.4\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 33.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests>=2.18.4->chembl_webresource_client) (2022.9.14)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests>=2.18.4->chembl_webresource_client) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests>=2.18.4->chembl_webresource_client) (3.0.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from url-normalize<2.0,>=1.4->requests-cache~=0.7.0->chembl_webresource_client) (1.14.0)\n",
            "Building wheels for collected packages: easydict\n",
            "  Building wheel for easydict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for easydict: filename=easydict-1.9-py3-none-any.whl size=6349 sha256=36edd51350d37fe04af048370fd64581c066a5b9e21abf58037714f4800edd89\n",
            "  Stored in directory: /root/.cache/pip/wheels/88/96/68/c2be18e7406804be2e593e1c37845f2dd20ac2ce1381ce40b0\n",
            "Successfully built easydict\n",
            "Installing collected packages: url-normalize, itsdangerous, attrs, pyyaml, requests-cache, easydict, chembl-webresource-client\n",
            "Successfully installed attrs-21.4.0 chembl-webresource-client-0.10.8 easydict-1.9 itsdangerous-2.1.2 pyyaml-6.0 requests-cache-0.7.5 url-normalize-1.4.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "attr",
                  "chembl_webresource_client",
                  "easydict"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install chembl_webresource_client "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Import Libraries"
      ],
      "metadata": {
        "id": "VJUwJXanuwzW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from chembl_webresource_client.new_client import new_client #Import necessary libraries"
      ],
      "metadata": {
        "id": "blJx1OisZAF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Search for target protein"
      ],
      "metadata": {
        "id": "ZduXOHVbvCw3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The target.search code performs a web search on the ChEMBL database based on query *Mycobacterium tuberculosis* under targets section"
      ],
      "metadata": {
        "id": "hwdBP_SNvGbg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = new_client.target # Assign new client to target variable\n",
        "targets = target.search('Mycobacterium tuberculosis')\n",
        "targets = pd.DataFrame.from_dict(targets)\n",
        "targets     # Display targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "yl8XCpWPZDN8",
        "outputId": "51030882-f2a5-4372-ccb8-3f6a963b6ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    cross_references                          organism  \\\n",
              "0                 []        Mycobacterium tuberculosis   \n",
              "1                 []  Mycobacterium tuberculosis H37Rv   \n",
              "2                 []  Mycobacterium tuberculosis H37Ra   \n",
              "3                 []                     Mycobacterium   \n",
              "4                 []        Mycobacterium tuberculosis   \n",
              "..               ...                               ...   \n",
              "117               []        Mycobacterium tuberculosis   \n",
              "118               []        Mycobacterium tuberculosis   \n",
              "119               []        Mycobacterium tuberculosis   \n",
              "120               []        Mycobacterium tuberculosis   \n",
              "121               []        Mycobacterium tuberculosis   \n",
              "\n",
              "                                             pref_name  score  \\\n",
              "0                           Mycobacterium tuberculosis   27.0   \n",
              "1                     Mycobacterium tuberculosis H37Rv   24.0   \n",
              "2                     Mycobacterium tuberculosis H37Ra   24.0   \n",
              "3                                        Mycobacterium   13.0   \n",
              "4            PYRAZINAMIDASE/NICOTINAMIDAS PNCA (PZase)   13.0   \n",
              "..                                                 ...    ...   \n",
              "117                                       70S ribosome    7.0   \n",
              "118                                       ATP synthase    7.0   \n",
              "119                              Thioredoxin reductase    7.0   \n",
              "120  ATP-dependent Clp protease ATP-binding subunit...    7.0   \n",
              "121   1,4-dihydroxy-2-naphthoate octaprenyltransferase    7.0   \n",
              "\n",
              "     species_group_flag target_chembl_id  \\\n",
              "0                 False        CHEMBL360   \n",
              "1                 False    CHEMBL2111188   \n",
              "2                 False    CHEMBL2366634   \n",
              "3                  True     CHEMBL614981   \n",
              "4                 False    CHEMBL1697663   \n",
              "..                  ...              ...   \n",
              "117               False    CHEMBL2363965   \n",
              "118               False    CHEMBL2364166   \n",
              "119               False    CHEMBL2390811   \n",
              "120               False    CHEMBL4630874   \n",
              "121               False    CHEMBL4879548   \n",
              "\n",
              "                                     target_components  \\\n",
              "0                                                   []   \n",
              "1                                                   []   \n",
              "2                                                   []   \n",
              "3                                                   []   \n",
              "4    [{'accession': 'Q50575', 'component_descriptio...   \n",
              "..                                                 ...   \n",
              "117  [{'accession': 'P9WHE1', 'component_descriptio...   \n",
              "118  [{'accession': 'P9WPU9', 'component_descriptio...   \n",
              "119  [{'accession': 'P9WHH1', 'component_descriptio...   \n",
              "120  [{'accession': 'P9WPC9', 'component_descriptio...   \n",
              "121  [{'accession': 'P9WIP3', 'component_descriptio...   \n",
              "\n",
              "                      target_type  tax_id  \n",
              "0                        ORGANISM    1773  \n",
              "1                        ORGANISM   83332  \n",
              "2                        ORGANISM  419947  \n",
              "3                        ORGANISM    1763  \n",
              "4                  SINGLE PROTEIN    1773  \n",
              "..                            ...     ...  \n",
              "117  PROTEIN NUCLEIC-ACID COMPLEX    1773  \n",
              "118               PROTEIN COMPLEX    1773  \n",
              "119                SINGLE PROTEIN    1773  \n",
              "120                SINGLE PROTEIN    1773  \n",
              "121                SINGLE PROTEIN    1773  \n",
              "\n",
              "[122 rows x 9 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cross_references</th>\n",
              "      <th>organism</th>\n",
              "      <th>pref_name</th>\n",
              "      <th>score</th>\n",
              "      <th>species_group_flag</th>\n",
              "      <th>target_chembl_id</th>\n",
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              "      <th>target_type</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>27.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL360</td>\n",
              "      <td>[]</td>\n",
              "      <td>ORGANISM</td>\n",
              "      <td>1773</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis H37Rv</td>\n",
              "      <td>Mycobacterium tuberculosis H37Rv</td>\n",
              "      <td>24.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL2111188</td>\n",
              "      <td>[]</td>\n",
              "      <td>ORGANISM</td>\n",
              "      <td>83332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis H37Ra</td>\n",
              "      <td>Mycobacterium tuberculosis H37Ra</td>\n",
              "      <td>24.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL2366634</td>\n",
              "      <td>[]</td>\n",
              "      <td>ORGANISM</td>\n",
              "      <td>419947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium</td>\n",
              "      <td>Mycobacterium</td>\n",
              "      <td>13.0</td>\n",
              "      <td>True</td>\n",
              "      <td>CHEMBL614981</td>\n",
              "      <td>[]</td>\n",
              "      <td>ORGANISM</td>\n",
              "      <td>1763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>PYRAZINAMIDASE/NICOTINAMIDAS PNCA (PZase)</td>\n",
              "      <td>13.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL1697663</td>\n",
              "      <td>[{'accession': 'Q50575', 'component_descriptio...</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>70S ribosome</td>\n",
              "      <td>7.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL2363965</td>\n",
              "      <td>[{'accession': 'P9WHE1', 'component_descriptio...</td>\n",
              "      <td>PROTEIN NUCLEIC-ACID COMPLEX</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>ATP synthase</td>\n",
              "      <td>7.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL2364166</td>\n",
              "      <td>[{'accession': 'P9WPU9', 'component_descriptio...</td>\n",
              "      <td>PROTEIN COMPLEX</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>Thioredoxin reductase</td>\n",
              "      <td>7.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL2390811</td>\n",
              "      <td>[{'accession': 'P9WHH1', 'component_descriptio...</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>ATP-dependent Clp protease ATP-binding subunit...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL4630874</td>\n",
              "      <td>[{'accession': 'P9WPC9', 'component_descriptio...</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>[]</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>1,4-dihydroxy-2-naphthoate octaprenyltransferase</td>\n",
              "      <td>7.0</td>\n",
              "      <td>False</td>\n",
              "      <td>CHEMBL4879548</td>\n",
              "      <td>[{'accession': 'P9WIP3', 'component_descriptio...</td>\n",
              "      <td>SINGLE PROTEIN</td>\n",
              "      <td>1773</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>122 rows × 9 columns</p>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Select and retrieve bioactivity data for *Mycobacterium tuberculosis* 3-oxoacyl-[acyl-carrier-protein] synthase III"
      ],
      "metadata": {
        "id": "85WX19VvvjXg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_data = targets[targets[\"pref_name\"] == \"3-oxoacyl-[acyl-carrier-protein] synthase III\"] # To identidy serial number of target protein\n",
        "print(filtered_data)\n",
        "selected_target = targets.target_chembl_id[53] # Selected target variable stores ChEMBL ID of protein of interest\n",
        "selected_target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "IHaI-D_hZIwo",
        "outputId": "5f57dac4-254a-4b78-c2f2-9bac1efd257c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                     cross_references  \\\n",
            "53  [{'xref_id': 'P0A574', 'xref_name': None, 'xre...   \n",
            "\n",
            "                      organism                                      pref_name  \\\n",
            "53  Mycobacterium tuberculosis  3-oxoacyl-[acyl-carrier-protein] synthase III   \n",
            "\n",
            "    score  species_group_flag target_chembl_id  \\\n",
            "53    7.0               False       CHEMBL3640   \n",
            "\n",
            "                                    target_components     target_type  tax_id  \n",
            "53  [{'accession': 'P9WNG3', 'component_descriptio...  SINGLE PROTEIN    1773  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'CHEMBL3640'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve only bioactivity data 3-oxoacyl-[acyl-carrier-protein] synthase III that are reported as IC50 values in nM (nanomolar) unit.\n",
        "activity = new_client.activity\n",
        "res = activity.filter(target_chembl_id=selected_target).filter(standard_type=\"IC50\") # Retrieve the bioactivity for our target variable \n",
        "                                                                                        #and filter for standard type to be only IC50 and no other classification\n",
        "df = pd.DataFrame.from_dict(res)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "id": "P6idw_ZWZLaa",
        "outputId": "b0dd18e0-47ef-4ef5-ff5c-38cc8df1559f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    activity_comment  activity_id activity_properties assay_chembl_id  \\\n",
              "0               None       672002                  []    CHEMBL648364   \n",
              "1               None       674666                  []    CHEMBL648364   \n",
              "2               None       674667                  []    CHEMBL648364   \n",
              "3               None       677317                  []    CHEMBL648364   \n",
              "4               None       678602                  []    CHEMBL648364   \n",
              "..               ...          ...                 ...             ...   \n",
              "129             None      2587263                  []   CHEMBL1010089   \n",
              "130             None      2587264                  []   CHEMBL1010089   \n",
              "131             None      2587265                  []   CHEMBL1010089   \n",
              "132             None      2587266                  []   CHEMBL1010089   \n",
              "133             None      2587267                  []   CHEMBL1010089   \n",
              "\n",
              "                                     assay_description assay_type  \\\n",
              "0    In vitro inhibitory activity against the recom...          B   \n",
              "1    In vitro inhibitory activity against the recom...          B   \n",
              "2    In vitro inhibitory activity against the recom...          B   \n",
              "3    In vitro inhibitory activity against the recom...          B   \n",
              "4    In vitro inhibitory activity against the recom...          B   \n",
              "..                                                 ...        ...   \n",
              "129      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "130      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "131      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "132      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "133      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "\n",
              "    assay_variant_accession assay_variant_mutation bao_endpoint   bao_format  \\\n",
              "0                      None                   None  BAO_0000190  BAO_0000357   \n",
              "1                      None                   None  BAO_0000190  BAO_0000357   \n",
              "2                      None                   None  BAO_0000190  BAO_0000357   \n",
              "3                      None                   None  BAO_0000190  BAO_0000357   \n",
              "4                      None                   None  BAO_0000190  BAO_0000357   \n",
              "..                      ...                    ...          ...          ...   \n",
              "129                    None                   None  BAO_0000190  BAO_0000357   \n",
              "130                    None                   None  BAO_0000190  BAO_0000357   \n",
              "131                    None                   None  BAO_0000190  BAO_0000357   \n",
              "132                    None                   None  BAO_0000190  BAO_0000357   \n",
              "133                    None                   None  BAO_0000190  BAO_0000357   \n",
              "\n",
              "     ...             target_organism  \\\n",
              "0    ...  Mycobacterium tuberculosis   \n",
              "1    ...  Mycobacterium tuberculosis   \n",
              "2    ...  Mycobacterium tuberculosis   \n",
              "3    ...  Mycobacterium tuberculosis   \n",
              "4    ...  Mycobacterium tuberculosis   \n",
              "..   ...                         ...   \n",
              "129  ...  Mycobacterium tuberculosis   \n",
              "130  ...  Mycobacterium tuberculosis   \n",
              "131  ...  Mycobacterium tuberculosis   \n",
              "132  ...  Mycobacterium tuberculosis   \n",
              "133  ...  Mycobacterium tuberculosis   \n",
              "\n",
              "                                  target_pref_name target_tax_id text_value  \\\n",
              "0    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "1    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "2    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "3    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "4    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "..                                             ...           ...        ...   \n",
              "129  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "130  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "131  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "132  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "133  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "\n",
              "     toid  type  units    uo_units upper_value  value  \n",
              "0    None  IC50     uM  UO_0000065        None  500.0  \n",
              "1    None  IC50     uM  UO_0000065        None  263.0  \n",
              "2    None  IC50     uM  UO_0000065        None  119.0  \n",
              "3    None  IC50     uM  UO_0000065        None   52.0  \n",
              "4    None  IC50     uM  UO_0000065        None  112.0  \n",
              "..    ...   ...    ...         ...         ...    ...  \n",
              "129  None  IC50     nM  UO_0000065        None   34.4  \n",
              "130  None  IC50     nM  UO_0000065        None   31.4  \n",
              "131  None  IC50     nM  UO_0000065        None  138.1  \n",
              "132  None  IC50     nM  UO_0000065        None   1.99  \n",
              "133  None  IC50     nM  UO_0000065        None  112.0  \n",
              "\n",
              "[134 rows x 45 columns]"
            ],
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              "      <th></th>\n",
              "      <th>activity_comment</th>\n",
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              "      <th>assay_chembl_id</th>\n",
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              "      <th>assay_variant_mutation</th>\n",
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              "      <th>bao_format</th>\n",
              "      <th>...</th>\n",
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              "      <th>target_tax_id</th>\n",
              "      <th>text_value</th>\n",
              "      <th>toid</th>\n",
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              "      <th>upper_value</th>\n",
              "      <th>value</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>672002</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
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              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>500.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>674666</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>263.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>674667</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>119.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>677317</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>678602</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>129</th>\n",
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              "      <td>2587263</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>34.4</td>\n",
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              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>None</td>\n",
              "      <td>2587264</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>31.4</td>\n",
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              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>None</td>\n",
              "      <td>2587265</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>138.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>None</td>\n",
              "      <td>2587266</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>None</td>\n",
              "      <td>2587267</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>112.0</td>\n",
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              "  </tbody>\n",
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              "<p>134 rows × 45 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Handel missing data"
      ],
      "metadata": {
        "id": "pi_KORStwlpV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df[df.standard_value.notna()] # Drop compounds that do not have IC50 reported\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "id": "wnNSyZXSZR0m",
        "outputId": "d0b44d01-78d0-4aca-c6c7-2713ef908b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    activity_comment  activity_id activity_properties assay_chembl_id  \\\n",
              "0               None       672002                  []    CHEMBL648364   \n",
              "1               None       674666                  []    CHEMBL648364   \n",
              "2               None       674667                  []    CHEMBL648364   \n",
              "3               None       677317                  []    CHEMBL648364   \n",
              "4               None       678602                  []    CHEMBL648364   \n",
              "..               ...          ...                 ...             ...   \n",
              "129             None      2587263                  []   CHEMBL1010089   \n",
              "130             None      2587264                  []   CHEMBL1010089   \n",
              "131             None      2587265                  []   CHEMBL1010089   \n",
              "132             None      2587266                  []   CHEMBL1010089   \n",
              "133             None      2587267                  []   CHEMBL1010089   \n",
              "\n",
              "                                     assay_description assay_type  \\\n",
              "0    In vitro inhibitory activity against the recom...          B   \n",
              "1    In vitro inhibitory activity against the recom...          B   \n",
              "2    In vitro inhibitory activity against the recom...          B   \n",
              "3    In vitro inhibitory activity against the recom...          B   \n",
              "4    In vitro inhibitory activity against the recom...          B   \n",
              "..                                                 ...        ...   \n",
              "129      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "130      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "131      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "132      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "133      Inhibition of Mycobacterium tuberculosis FabH          B   \n",
              "\n",
              "    assay_variant_accession assay_variant_mutation bao_endpoint   bao_format  \\\n",
              "0                      None                   None  BAO_0000190  BAO_0000357   \n",
              "1                      None                   None  BAO_0000190  BAO_0000357   \n",
              "2                      None                   None  BAO_0000190  BAO_0000357   \n",
              "3                      None                   None  BAO_0000190  BAO_0000357   \n",
              "4                      None                   None  BAO_0000190  BAO_0000357   \n",
              "..                      ...                    ...          ...          ...   \n",
              "129                    None                   None  BAO_0000190  BAO_0000357   \n",
              "130                    None                   None  BAO_0000190  BAO_0000357   \n",
              "131                    None                   None  BAO_0000190  BAO_0000357   \n",
              "132                    None                   None  BAO_0000190  BAO_0000357   \n",
              "133                    None                   None  BAO_0000190  BAO_0000357   \n",
              "\n",
              "     ...             target_organism  \\\n",
              "0    ...  Mycobacterium tuberculosis   \n",
              "1    ...  Mycobacterium tuberculosis   \n",
              "2    ...  Mycobacterium tuberculosis   \n",
              "3    ...  Mycobacterium tuberculosis   \n",
              "4    ...  Mycobacterium tuberculosis   \n",
              "..   ...                         ...   \n",
              "129  ...  Mycobacterium tuberculosis   \n",
              "130  ...  Mycobacterium tuberculosis   \n",
              "131  ...  Mycobacterium tuberculosis   \n",
              "132  ...  Mycobacterium tuberculosis   \n",
              "133  ...  Mycobacterium tuberculosis   \n",
              "\n",
              "                                  target_pref_name target_tax_id text_value  \\\n",
              "0    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "1    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "2    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "3    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "4    3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "..                                             ...           ...        ...   \n",
              "129  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "130  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "131  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "132  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "133  3-oxoacyl-[acyl-carrier-protein] synthase III          1773       None   \n",
              "\n",
              "     toid  type  units    uo_units upper_value  value  \n",
              "0    None  IC50     uM  UO_0000065        None  500.0  \n",
              "1    None  IC50     uM  UO_0000065        None  263.0  \n",
              "2    None  IC50     uM  UO_0000065        None  119.0  \n",
              "3    None  IC50     uM  UO_0000065        None   52.0  \n",
              "4    None  IC50     uM  UO_0000065        None  112.0  \n",
              "..    ...   ...    ...         ...         ...    ...  \n",
              "129  None  IC50     nM  UO_0000065        None   34.4  \n",
              "130  None  IC50     nM  UO_0000065        None   31.4  \n",
              "131  None  IC50     nM  UO_0000065        None  138.1  \n",
              "132  None  IC50     nM  UO_0000065        None   1.99  \n",
              "133  None  IC50     nM  UO_0000065        None  112.0  \n",
              "\n",
              "[133 rows x 45 columns]"
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              "      <th>0</th>\n",
              "      <td>None</td>\n",
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              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
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              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
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              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>500.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>674666</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
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              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>263.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>674667</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>119.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>677317</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
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              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>52.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>678602</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL648364</td>\n",
              "      <td>In vitro inhibitory activity against the recom...</td>\n",
              "      <td>B</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
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              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>112.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
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              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
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              "      <td>34.4</td>\n",
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              "      <th>130</th>\n",
              "      <td>None</td>\n",
              "      <td>2587264</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
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              "      <td>None</td>\n",
              "      <td>2587265</td>\n",
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              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
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              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>138.1</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>None</td>\n",
              "      <td>2587266</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.99</td>\n",
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              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>None</td>\n",
              "      <td>2587267</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL1010089</td>\n",
              "      <td>Inhibition of Mycobacterium tuberculosis FabH</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>...</td>\n",
              "      <td>Mycobacterium tuberculosis</td>\n",
              "      <td>3-oxoacyl-[acyl-carrier-protein] synthase III</td>\n",
              "      <td>1773</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>112.0</td>\n",
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              "<p>133 rows × 45 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data pre-processing"
      ],
      "metadata": {
        "id": "HagrTW2sw6a_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Labeling compounds as active or inactive"
      ],
      "metadata": {
        "id": "QL4UflwzxE6k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compounds having values of less than 1000 nM will be considered to be active while those greater than 10,000 nM will be considered to be inactive. As for those values in between 1,000 and 10,000 nM will be referred to as intermediate. (Lower IC50 indicates higher potency and lesser side effects. Values chosen here are not absolute and can be adjusted depending on decided threshold IC50 value)."
      ],
      "metadata": {
        "id": "THn6MaF6xL-V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivity_class = []\n",
        "counter = 0\n",
        "for i in df2.standard_value:\n",
        "  if float(i) >= 10000:\n",
        "    bioactivity_class.append(\"inactive\")\n",
        "  elif float(i) < 1000:\n",
        "    bioactivity_class.append(\"active\")\n",
        "    counter += 1\n",
        "  else:\n",
        "    bioactivity_class.append(\"intermediate\")\n",
        "print(counter) # Check how many active compounds we have out of 133"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZLrPlFvZW1b",
        "outputId": "410ff28c-c9df-4348-b3e6-e7de4e8cedc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combine the 3 columns (molecule_chembl_id,canonical_smiles,standard_value) and bioactivity_class into a DataFrame"
      ],
      "metadata": {
        "id": "Yn5xcEWCDTYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selection = ['molecule_chembl_id','canonical_smiles','standard_value']\n",
        "df3 = df2[selection]\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "sBKjjeWhZYcA",
        "outputId": "9dd112fd-208c-49ca-b129-61c74c320fa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                  canonical_smiles  \\\n",
              "0         CHEMBL123184                  COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1   \n",
              "1         CHEMBL122699                CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O   \n",
              "2         CHEMBL124117  CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1   \n",
              "3         CHEMBL332364           CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O   \n",
              "4         CHEMBL432983                   CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O   \n",
              "..                 ...                                               ...   \n",
              "129       CHEMBL462375                  Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462376            CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "131       CHEMBL462553                Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "132       CHEMBL454282         Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "133       CHEMBL501551                 Cc1ccc(SC2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "    standard_value  \n",
              "0         500000.0  \n",
              "1         263000.0  \n",
              "2         119000.0  \n",
              "3          52000.0  \n",
              "4         112000.0  \n",
              "..             ...  \n",
              "129           34.4  \n",
              "130           31.4  \n",
              "131          138.1  \n",
              "132           1.99  \n",
              "133          112.0  \n",
              "\n",
              "[133 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>standard_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL123184</td>\n",
              "      <td>COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1</td>\n",
              "      <td>500000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL122699</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O</td>\n",
              "      <td>263000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL124117</td>\n",
              "      <td>CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1</td>\n",
              "      <td>119000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL332364</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O</td>\n",
              "      <td>52000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL432983</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O</td>\n",
              "      <td>112000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>34.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462376</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>31.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>CHEMBL462553</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1</td>\n",
              "      <td>138.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>CHEMBL454282</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>CHEMBL501551</td>\n",
              "      <td>Cc1ccc(SC2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>112.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>133 rows × 3 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bioactivity_class = pd.Series(bioactivity_class, name='bioactivity_class')\n",
        "df4 = pd.concat([df3, bioactivity_class], axis = 1)\n",
        "df4 = df4.dropna()\n",
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "2OFIsT2Ba2jr",
        "outputId": "90ecf501-ea88-4bf2-d013-2315ab74c8e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                  canonical_smiles  \\\n",
              "0         CHEMBL123184                  COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1   \n",
              "1         CHEMBL122699                CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O   \n",
              "2         CHEMBL124117  CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1   \n",
              "3         CHEMBL332364           CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O   \n",
              "4         CHEMBL432983                   CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O   \n",
              "..                 ...                                               ...   \n",
              "128       CHEMBL451059      Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1   \n",
              "129       CHEMBL462375                  Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462376            CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "131       CHEMBL462553                Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "132       CHEMBL454282         Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "    standard_value bioactivity_class  \n",
              "0         500000.0          inactive  \n",
              "1         263000.0          inactive  \n",
              "2         119000.0          inactive  \n",
              "3          52000.0          inactive  \n",
              "4         112000.0          inactive  \n",
              "..             ...               ...  \n",
              "128           8.47            active  \n",
              "129           34.4            active  \n",
              "130           31.4            active  \n",
              "131          138.1            active  \n",
              "132           1.99            active  \n",
              "\n",
              "[132 rows x 4 columns]"
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>bioactivity_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL123184</td>\n",
              "      <td>COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1</td>\n",
              "      <td>500000.0</td>\n",
              "      <td>inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL122699</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O</td>\n",
              "      <td>263000.0</td>\n",
              "      <td>inactive</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL124117</td>\n",
              "      <td>CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1</td>\n",
              "      <td>119000.0</td>\n",
              "      <td>inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL332364</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>inactive</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL432983</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O</td>\n",
              "      <td>112000.0</td>\n",
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              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>CHEMBL451059</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1</td>\n",
              "      <td>8.47</td>\n",
              "      <td>active</td>\n",
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              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>34.4</td>\n",
              "      <td>active</td>\n",
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              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462376</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>31.4</td>\n",
              "      <td>active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>CHEMBL462553</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1</td>\n",
              "      <td>138.1</td>\n",
              "      <td>active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>CHEMBL454282</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>1.99</td>\n",
              "      <td>active</td>\n",
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              "  </tbody>\n",
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              "<p>132 rows × 4 columns</p>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-ba9e7d2c-0fbb-42ce-bd47-e66f62778c45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ba9e7d2c-0fbb-42ce-bd47-e66f62778c45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Store data in a CSV file"
      ],
      "metadata": {
        "id": "wD0EP6ZGDu_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.to_csv(r'bioactivity_data_preprocessed.csv', index=False)"
      ],
      "metadata": {
        "id": "NBeHaSh4a4Oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Part 2) Identifying Discriptors"
      ],
      "metadata": {
        "id": "hy1biY5KD6Zv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install conda and rdkit"
      ],
      "metadata": {
        "id": "ghq7oP7zEGT6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conda is a package managment software that opens, runs and installs packages easily and quickly. RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python."
      ],
      "metadata": {
        "id": "3tQqt9kREKfF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "! conda install -c rdkit rdkit -y\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OD2J00u4a9se",
        "outputId": "888e9237-2f37-4e0e-de3a-55e71e9eacef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-22 02:22:04--  https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85055499 (81M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh.2’\n",
            "\n",
            "Miniconda3-py37_4.8 100%[===================>]  81.12M   130MB/s    in 0.6s    \n",
            "\n",
            "2022-09-22 02:22:05 (130 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh.2’ saved [85055499/85055499]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): \\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \n",
            "Found conflicts! Looking for incompatible packages.\n",
            "This can take several minutes.  Press CTRL-C to abort.\n",
            "\b\b| \b\bfailed\n",
            "\n",
            "UnsatisfiableError: The following specifications were found to be incompatible with each other:\n",
            "\n",
            "Output in format: Requested package -> Available versions\n",
            "\n",
            "Package tk conflicts for:\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "rdkit -> pillow -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "packaging -> python[version='>=3.6'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "python==3.7.6=h0371630_2 -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "python-dateutil -> python -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "toolz -> python[version='>=3.5'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pillow -> tk\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "tk==8.6.8=hbc83047_0\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> tk[version='>=8.6.8,<8.7.0a0']\n",
            "\n",
            "Package _openmp_mutex conflicts for:\n",
            "mkl-service -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "bottleneck -> libgcc-ng[version='>=11.2.0'] -> _openmp_mutex\n",
            "libwebp -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "giflib -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libedit==3.1.20181209=hc058e9b_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "numpy -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "tk==8.6.8=hbc83047_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libpng -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "rdkit -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libwebp-base -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "numexpr -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "libffi==3.2.1=hd88cf55_4 -> libgcc-ng[version='>=7.2.0'] -> _openmp_mutex\n",
            "numpy-base -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "cryptography==2.8=py37h1ba5d50_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "cairo -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "zstd -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libxml2 -> libgcc-ng[version='>=9.3.0'] -> _openmp_mutex\n",
            "pandas -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "python==3.7.6=h0371630_2 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "bzip2 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "freetype -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "pixman -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "readline==7.0=h7b6447c_5 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "py-boost -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "mkl_fft -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "zlib==1.2.11=h7b6447c_3 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "yaml==0.1.7=had09818_2 -> libgcc-ng[version='>=7.2.0'] -> _openmp_mutex\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "jpeg -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "libtiff -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "sqlite==3.31.1=h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "openssl==1.1.1d=h7b6447c_4 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libxcb -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "cytoolz -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "libuuid -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "lz4-c -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "pillow -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "fontconfig -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "glib -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "pcre -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "libboost -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "icu -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "_openmp_mutex\n",
            "lcms2 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "ncurses==6.2=he6710b0_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libgcc-ng[version='>=7.3.0'] -> _openmp_mutex\n",
            "xz==5.2.4=h14c3975_4 -> libgcc-ng[version='>=7.2.0'] -> _openmp_mutex\n",
            "mkl_random -> libgcc-ng[version='>=7.5.0'] -> _openmp_mutex\n",
            "\n",
            "Package zstd conflicts for:\n",
            "zstd\n",
            "libwebp -> libtiff[version='>=4.1.0,<5.0a0'] -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "rdkit -> libboost[version='>=1.73.0,<1.74.0a0'] -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "libboost -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "libtiff -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11] -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "pillow -> libtiff[version='>=4.1.0,<5.0a0'] -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0'] -> zstd[version='>=1.4.5,<1.5.0a0']\n",
            "\n",
            "Package libuuid conflicts for:\n",
            "cairo -> fontconfig[version='>=2.13.1,<3.0a0'] -> libuuid[version='>=1.0.3,<2.0a0']\n",
            "libuuid\n",
            "fontconfig -> libuuid[version='>=1.0.3,<2.0a0']\n",
            "\n",
            "Package conda-package-handling conflicts for:\n",
            "conda==4.8.2=py37_0 -> conda-package-handling[version='>=1.3.0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "\n",
            "Package libstdcxx-ng conflicts for:\n",
            "python-dateutil -> python -> libstdcxx-ng[version='>=7.3.0']\n",
            "readline==7.0=h7b6447c_5 -> ncurses[version='>=6.1,<7.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "ncurses==6.2=he6710b0_0 -> libstdcxx-ng[version='>=7.3.0']\n",
            "libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "packaging -> python[version='>=3.6'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "glib -> libffi[version='>=3.3'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "libwebp -> libtiff[version='>=4.1.0,<5.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "toolz -> python[version='>=3.5'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "libboost -> libstdcxx-ng[version='>=7.3.0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "rdkit -> numpy[version='>=1.12'] -> libstdcxx-ng[version='>=7.5.0']\n",
            "pcre -> libstdcxx-ng[version='>=7.5.0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> libstdcxx-ng[version='>=7.3.0|>=7.5.0']\n",
            "libedit==3.1.20181209=hc058e9b_0 -> ncurses[version='>=6.1,<7.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "libffi==3.2.1=hd88cf55_4 -> libstdcxx-ng[version='>=7.2.0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "mkl_fft -> libstdcxx-ng[version='>=7.5.0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "tqdm==4.42.1=py_0 -> python -> libstdcxx-ng[version='>=7.3.0']\n",
            "numexpr -> libstdcxx-ng[version='>=7.5.0']\n",
            "libtiff -> libstdcxx-ng[version='>=7.3.0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "cairo -> glib[version='>=2.67.4,<3.0a0'] -> libstdcxx-ng[version='>=7.5.0']\n",
            "icu -> libstdcxx-ng[version='>=7.3.0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libffi[version='>=3.2.1,<4.0a0'] -> libstdcxx-ng[version='>=7.2.0|>=7.3.0']\n",
            "libxml2 -> icu[version='>=58.2,<59.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "lz4-c -> libstdcxx-ng[version='>=7.5.0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "numpy -> libstdcxx-ng[version='>=7.5.0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "zstd -> lz4-c[version='>=1.9.3,<1.10.0a0'] -> libstdcxx-ng[version='>=7.5.0']\n",
            "zstd -> libstdcxx-ng[version='>=7.3.0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "py-boost -> libstdcxx-ng[version='>=7.3.0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pillow -> libtiff[version='>=4.1.0,<5.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "glib -> libstdcxx-ng[version='>=7.5.0']\n",
            "pandas -> libstdcxx-ng[version='>=7.5.0']\n",
            "numpy-base -> libstdcxx-ng[version='>=7.5.0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "python==3.7.6=h0371630_2 -> libstdcxx-ng[version='>=7.3.0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pillow -> libstdcxx-ng[version='>=7.5.0']\n",
            "rdkit -> libstdcxx-ng[version='>=7.3.0']\n",
            "python==3.7.6=h0371630_2 -> libffi[version='>=3.2.1,<4.0a0'] -> libstdcxx-ng[version='>=7.2.0']\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> libstdcxx-ng[version='>=7.3.0']\n",
            "mkl_random -> libstdcxx-ng[version='>=7.5.0']\n",
            "cairo -> libstdcxx-ng[version='>=7.3.0']\n",
            "\n",
            "Package mkl conflicts for:\n",
            "mkl_fft -> mkl[version='>=2021.3.0,<2022.0a0']\n",
            "rdkit -> numpy[version='>=1.12'] -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "numpy-base -> mkl-service[version='>=2.3.0,<3.0a0'] -> mkl[version='>=2021.2.0,<2022.0a0']\n",
            "pandas -> numexpr[version='>=2.7.0'] -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "numpy -> mkl-service[version='>=2.3.0,<3.0a0'] -> mkl[version='>=2021.2.0,<2022.0a0|>=2021.3.0,<2022.0a0']\n",
            "mkl_fft -> mkl-service[version='>=2.3.0,<3.0a0'] -> mkl[version='>=2021.2.0,<2022.0a0|>=2021.4.0,<2022.0a0']\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "numexpr -> mkl-service[version='>=2.3.0,<3.0a0'] -> mkl[version='>=2021.2.0,<2022.0a0']\n",
            "numpy-base -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "numexpr -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "mkl_random -> mkl[version='>=2021.2.0,<2022.0a0']\n",
            "mkl\n",
            "mkl-service -> mkl[version='>=2021.2.0,<2022.0a0']\n",
            "mkl_random -> numpy[version='>=1.16,<2.0a0'] -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "numpy -> mkl[version='>=2021.4.0,<2022.0a0']\n",
            "\n",
            "Package zlib conflicts for:\n",
            "libboost -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "libpng -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pillow -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "python-dateutil -> python -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "glib -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "zstd -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "freetype -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "libtiff -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "packaging -> python[version='>=3.6'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "tk==8.6.8=hbc83047_0 -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "libxml2 -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "cairo -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "toolz -> python[version='>=3.5'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "fontconfig -> freetype[version='>=2.10.4,<3.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "zlib==1.2.11=h7b6447c_3\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "python==3.7.6=h0371630_2 -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "libwebp -> libpng[version='>=1.6.37,<1.7.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0']\n",
            "\n",
            "Package ld_impl_linux-64 conflicts for:\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "python==3.7.6=h0371630_2 -> ld_impl_linux-64\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "ld_impl_linux-64==2.33.1=h53a641e_7\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "packaging -> python[version='>=3.6'] -> ld_impl_linux-64\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "tqdm==4.42.1=py_0 -> python -> ld_impl_linux-64\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "python-dateutil -> python -> ld_impl_linux-64\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "toolz -> python[version='>=3.5'] -> ld_impl_linux-64\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> ld_impl_linux-64\n",
            "\n",
            "Package ncurses conflicts for:\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "python==3.7.6=h0371630_2 -> ncurses[version='>=6.1,<7.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "readline==7.0=h7b6447c_5 -> ncurses[version='>=6.1,<7.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "packaging -> python[version='>=3.6'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "ncurses==6.2=he6710b0_0\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "toolz -> python[version='>=3.5'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> ncurses[version='>=6.1,<7.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "sqlite==3.31.1=h7b6447c_0 -> libedit[version='>=3.1.20181209,<3.2.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "libedit==3.1.20181209=hc058e9b_0 -> ncurses[version='>=6.1,<7.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "python-dateutil -> python -> ncurses[version='>=6.1,<7.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> ncurses[version='>=6.1,<7.0a0']\n",
            "\n",
            "Package libffi conflicts for:\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libffi[version='>=3.2.1,<4.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "glib -> libffi[version='>=3.3|>=3.3,<3.4.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "python-dateutil -> python -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "packaging -> python[version='>=3.6'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> cffi[version='>=1.8'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "toolz -> python[version='>=3.5'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "cairo -> glib[version='>=2.67.4,<3.0a0'] -> libffi[version='>=3.3|>=3.3,<3.4.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "python==3.7.6=h0371630_2 -> libffi[version='>=3.2.1,<4.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> libffi[version='>=3.2.1,<4.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "libffi==3.2.1=hd88cf55_4\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> libffi[version='>=3.2.1,<4.0a0']\n",
            "\n",
            "Package giflib conflicts for:\n",
            "pillow -> libwebp[version='>=0.3.0'] -> giflib[version='>=5.2.1,<5.3.0a0']\n",
            "libwebp -> giflib[version='>=5.2.1,<5.3.0a0']\n",
            "giflib\n",
            "\n",
            "Package _libgcc_mutex conflicts for:\n",
            "libtiff -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "fontconfig -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "py-boost -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "numpy -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libpng -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "mkl_fft -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "_libgcc_mutex==0.1=main\n",
            "tk==8.6.8=hbc83047_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "lcms2 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libwebp-base -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "mkl_random -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "sqlite==3.31.1=h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libffi==3.2.1=hd88cf55_4 -> libgcc-ng[version='>=7.2.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "cryptography==2.8=py37h1ba5d50_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "pandas -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "xz==5.2.4=h14c3975_4 -> libgcc-ng[version='>=7.2.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "_openmp_mutex -> _libgcc_mutex==0.1=main\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "freetype -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "cairo -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "numexpr -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libwebp -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "bottleneck -> libgcc-ng[version='>=11.2.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "yaml==0.1.7=had09818_2 -> libgcc-ng[version='>=7.2.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "pillow -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "icu -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "cytoolz -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "zstd -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "giflib -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "bzip2 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "python==3.7.6=h0371630_2 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libxcb -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "numpy-base -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "rdkit -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "zlib==1.2.11=h7b6447c_3 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libedit==3.1.20181209=hc058e9b_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "pcre -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libgcc-ng==9.1.0=hdf63c60_0 -> _libgcc_mutex=[build=main]\n",
            "pixman -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "lz4-c -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "readline==7.0=h7b6447c_5 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "jpeg -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "openssl==1.1.1d=h7b6447c_4 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "mkl-service -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libuuid -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "ncurses==6.2=he6710b0_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libgomp -> _libgcc_mutex==0.1=main\n",
            "libxml2 -> libgcc-ng[version='>=9.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "libboost -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "glib -> libgcc-ng[version='>=7.5.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> _libgcc_mutex[version='*|0.1',build=main]\n",
            "\n",
            "Package readline conflicts for:\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> readline[version='>=7.0,<8.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "toolz -> python[version='>=3.5'] -> readline[version='>=7.0,<8.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "python-dateutil -> python -> readline[version='>=7.0,<8.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "packaging -> python[version='>=3.6'] -> readline[version='>=7.0,<8.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "python==3.7.6=h0371630_2 -> readline[version='>=7.0,<8.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "readline==7.0=h7b6447c_5\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> readline[version='>=7.0,<8.0a0']\n",
            "\n",
            "Package libboost conflicts for:\n",
            "libboost\n",
            "rdkit -> libboost[version='>=1.73.0,<1.74.0a0']\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11]\n",
            "rdkit -> py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11]\n",
            "\n",
            "Package openssl conflicts for:\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "toolz -> python[version='>=3.5'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "packaging -> python[version='>=3.6'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "python-dateutil -> python -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "openssl==1.1.1d=h7b6447c_4\n",
            "python==3.7.6=h0371630_2 -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "tqdm==4.42.1=py_0 -> python -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.1.1d,<1.1.2a']\n",
            "\n",
            "Package libpng conflicts for:\n",
            "pillow -> freetype[version='>=2.10.4,<3.0a0'] -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "libwebp -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "fontconfig -> freetype[version='>=2.10.4,<3.0a0'] -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "freetype -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "cairo -> libpng[version='>=1.6.37,<1.7.0a0']\n",
            "libpng\n",
            "\n",
            "Package mkl-service conflicts for:\n",
            "numexpr -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "rdkit -> numpy[version='>=1.12'] -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "pandas -> numexpr[version='>=2.7.0'] -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "mkl-service\n",
            "numpy -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "mkl_fft -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "mkl_random -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "numpy-base -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> mkl-service[version='>=2.3.0,<3.0a0']\n",
            "\n",
            "Package sqlite conflicts for:\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "python-dateutil -> python -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "toolz -> python[version='>=3.5'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "packaging -> python[version='>=3.6'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "sqlite==3.31.1=h7b6447c_0\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "python==3.7.6=h0371630_2 -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> sqlite[version='>=3.30.1,<4.0a0']\n",
            "\n",
            "Package glib conflicts for:\n",
            "glib\n",
            "cairo -> glib[version='>=2.67.4,<3.0a0']\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> glib[version='>=2.67.4,<3.0a0']\n",
            "\n",
            "Package bottleneck conflicts for:\n",
            "bottleneck\n",
            "pandas -> bottleneck[version='>=1.2.1']\n",
            "rdkit -> pandas -> bottleneck[version='>=1.2.1']\n",
            "\n",
            "Package xz conflicts for:\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "rdkit -> libboost[version='>=1.73.0,<1.74.0a0'] -> xz[version='>=5.2.4,<6.0a0|>=5.2.5,<6.0a0']\n",
            "python-dateutil -> python -> xz[version='>=5.2.4,<6.0a0']\n",
            "libtiff -> xz[version='>=5.2.5,<6.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "fontconfig -> libxml2[version='>=2.9.10,<2.10.0a0'] -> xz[version='>=5.2.5,<6.0a0']\n",
            "pillow -> libtiff[version='>=4.1.0,<5.0a0'] -> xz[version='>=5.2.4,<6.0a0|>=5.2.5,<6.0a0']\n",
            "libwebp -> libtiff[version='>=4.1.0,<5.0a0'] -> xz[version='>=5.2.5,<6.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "packaging -> python[version='>=3.6'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11] -> xz[version='>=5.2.4,<6.0a0|>=5.2.5,<6.0a0']\n",
            "libboost -> xz[version='>=5.2.5,<6.0a0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "tqdm==4.42.1=py_0 -> python -> xz[version='>=5.2.4,<6.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "python==3.7.6=h0371630_2 -> xz[version='>=5.2.4,<6.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "libxml2 -> xz[version='>=5.2.5,<6.0a0']\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0'] -> xz[version='>=5.2.5,<6.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "zstd -> xz[version='>=5.2.5,<6.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "xz==5.2.4=h14c3975_4\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "toolz -> python[version='>=3.5'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> xz[version='>=5.2.4,<6.0a0']\n",
            "\n",
            "Package asn1crypto conflicts for:\n",
            "asn1crypto==1.3.0=py37_0\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> asn1crypto[version='>=0.21.0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> asn1crypto[version='>=0.21.0']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> asn1crypto[version='>=0.21.0']\n",
            "\n",
            "Package python conflicts for:\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "python==3.7.6=h0371630_2\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "python-dateutil -> six[version='>=1.5'] -> python[version='>=3.7,<3.8.0a0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "toolz -> python[version='>=3.5']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "packaging -> pyparsing[version='>=2.0.2,!=3.0.5'] -> python[version='>=3.7,<3.8.0a0']\n",
            "packaging -> python[version='>=3.6']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0']\n",
            "pillow -> python[version='>=3.7,<3.8.0a0']\n",
            "numexpr -> packaging -> python[version='>=3.6']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "bottleneck -> python[version='>=3.7,<3.8.0a0']\n",
            "pyopenssl==19.1.0=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> tqdm -> python\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "pandas -> python-dateutil[version='>=2.7.3'] -> python\n",
            "python-dateutil -> python\n",
            "cytoolz -> toolz[version='>=0.10.0'] -> python[version='>=3.5']\n",
            "cytoolz -> python[version='>=3.7,<3.8.0a0']\n",
            "pandas -> python[version='>=3.7,<3.8.0a0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "conda==4.8.2=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "rdkit -> python[version='>=3.7,<3.8.0a0']\n",
            "urllib3==1.25.8=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0']\n",
            "py-boost -> python[version='>=3.7,<3.8.0a0']\n",
            "tqdm==4.42.1=py_0 -> python\n",
            "\n",
            "Package libgcc-ng conflicts for:\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> libgcc-ng[version='>=7.3.0']\n",
            "idna==2.8=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "openssl==1.1.1d=h7b6447c_4 -> libgcc-ng[version='>=7.3.0']\n",
            "libwebp-base -> libgcc-ng[version='>=7.5.0']\n",
            "pandas -> libgcc-ng[version='>=7.5.0']\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0']\n",
            "cairo -> freetype[version='>=2.10.4,<3.0a0'] -> libgcc-ng[version='>=7.5.0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> openssl[version='>=1.1.1d,<1.1.2a'] -> libgcc-ng[version='>=7.5.0']\n",
            "pytz -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libxml2 -> icu[version='>=58.2,<59.0a0'] -> libgcc-ng[version='>=7.3.0|>=7.5.0']\n",
            "cairo -> libgcc-ng[version='>=7.3.0']\n",
            "tk==8.6.8=hbc83047_0 -> libgcc-ng[version='>=7.3.0']\n",
            "numpy-base -> libgcc-ng[version='>=7.5.0']\n",
            "freetype -> libpng[version='>=1.6.37,<1.7.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libuuid -> libgcc-ng[version='>=7.5.0']\n",
            "pip==20.0.2=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libxml2 -> libgcc-ng[version='>=9.3.0']\n",
            "pyparsing -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "setuptools==45.2.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "lz4-c -> libgcc-ng[version='>=7.5.0']\n",
            "pycosat==0.6.3=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0']\n",
            "zstd -> libgcc-ng[version='>=7.3.0']\n",
            "sqlite==3.31.1=h7b6447c_0 -> libgcc-ng[version='>=7.3.0']\n",
            "pandas -> bottleneck[version='>=1.2.1'] -> libgcc-ng[version='>=11.2.0|>=7.3.0']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> libgcc-ng[version='>=7.3.0']\n",
            "readline==7.0=h7b6447c_5 -> libgcc-ng[version='>=7.3.0']\n",
            "pcre -> libgcc-ng[version='>=7.5.0']\n",
            "conda==4.8.2=py37_0 -> conda-package-handling[version='>=1.3.0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libwebp -> libgcc-ng[version='>=7.5.0']\n",
            "ncurses==6.2=he6710b0_0 -> libgcc-ng[version='>=7.3.0']\n",
            "libgcc-ng==9.1.0=hdf63c60_0\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> libgcc-ng[version='>=7.3.0']\n",
            "mkl_random -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libwebp -> giflib[version='>=5.2.1,<5.3.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "requests==2.22.0=py37_1 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libboost -> libgcc-ng[version='>=7.3.0']\n",
            "python==3.7.6=h0371630_2 -> libgcc-ng[version='>=7.3.0']\n",
            "bzip2 -> libgcc-ng[version='>=7.3.0']\n",
            "six==1.14.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "pysocks==1.7.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "mkl-service -> libgcc-ng[version='>=7.5.0']\n",
            "cytoolz -> libgcc-ng[version='>=7.3.0']\n",
            "toolz -> python[version='>=3.5'] -> libgcc-ng[version='>=7.3.0']\n",
            "numexpr -> libgcc-ng[version='>=7.5.0']\n",
            "rdkit -> libgcc-ng[version='>=7.3.0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libgcc-ng[version='>=7.3.0']\n",
            "python==3.7.6=h0371630_2 -> libffi[version='>=3.2.1,<4.0a0'] -> libgcc-ng[version='>=7.2.0|>=7.5.0']\n",
            "yaml==0.1.7=had09818_2 -> libgcc-ng[version='>=7.2.0']\n",
            "lcms2 -> jpeg[version='>=9b,<10a'] -> libgcc-ng[version='>=7.5.0']\n",
            "freetype -> libgcc-ng[version='>=7.5.0']\n",
            "mkl_fft -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "cffi==1.14.0=py37h2e261b9_0 -> libffi[version='>=3.2.1,<4.0a0'] -> libgcc-ng[version='>=7.2.0']\n",
            "libtiff -> libgcc-ng[version='>=7.3.0']\n",
            "icu -> libgcc-ng[version='>=7.3.0']\n",
            "glib -> libffi[version='>=3.3'] -> libgcc-ng[version='>=7.3.0']\n",
            "numexpr -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> yaml[version='>=0.1.7,<0.2.0a0'] -> libgcc-ng[version='>=7.2.0']\n",
            "libxcb -> libgcc-ng[version='>=7.5.0']\n",
            "wheel==0.34.2=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "py-boost -> libgcc-ng[version='>=7.3.0']\n",
            "chardet==3.0.4=py37_1003 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libffi==3.2.1=hd88cf55_4 -> libgcc-ng[version='>=7.2.0']\n",
            "xz==5.2.4=h14c3975_4 -> libgcc-ng[version='>=7.2.0']\n",
            "pycparser==2.19=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "libboost -> xz[version='>=5.2.5,<6.0a0'] -> libgcc-ng[version='>=7.5.0']\n",
            "certifi==2019.11.28=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "zlib==1.2.11=h7b6447c_3 -> libgcc-ng[version='>=7.3.0']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> libgcc-ng[version='>=7.3.0']\n",
            "libpng -> libgcc-ng[version='>=7.3.0']\n",
            "zstd -> lz4-c[version='>=1.9.3,<1.10.0a0'] -> libgcc-ng[version='>=7.5.0']\n",
            "asn1crypto==1.3.0=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "glib -> libgcc-ng[version='>=7.5.0']\n",
            "python-dateutil -> python -> libgcc-ng[version='>=7.3.0']\n",
            "rdkit -> freetype[version='>=2.10.3,<3.0a0'] -> libgcc-ng[version='>=7.5.0']\n",
            "pillow -> lcms2[version='>=2.12,<3.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "numpy -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "lcms2 -> libgcc-ng[version='>=7.3.0']\n",
            "fontconfig -> freetype[version='>=2.10.4,<3.0a0'] -> libgcc-ng[version='>=7.5.0|>=9.3.0']\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> libgcc-ng[version='>=7.3.0|>=7.5.0']\n",
            "mkl_random -> libgcc-ng[version='>=7.5.0']\n",
            "mkl_fft -> libgcc-ng[version='>=7.5.0']\n",
            "libedit==3.1.20181209=hc058e9b_0 -> libgcc-ng[version='>=7.3.0']\n",
            "fontconfig -> libgcc-ng[version='>=7.3.0']\n",
            "tqdm==4.42.1=py_0 -> python -> libgcc-ng[version='>=7.3.0']\n",
            "jpeg -> libgcc-ng[version='>=7.5.0']\n",
            "numpy -> libgcc-ng[version='>=7.5.0']\n",
            "numpy-base -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "pixman -> libgcc-ng[version='>=7.5.0']\n",
            "mkl-service -> python[version='>=3.7,<3.8.0a0'] -> libgcc-ng[version='>=7.3.0']\n",
            "pillow -> libgcc-ng[version='>=7.5.0']\n",
            "libtiff -> jpeg[version='>=9b,<10a'] -> libgcc-ng[version='>=7.5.0']\n",
            "bottleneck -> libgcc-ng[version='>=11.2.0']\n",
            "giflib -> libgcc-ng[version='>=7.3.0']\n",
            "packaging -> python[version='>=3.6'] -> libgcc-ng[version='>=7.3.0']\n",
            "\n",
            "Package numexpr conflicts for:\n",
            "numexpr\n",
            "pandas -> numexpr[version='>=2.7.0']\n",
            "rdkit -> pandas -> numexpr[version='>=2.7.0']\n",
            "\n",
            "Package yaml conflicts for:\n",
            "yaml==0.1.7=had09818_2\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0 -> yaml[version='>=0.1.7,<0.2.0a0']\n",
            "conda==4.8.2=py37_0 -> ruamel_yaml[version='>=0.11.14,<0.16'] -> yaml[version='>=0.1.7,<0.2.0a0']\n",
            "\n",
            "Package ca-certificates conflicts for:\n",
            "cryptography==2.8=py37h1ba5d50_0 -> openssl[version='>=1.1.1d,<1.1.2a'] -> ca-certificates\n",
            "openssl==1.1.1d=h7b6447c_4 -> ca-certificates\n",
            "python==3.7.6=h0371630_2 -> openssl[version='>=1.1.1d,<1.1.2a'] -> ca-certificates\n",
            "ca-certificates==2020.1.1=0\n",
            "\n",
            "Package intel-openmp conflicts for:\n",
            "numexpr -> mkl[version='>=2021.4.0,<2022.0a0'] -> intel-openmp=2021\n",
            "mkl -> intel-openmp=2021\n",
            "intel-openmp\n",
            "numpy-base -> mkl[version='>=2021.4.0,<2022.0a0'] -> intel-openmp=2021\n",
            "mkl-service -> mkl[version='>=2021.2.0,<2022.0a0'] -> intel-openmp=2021\n",
            "mkl_fft -> mkl[version='>=2021.3.0,<2022.0a0'] -> intel-openmp=2021\n",
            "mkl_random -> mkl[version='>=2021.2.0,<2022.0a0'] -> intel-openmp=2021\n",
            "numpy -> mkl[version='>=2021.4.0,<2022.0a0'] -> intel-openmp=2021\n",
            "\n",
            "Package pillow conflicts for:\n",
            "pillow\n",
            "rdkit -> pillow\n",
            "\n",
            "Package blas conflicts for:\n",
            "mkl_random -> numpy[version='>=1.16,<2.0a0'] -> blas==1.0=mkl\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> blas==1.0=mkl\n",
            "rdkit -> numpy[version='>=1.12'] -> blas==1.0=mkl\n",
            "numpy-base -> blas==1.0=mkl\n",
            "pandas -> numexpr[version='>=2.7.0'] -> blas==1.0=mkl\n",
            "numpy -> mkl_random -> blas=[build=mkl]\n",
            "numexpr -> blas==1.0=mkl\n",
            "mkl_random -> blas=[build=mkl]\n",
            "blas\n",
            "numpy -> blas==1.0=mkl\n",
            "mkl_fft -> blas==1.0=mkl\n",
            "\n",
            "Package six conflicts for:\n",
            "numpy -> mkl-service[version='>=2.3.0,<3.0a0'] -> six\n",
            "pandas -> python-dateutil[version='>=2.7.3'] -> six[version='>=1.5']\n",
            "pyopenssl==19.1.0=py37_0 -> six[version='>=1.5.2']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> six[version='>=1.4.1|>=1.5.2']\n",
            "numpy-base -> mkl-service[version='>=2.3.0,<3.0a0'] -> six\n",
            "six==1.14.0=py37_0\n",
            "mkl-service -> six\n",
            "mkl_fft -> mkl-service[version='>=2.3.0,<3.0a0'] -> six\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> six\n",
            "mkl_random -> mkl-service[version='>=2.3.0,<3.0a0'] -> six\n",
            "cryptography==2.8=py37h1ba5d50_0 -> six[version='>=1.4.1']\n",
            "conda==4.8.2=py37_0 -> conda-package-handling[version='>=1.3.0'] -> six[version='>=1.5.2']\n",
            "numexpr -> mkl-service[version='>=2.3.0,<3.0a0'] -> six\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> six[version='>=1.4.1']\n",
            "python-dateutil -> six[version='>=1.5']\n",
            "\n",
            "Package cffi conflicts for:\n",
            "cffi==1.14.0=py37h2e261b9_0\n",
            "cryptography==2.8=py37h1ba5d50_0 -> cffi[version='>=1.8']\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> cffi[version='>=1.8']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> cffi[version='>=1.8']\n",
            "\n",
            "Package libwebp-base conflicts for:\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0'] -> libwebp-base\n",
            "pillow -> libtiff[version='>=4.1.0,<5.0a0'] -> libwebp-base\n",
            "libtiff -> libwebp-base\n",
            "libwebp-base\n",
            "libwebp -> libwebp-base\n",
            "\n",
            "Package libwebp conflicts for:\n",
            "libwebp\n",
            "rdkit -> pillow -> libwebp[version='>=0.3.0|>=1.2.0,<1.3.0a0']\n",
            "pillow -> libwebp[version='>=0.3.0|>=1.2.0,<1.3.0a0']\n",
            "\n",
            "Package icu conflicts for:\n",
            "libxml2 -> icu[version='>=58.2,<59.0a0']\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11] -> icu[version='>=58.2,<59.0a0']\n",
            "icu\n",
            "fontconfig -> libxml2[version='>=2.9.10,<2.10.0a0'] -> icu[version='>=58.2,<59.0a0']\n",
            "rdkit -> libboost[version='>=1.73.0,<1.74.0a0'] -> icu[version='>=58.2,<59.0a0']\n",
            "libboost -> icu[version='>=58.2,<59.0a0']\n",
            "\n",
            "Package numpy-base conflicts for:\n",
            "numpy -> numpy-base==1.21.5=py37hf524024_2\n",
            "rdkit -> numpy[version='>=1.12'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "numexpr -> numpy[version='>=1.16.6,<2.0a0'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "mkl_fft -> numpy[version='>=1.16,<2.0a0'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "numpy-base\n",
            "mkl_random -> numpy[version='>=1.16,<2.0a0'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "pandas -> numpy[version='>=1.19.2,<2.0a0'] -> numpy-base==1.21.5=py37hf524024_2\n",
            "\n",
            "Package pysocks conflicts for:\n",
            "pysocks==1.7.1=py37_0\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1'] -> pysocks[version='>=1.5.6,<2.0,!=1.5.7']\n",
            "urllib3==1.25.8=py37_0 -> pysocks[version='>=1.5.6,<2.0,!=1.5.7']\n",
            "\n",
            "Package freetype conflicts for:\n",
            "pillow -> freetype[version='>=2.10.4,<3.0a0']\n",
            "cairo -> freetype[version='>=2.10.4,<3.0a0']\n",
            "fontconfig -> freetype[version='>=2.10.4,<3.0a0']\n",
            "freetype\n",
            "rdkit -> freetype[version='>=2.10.3,<3.0a0']\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> freetype[version='>=2.10.4,<3.0a0']\n",
            "\n",
            "Package cryptography conflicts for:\n",
            "urllib3==1.25.8=py37_0 -> pyopenssl[version='>=0.14'] -> cryptography[version='>=2.8']\n",
            "cryptography==2.8=py37h1ba5d50_0\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8']\n",
            "conda==4.8.2=py37_0 -> pyopenssl[version='>=16.2.0'] -> cryptography[version='>=2.8']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4']\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1'] -> cryptography[version='>=1.3.4']\n",
            "\n",
            "Package certifi conflicts for:\n",
            "certifi==2019.11.28=py37_0\n",
            "requests==2.22.0=py37_1 -> certifi[version='>=2017.4.17']\n",
            "urllib3==1.25.8=py37_0 -> certifi\n",
            "wheel==0.34.2=py37_0 -> setuptools -> certifi[version='>=2016.9.26']\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1'] -> certifi\n",
            "setuptools==45.2.0=py37_0 -> certifi[version='>=2016.9.26']\n",
            "conda==4.8.2=py37_0 -> requests[version='>=2.18.4,<3'] -> certifi[version='>=2016.9.26|>=2017.4.17']\n",
            "pip==20.0.2=py37_1 -> setuptools -> certifi[version='>=2016.9.26']\n",
            "\n",
            "Package pytz conflicts for:\n",
            "pandas -> pytz[version='>=2017.3']\n",
            "pytz\n",
            "rdkit -> pandas -> pytz[version='>=2017.3']\n",
            "\n",
            "Package libxcb conflicts for:\n",
            "libxcb\n",
            "cairo -> libxcb[version='>=1.14,<2.0a0']\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> libxcb[version='>=1.14,<2.0a0']\n",
            "\n",
            "Package packaging conflicts for:\n",
            "packaging\n",
            "numexpr -> packaging\n",
            "pandas -> numexpr[version='>=2.7.0'] -> packaging\n",
            "\n",
            "Package tqdm conflicts for:\n",
            "conda==4.8.2=py37_0 -> conda-package-handling[version='>=1.3.0'] -> tqdm\n",
            "conda-package-handling==1.6.0=py37h7b6447c_0 -> tqdm\n",
            "tqdm==4.42.1=py_0\n",
            "\n",
            "Package pcre conflicts for:\n",
            "pcre\n",
            "cairo -> glib[version='>=2.67.4,<3.0a0'] -> pcre[version='>=8.45,<9.0a0']\n",
            "glib -> pcre[version='>=8.45,<9.0a0']\n",
            "\n",
            "Package jpeg conflicts for:\n",
            "libtiff -> jpeg[version='>=9b,<10a']\n",
            "rdkit -> pillow -> jpeg\n",
            "lcms2 -> jpeg[version='>=9b,<10a']\n",
            "pillow -> lcms2[version='>=2.12,<3.0a0'] -> jpeg[version='>=9b,<10a|>=9d,<10a']\n",
            "libwebp -> jpeg[version='>=9d,<10a']\n",
            "jpeg\n",
            "libwebp -> libtiff[version='>=4.1.0,<5.0a0'] -> jpeg[version='>=9b,<10a']\n",
            "pillow -> jpeg\n",
            "\n",
            "Package python-dateutil conflicts for:\n",
            "python-dateutil\n",
            "rdkit -> pandas -> python-dateutil[version='>=2.7.3']\n",
            "pandas -> python-dateutil[version='>=2.7.3']\n",
            "\n",
            "Package requests conflicts for:\n",
            "conda==4.8.2=py37_0 -> requests[version='>=2.18.4,<3']\n",
            "requests==2.22.0=py37_1\n",
            "\n",
            "Package wheel conflicts for:\n",
            "wheel==0.34.2=py37_0\n",
            "pip==20.0.2=py37_1 -> wheel\n",
            "\n",
            "Package urllib3 conflicts for:\n",
            "conda==4.8.2=py37_0 -> requests[version='>=2.18.4,<3'] -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1']\n",
            "urllib3==1.25.8=py37_0\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1']\n",
            "\n",
            "Package pyopenssl conflicts for:\n",
            "pyopenssl==19.1.0=py37_0\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1'] -> pyopenssl[version='>=0.14']\n",
            "conda==4.8.2=py37_0 -> pyopenssl[version='>=16.2.0']\n",
            "urllib3==1.25.8=py37_0 -> pyopenssl[version='>=0.14']\n",
            "\n",
            "Package libgomp conflicts for:\n",
            "libgomp\n",
            "_openmp_mutex -> libgomp[version='>=7.5.0']\n",
            "\n",
            "Package libedit conflicts for:\n",
            "libedit==3.1.20181209=hc058e9b_0\n",
            "python==3.7.6=h0371630_2 -> sqlite[version='>=3.30.1,<4.0a0'] -> libedit[version='>=3.1.20181209,<3.2.0a0']\n",
            "sqlite==3.31.1=h7b6447c_0 -> libedit[version='>=3.1.20181209,<3.2.0a0']\n",
            "\n",
            "Package idna conflicts for:\n",
            "conda==4.8.2=py37_0 -> requests[version='>=2.18.4,<3'] -> idna[version='>=2.5,<2.9']\n",
            "cryptography==2.8=py37h1ba5d50_0 -> idna[version='>=2.1']\n",
            "urllib3==1.25.8=py37_0 -> idna[version='>=2.0.0']\n",
            "requests==2.22.0=py37_1 -> idna[version='>=2.5,<2.9']\n",
            "requests==2.22.0=py37_1 -> urllib3[version='>=1.21.1,<1.26,!=1.25.0,!=1.25.1'] -> idna[version='>=2.0.0']\n",
            "urllib3==1.25.8=py37_0 -> cryptography[version='>=1.3.4'] -> idna[version='>=2.1']\n",
            "idna==2.8=py37_0\n",
            "pyopenssl==19.1.0=py37_0 -> cryptography[version='>=2.8'] -> idna[version='>=2.1']\n",
            "\n",
            "Package cairo conflicts for:\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0']\n",
            "cairo\n",
            "\n",
            "Package setuptools conflicts for:\n",
            "wheel==0.34.2=py37_0 -> setuptools\n",
            "conda==4.8.2=py37_0 -> setuptools[version='>=31.0.1']\n",
            "pip==20.0.2=py37_1 -> setuptools\n",
            "setuptools==45.2.0=py37_0\n",
            "\n",
            "Package toolz conflicts for:\n",
            "toolz\n",
            "cytoolz -> toolz[version='>=0.10.0']\n",
            "\n",
            "Package pixman conflicts for:\n",
            "pixman\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> pixman[version='>=0.40.0,<1.0a0']\n",
            "cairo -> pixman[version='>=0.40.0,<1.0a0']\n",
            "\n",
            "Package fontconfig conflicts for:\n",
            "cairo -> fontconfig[version='>=2.13.1,<3.0a0']\n",
            "fontconfig\n",
            "rdkit -> cairo[version='>=1.14.12,<2.0a0'] -> fontconfig[version='>=2.13.1,<3.0a0']\n",
            "\n",
            "Package mkl_fft conflicts for:\n",
            "pandas -> numpy[version='>=1.19.2,<2.0a0'] -> mkl_fft\n",
            "bottleneck -> numpy[version='>=1.16.6,<2.0a0'] -> mkl_fft\n",
            "mkl_random -> numpy[version='>=1.16,<2.0a0'] -> mkl_fft\n",
            "numexpr -> numpy[version='>=1.16.6,<2.0a0'] -> mkl_fft\n",
            "mkl_fft\n",
            "numpy -> mkl_fft\n",
            "rdkit -> numpy[version='>=1.12'] -> mkl_fft\n",
            "\n",
            "Package pyparsing conflicts for:\n",
            "numexpr -> packaging -> pyparsing[version='>=2.0.2,!=3.0.5']\n",
            "pyparsing\n",
            "packaging -> pyparsing[version='>=2.0.2,!=3.0.5']\n",
            "\n",
            "Package pandas conflicts for:\n",
            "rdkit -> pandas\n",
            "pandas\n",
            "\n",
            "Package lz4-c conflicts for:\n",
            "lz4-c\n",
            "libtiff -> zstd[version='>=1.4.5,<1.5.0a0'] -> lz4-c[version='>=1.9.3,<1.10.0a0']\n",
            "zstd -> lz4-c[version='>=1.9.3,<1.10.0a0']\n",
            "libboost -> zstd[version='>=1.4.5,<1.5.0a0'] -> lz4-c[version='>=1.9.3,<1.10.0a0']\n",
            "\n",
            "Package py-boost conflicts for:\n",
            "py-boost\n",
            "rdkit -> py-boost\n",
            "\n",
            "Package chardet conflicts for:\n",
            "conda==4.8.2=py37_0 -> requests[version='>=2.18.4,<3'] -> chardet[version='>=3.0.2,<3.1.0']\n",
            "chardet==3.0.4=py37_1003\n",
            "requests==2.22.0=py37_1 -> chardet[version='>=3.0.2,<3.1.0']\n",
            "\n",
            "Package pycosat conflicts for:\n",
            "conda==4.8.2=py37_0 -> pycosat[version='>=0.6.3']\n",
            "pycosat==0.6.3=py37h7b6447c_0\n",
            "\n",
            "Package ruamel_yaml conflicts for:\n",
            "conda==4.8.2=py37_0 -> ruamel_yaml[version='>=0.11.14,<0.16']\n",
            "ruamel_yaml==0.15.87=py37h7b6447c_0\n",
            "\n",
            "Package bzip2 conflicts for:\n",
            "py-boost -> libboost[version='>=1.73.0,<1.73.1.0a0',build=h3ff78a5_11] -> bzip2[version='>=1.0.8,<2.0a0']\n",
            "bzip2\n",
            "libboost -> bzip2[version='>=1.0.8,<2.0a0']\n",
            "rdkit -> libboost[version='>=1.73.0,<1.74.0a0'] -> bzip2[version='>=1.0.8,<2.0a0']\n",
            "\n",
            "Package libtiff conflicts for:\n",
            "pillow -> libtiff[version='>=4.1.0,<5.0a0']\n",
            "libwebp -> libtiff[version='>=4.1.0,<5.0a0']\n",
            "rdkit -> pillow -> libtiff[version='>=4.1.0,<5.0a0']\n",
            "libtiff\n",
            "lcms2 -> libtiff[version='>=4.1.0,<5.0a0']\n",
            "\n",
            "Package pycparser conflicts for:\n",
            "cryptography==2.8=py37h1ba5d50_0 -> cffi[version='>=1.8'] -> pycparser\n",
            "pycparser==2.19=py37_0\n",
            "cffi==1.14.0=py37h2e261b9_0 -> pycparser\n",
            "\n",
            "Package lcms2 conflicts for:\n",
            "pillow -> lcms2[version='>=2.12,<3.0a0']\n",
            "lcms2\n",
            "rdkit -> pillow -> lcms2[version='>=2.12,<3.0a0']\n",
            "\n",
            "Package libxml2 conflicts for:\n",
            "cairo -> fontconfig[version='>=2.13.1,<3.0a0'] -> libxml2[version='>=2.9.10,<2.10.0a0']\n",
            "libxml2\n",
            "fontconfig -> libxml2[version='>=2.9.10,<2.10.0a0']\n",
            "\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "# All requested packages already installed.\n",
            "\n",
            "Retrieving notices: ...working... done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Load Bioactivity Data and import files"
      ],
      "metadata": {
        "id": "teSZZzV6E89C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('bioactivity_data_preprocessed.csv')"
      ],
      "metadata": {
        "id": "7FaRDS-MbBPk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors, Lipinski"
      ],
      "metadata": {
        "id": "iYuGwkNfbDrT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Calculate Lipinski descriptors\n",
        "\n",
        "The Lipinski rule of 5 is based on the Absorption, Distribution, Metabolism and Excretion (ADME) that is also known as the pharmacokinetic profile. Lipinski analyzed all orally active FDA-approved drugs in the formulation of what is to be known as the Rule-of-Five or Lipinski's Rule.\n",
        "The Lipinski's Rule stated the following:\n",
        "Molecular weight < 500 Dalton\n",
        "Octanol-water partition coefficient (LogP) < 5\n",
        "Hydrogen bond donors < 5\n",
        "Hydrogen bond acceptors < 10\n",
        "(Benet LZ, Hosey CM, Ursu O, Oprea TI. BDDCS, the Rule of 5 and drugability. Adv Drug Deliv Rev. 2016 Jun 1;101:89-98. doi: 10.1016/j.addr.2016.05.007. Epub 2016 May 13. PMID: 27182629; PMCID: PMC4910824.)"
      ],
      "metadata": {
        "id": "J-oL98hEHEBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lipinski(smiles, verbose=False):\n",
        "  moldata = []\n",
        "  for elem in smiles:\n",
        "    mol = Chem.MolFromSmiles(elem)\n",
        "    moldata.append(mol)\n",
        "  print(moldata)\n",
        "  \n",
        "  all_data = np.arange(1,1)\n",
        "  i=0\n",
        "\n",
        "  for mol in moldata:\n",
        "    mol_weight = Descriptors.MolWt(mol)\n",
        "    mol_logP = Descriptors.MolLogP(mol)\n",
        "    mol_H_donors= Lipinski.NumHDonors(mol)\n",
        "    mol_H_acceptors = Lipinski.NumHAcceptors(mol)\n",
        "\n",
        "    each_row = np.array ([mol_weight, mol_logP, mol_H_donors, mol_H_acceptors])\n",
        "\n",
        "    if i == 0:\n",
        "      all_data = each_row\n",
        "      i=i+1\n",
        "    else:\n",
        "      all_data = np.vstack([all_data, each_row])\n",
        "  #dimension = all_data.shape()\n",
        "  #print(dimension)\n",
        "\n",
        "  columnNames=[\"MolWt\",\"LogP\",\"NumHDonors\",\"NumHAcceptors\"]   \n",
        "  descriptors = pd.DataFrame(data=all_data,columns=columnNames)\n",
        "    \n",
        "  return descriptors\n"
      ],
      "metadata": {
        "id": "-g-BuZG2bI_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = lipinski(df.canonical_smiles)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "r6hrqKZmbMaL",
        "outputId": "c3ca23a6-8da4-496d-edfb-da9679ade533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<rdkit.Chem.rdchem.Mol object at 0x7fdc15113030>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151bff30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151bff80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151bf3a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6d1c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6d170>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6d260>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6d120>, <rdkit.Chem.rdchem.Mol object at 0x7fdc15078ee0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151c3530>, <rdkit.Chem.rdchem.Mol object at 0x7fdc15398b70>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151bcc60>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14fdb030>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14fdbc10>, <rdkit.Chem.rdchem.Mol object at 0x7fdc15007800>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151c0850>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151c0e40>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151c0f30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151c08a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc17eb38a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151cdf80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc151cde90>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ec3e40>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ec3990>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ec3f30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ec31c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ec3d00>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba670>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ebaf80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ebaf30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba580>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba940>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba760>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba7b0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba800>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba850>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba710>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14eba620>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14ebaee0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a4e0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a8a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a260>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a120>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6ac60>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6ad00>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a760>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e6a3a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f080>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f1c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f5d0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc150a8530>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e83120>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f670>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f6c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f710>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f760>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f7b0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f800>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f850>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f8a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f8f0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f940>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f990>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2f9e0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fa30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fa80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fad0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fb20>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fb70>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fbc0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fc10>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fc60>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fcb0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fd00>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fd50>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fda0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fdf0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fe40>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fe90>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2fee0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ff30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ff80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e030>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e080>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e0d0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e120>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e170>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e1c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e210>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e260>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e2b0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e300>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e350>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e3a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e3f0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e440>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e490>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e4e0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e530>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e580>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e5d0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e620>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e670>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e6c0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e710>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e760>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e7b0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e800>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e850>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e8a0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e8f0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e940>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e990>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2e9e0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ea30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ea80>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ead0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2eb20>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2eb70>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ebc0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ec10>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ec60>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ecb0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ed00>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ed50>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2eda0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2edf0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ee40>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ee90>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2eee0>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ef30>, <rdkit.Chem.rdchem.Mol object at 0x7fdc14e2ef80>]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MolWt     LogP  NumHDonors  NumHAcceptors\n",
              "0    264.346  3.10180         1.0            4.0\n",
              "1    270.300  3.37140         1.0            3.0\n",
              "2    367.470  4.71860         2.0            4.0\n",
              "3    326.417  4.88550         1.0            4.0\n",
              "4    248.347  3.40162         1.0            3.0\n",
              "..       ...      ...         ...            ...\n",
              "127  364.422  4.54542         2.0            4.0\n",
              "128  264.302  2.23902         2.0            4.0\n",
              "129  307.327  1.88900         3.0            5.0\n",
              "130  282.364  3.98102         0.0            2.0\n",
              "131  312.346  2.73182         0.0            4.0\n",
              "\n",
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          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataframe with Lipinsky discriptors corresponding to each molecule"
      ],
      "metadata": {
        "id": "TIVppdTDHcbI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.concat([df,df2], axis=1) \n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "cPsD3on3bO4u",
        "outputId": "c3b669cd-706a-40df-b76d-8b1b679e38e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                  canonical_smiles  \\\n",
              "0         CHEMBL123184                  COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1   \n",
              "1         CHEMBL122699                CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O   \n",
              "2         CHEMBL124117  CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1   \n",
              "3         CHEMBL332364           CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O   \n",
              "4         CHEMBL432983                   CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O   \n",
              "..                 ...                                               ...   \n",
              "127       CHEMBL451059      Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1   \n",
              "128       CHEMBL462375                  Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "129       CHEMBL462376            CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462553                Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "131       CHEMBL454282         Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "     standard_value bioactivity_class    MolWt     LogP  NumHDonors  \\\n",
              "0         500000.00          inactive  264.346  3.10180         1.0   \n",
              "1         263000.00          inactive  270.300  3.37140         1.0   \n",
              "2         119000.00          inactive  367.470  4.71860         2.0   \n",
              "3          52000.00          inactive  326.417  4.88550         1.0   \n",
              "4         112000.00          inactive  248.347  3.40162         1.0   \n",
              "..              ...               ...      ...      ...         ...   \n",
              "127            8.47            active  364.422  4.54542         2.0   \n",
              "128           34.40            active  264.302  2.23902         2.0   \n",
              "129           31.40            active  307.327  1.88900         3.0   \n",
              "130          138.10            active  282.364  3.98102         0.0   \n",
              "131            1.99            active  312.346  2.73182         0.0   \n",
              "\n",
              "     NumHAcceptors  \n",
              "0              4.0  \n",
              "1              3.0  \n",
              "2              4.0  \n",
              "3              4.0  \n",
              "4              3.0  \n",
              "..             ...  \n",
              "127            4.0  \n",
              "128            4.0  \n",
              "129            5.0  \n",
              "130            2.0  \n",
              "131            4.0  \n",
              "\n",
              "[132 rows x 8 columns]"
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              "      <th>molecule_chembl_id</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL123184</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL122699</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O</td>\n",
              "      <td>263000.00</td>\n",
              "      <td>inactive</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL124117</td>\n",
              "      <td>CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1</td>\n",
              "      <td>119000.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL332364</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O</td>\n",
              "      <td>52000.00</td>\n",
              "      <td>inactive</td>\n",
              "      <td>326.417</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL432983</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O</td>\n",
              "      <td>112000.00</td>\n",
              "      <td>inactive</td>\n",
              "      <td>248.347</td>\n",
              "      <td>3.40162</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>CHEMBL451059</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1</td>\n",
              "      <td>8.47</td>\n",
              "      <td>active</td>\n",
              "      <td>364.422</td>\n",
              "      <td>4.54542</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>34.40</td>\n",
              "      <td>active</td>\n",
              "      <td>264.302</td>\n",
              "      <td>2.23902</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462376</td>\n",
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              "      <td>31.40</td>\n",
              "      <td>active</td>\n",
              "      <td>307.327</td>\n",
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              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462553</td>\n",
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              "      <td>138.10</td>\n",
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              "    <tr>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13e6ccda-4464-46f9-bb5f-5c55e373770c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Convert IC50 to pIC50\n",
        "\n",
        "To allow IC50 data to be more uniformly distributed, we will convert IC50 to the negative logarithmic scale which is essentially -log10(IC50).\n",
        "This custom function pIC50 will accept a DataFrame as input and will:\n",
        "Take the IC50 values from the standard_value column and converts it from nM to M by multiplying the value by 10 −9 \n",
        "Take the molar value and apply -log10\n",
        "Delete the standard_value column and create a new pIC50 column"
      ],
      "metadata": {
        "id": "tPzdV-0XHvdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def pIC50(input):\n",
        "    pIC50 = []\n",
        "\n",
        "    for i in input['standard_value']:\n",
        "        molar = i*(10**-9) # Converts nM to M\n",
        "        pIC50.append(-np.log10(molar))\n",
        "\n",
        "    input['pIC50'] = pIC50\n",
        "    x = input.drop('standard_value', 1)\n",
        "        \n",
        "    return x"
      ],
      "metadata": {
        "id": "CB3pUhvkbSo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final = pIC50(df2)\n",
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "tFqzD56Nbbuw",
        "outputId": "d130469c-174f-438f-f654-3cd8fdad7067"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                  canonical_smiles  \\\n",
              "0         CHEMBL123184                  COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1   \n",
              "1         CHEMBL122699                CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O   \n",
              "2         CHEMBL124117  CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1   \n",
              "3         CHEMBL332364           CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O   \n",
              "4         CHEMBL432983                   CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O   \n",
              "..                 ...                                               ...   \n",
              "127       CHEMBL451059      Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1   \n",
              "128       CHEMBL462375                  Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "129       CHEMBL462376            CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462553                Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "131       CHEMBL454282         Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "    bioactivity_class    MolWt     LogP  NumHDonors  NumHAcceptors     pIC50  \n",
              "0            inactive  264.346  3.10180         1.0            4.0  3.301030  \n",
              "1            inactive  270.300  3.37140         1.0            3.0  3.580044  \n",
              "2            inactive  367.470  4.71860         2.0            4.0  3.924453  \n",
              "3            inactive  326.417  4.88550         1.0            4.0  4.283997  \n",
              "4            inactive  248.347  3.40162         1.0            3.0  3.950782  \n",
              "..                ...      ...      ...         ...            ...       ...  \n",
              "127            active  364.422  4.54542         2.0            4.0  8.072117  \n",
              "128            active  264.302  2.23902         2.0            4.0  7.463442  \n",
              "129            active  307.327  1.88900         3.0            5.0  7.503070  \n",
              "130            active  282.364  3.98102         0.0            2.0  6.859806  \n",
              "131            active  312.346  2.73182         0.0            4.0  8.701147  \n",
              "\n",
              "[132 rows x 8 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>bioactivity_class</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>LogP</th>\n",
              "      <th>NumHDonors</th>\n",
              "      <th>NumHAcceptors</th>\n",
              "      <th>pIC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL123184</td>\n",
              "      <td>COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1</td>\n",
              "      <td>inactive</td>\n",
              "      <td>264.346</td>\n",
              "      <td>3.10180</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.301030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL122699</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>270.300</td>\n",
              "      <td>3.37140</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.580044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL124117</td>\n",
              "      <td>CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1</td>\n",
              "      <td>inactive</td>\n",
              "      <td>367.470</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.924453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL332364</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>326.417</td>\n",
              "      <td>4.88550</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.283997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL432983</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>248.347</td>\n",
              "      <td>3.40162</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.950782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>CHEMBL451059</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>364.422</td>\n",
              "      <td>4.54542</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.072117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>264.302</td>\n",
              "      <td>2.23902</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.463442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462376</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>307.327</td>\n",
              "      <td>1.88900</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.503070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462553</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>282.364</td>\n",
              "      <td>3.98102</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.859806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>CHEMBL454282</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>312.346</td>\n",
              "      <td>2.73182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.701147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>132 rows × 8 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Removing the 'intermediate' bioactivity class\n",
        "\n",
        "Here, we will be removing the intermediate class from our data set."
      ],
      "metadata": {
        "id": "dsCAW6DPILrq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_final = df_final[df_final.bioactivity_class != 'intermediate']\n",
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "VULtHYTJ_itS",
        "outputId": "9364293c-21f2-4183-d527-f8f2c02314af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                  canonical_smiles  \\\n",
              "0         CHEMBL123184                  COc1cccc(CC2(C)SC(O)=C(C)C2=O)c1   \n",
              "1         CHEMBL122699                CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O   \n",
              "2         CHEMBL124117  CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1   \n",
              "3         CHEMBL332364           CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O   \n",
              "4         CHEMBL432983                   CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O   \n",
              "..                 ...                                               ...   \n",
              "127       CHEMBL451059      Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1   \n",
              "128       CHEMBL462375                  Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "129       CHEMBL462376            CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462553                Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "131       CHEMBL454282         Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "    bioactivity_class    MolWt     LogP  NumHDonors  NumHAcceptors     pIC50  \n",
              "0            inactive  264.346  3.10180         1.0            4.0  3.301030  \n",
              "1            inactive  270.300  3.37140         1.0            3.0  3.580044  \n",
              "2            inactive  367.470  4.71860         2.0            4.0  3.924453  \n",
              "3            inactive  326.417  4.88550         1.0            4.0  4.283997  \n",
              "4            inactive  248.347  3.40162         1.0            3.0  3.950782  \n",
              "..                ...      ...      ...         ...            ...       ...  \n",
              "127            active  364.422  4.54542         2.0            4.0  8.072117  \n",
              "128            active  264.302  2.23902         2.0            4.0  7.463442  \n",
              "129            active  307.327  1.88900         3.0            5.0  7.503070  \n",
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              "131            active  312.346  2.73182         0.0            4.0  8.701147  \n",
              "\n",
              "[128 rows x 8 columns]"
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>CHEMBL122699</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cc(F)cc(F)c2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>270.300</td>\n",
              "      <td>3.37140</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.580044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL124117</td>\n",
              "      <td>CC(=O)Nc1cccc(-c2ccc(CC3(C)SC(O)=C(C)C3=O)cc2)c1</td>\n",
              "      <td>inactive</td>\n",
              "      <td>367.470</td>\n",
              "      <td>4.71860</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.924453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL332364</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2cccc(Oc3ccccc3)c2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>326.417</td>\n",
              "      <td>4.88550</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.283997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL432983</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(C)cc2)SC1=O</td>\n",
              "      <td>inactive</td>\n",
              "      <td>248.347</td>\n",
              "      <td>3.40162</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.950782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>CHEMBL451059</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>364.422</td>\n",
              "      <td>4.54542</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.072117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>264.302</td>\n",
              "      <td>2.23902</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.463442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462376</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>307.327</td>\n",
              "      <td>1.88900</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.503070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462553</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>282.364</td>\n",
              "      <td>3.98102</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.859806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>CHEMBL454282</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>312.346</td>\n",
              "      <td>2.73182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.701147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>128 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55ec285a-4b55-40fe-b41a-41d527c9f479')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-55ec285a-4b55-40fe-b41a-41d527c9f479 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-55ec285a-4b55-40fe-b41a-41d527c9f479');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 3)Exploratory Data Analysis (Chemical Space Analysis) via Lipinski descriptors"
      ],
      "metadata": {
        "id": "6hU4wXw7IVCo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Import library"
      ],
      "metadata": {
        "id": "godpQiVdIqDp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set(style='ticks')\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "7xuGmeF9bfBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Frequency plot of the 2 bioactivity classes"
      ],
      "metadata": {
        "id": "SO5FFiSkJMqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.countplot(x='bioactivity_class', data=df_final, edgecolor='black')\n",
        "\n",
        "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.savefig('plot_bioactivity_class.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "Q8wQnUmRbgsF",
        "outputId": "0c3afb08-4ae7-40b5-e8aa-ea17be320c6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 2. Bar graph depicting number of active and inactive labeled compounds"
      ],
      "metadata": {
        "id": "XRHSUVtlEOUJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Scatter plot of MW versus LogP"
      ],
      "metadata": {
        "id": "QsdrTA7zJYRL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This graph shows the range of values for different discriptors seen in active and inactive compounds. Both compounds span similar chemical spaces. Higher the pIC50, lower is the IC50 value, which can be seen in active compounds that are represented by bigger siize bubbles compared to inactive compounds. Most of the active compounds occupy the first quadrant (bottom left area) in the graph which is expected since low molecular weight and low logP is typical for bioactive compounds.\n",
        "\n",
        "The following bar graphs analysing individual discriptors further emphasize this."
      ],
      "metadata": {
        "id": "d9h6lRxoKzNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.scatterplot(x='MolWt', y='LogP', data=df_final, hue='bioactivity_class', size='pIC50', edgecolor='black', alpha=0.7)\n",
        "\n",
        "plt.xlabel('MW', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
        "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)\n",
        "plt.savefig('plot_MW_vs_LogP.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "BIC2Q1PAblM3",
        "outputId": "82b6e7e4-58d8-4627-e5d3-f5731327e199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 3. Scatter plot depicting the molecular discriptor values of active and inactive compounds."
      ],
      "metadata": {
        "id": "pQl2kgwnFJUP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## pIC50 Box Plots"
      ],
      "metadata": {
        "id": "JGoxfqQBK0-L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.boxplot(x = 'bioactivity_class', y = 'pIC50', data = df_final)\n",
        "\n",
        "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('pIC50 value', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.savefig('plot_ic50.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "FoaJRCpFbmk_",
        "outputId": "7fd3c344-639e-4d8a-b56e-e6093d8c4df3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 4. Bar graph representing difference in IC50 values in active vs. inactive classes"
      ],
      "metadata": {
        "id": "7RTypAyGGqKv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Statistical analysis | Mann-Whitney U Test"
      ],
      "metadata": {
        "id": "14ZfzLEKLE5e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mannwhitney(descriptor, verbose=False):\n",
        "  # reference - https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n",
        "  from numpy.random import seed\n",
        "  from numpy.random import randn\n",
        "  from scipy.stats import mannwhitneyu\n",
        "\n",
        "# seed the random number generator\n",
        "  seed(1)\n",
        "\n",
        "# actives and inactives\n",
        "  selection = [descriptor, 'bioactivity_class']\n",
        "  df = df_final[selection]\n",
        "  active = df[df.bioactivity_class == 'active']\n",
        "  active = active[descriptor]\n",
        "\n",
        "  selection = [descriptor, 'bioactivity_class']\n",
        "  df = df_final[selection]\n",
        "  inactive = df[df.bioactivity_class == 'inactive']\n",
        "  inactive = inactive[descriptor]\n",
        "\n",
        "# compare samples\n",
        "  stat, p = mannwhitneyu(active, inactive)\n",
        "  #print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
        "\n",
        "# interpret\n",
        "  alpha = 0.05\n",
        "  if p > alpha:\n",
        "    interpretation = 'Same distribution (fail to reject H0)'\n",
        "  else:\n",
        "    interpretation = 'Different distribution (reject H0)'\n",
        "  \n",
        "  results = pd.DataFrame({'Descriptor':descriptor,\n",
        "                          'Statistics':stat,\n",
        "                          'p':p,\n",
        "                          'alpha':alpha,\n",
        "                          'Interpretation':interpretation}, index=[0])\n",
        "  filename = 'mannwhitneyu_' + descriptor + '.csv'\n",
        "  results.to_csv(filename)\n",
        "\n",
        "  return results"
      ],
      "metadata": {
        "id": "SnRtspwWbuJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mannwhitney('pIC50')  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "L2fhlNMLbsNE",
        "outputId": "9f554549-12bb-44f3-afb2-3ff26f33b96c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Descriptor  Statistics             p  alpha  \\\n",
              "0      pIC50      1547.0  1.860713e-09   0.05   \n",
              "\n",
              "                       Interpretation  \n",
              "0  Different distribution (reject H0)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1f9b893-30a5-4434-b5c4-08e8bc44c428\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pIC50</td>\n",
              "      <td>1547.0</td>\n",
              "      <td>1.860713e-09</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1f9b893-30a5-4434-b5c4-08e8bc44c428')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f1f9b893-30a5-4434-b5c4-08e8bc44c428 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f1f9b893-30a5-4434-b5c4-08e8bc44c428');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p value is lesser than 0.05, reject null hypothesis. Activity in compounds is statistically significant"
      ],
      "metadata": {
        "id": "4wYWc0ctHi2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.boxplot(x = 'bioactivity_class', y = 'LogP', data = df_final)\n",
        "\n",
        "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.savefig('plot_LogP.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "krv9wv0wehQ7",
        "outputId": "d6fac073-075b-4d48-ff27-e4728d58e989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 5. Bar graph representing difference in LogP values in active vs. inactive classes"
      ],
      "metadata": {
        "id": "nwMpN_nUHwxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mannwhitney('LogP')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "tkYcNN1be9ZH",
        "outputId": "0b7cfd22-cc20-4366-b516-3638ece90ab6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Descriptor  Statistics         p  alpha                      Interpretation\n",
              "0       LogP       479.0  0.015004   0.05  Different distribution (reject H0)"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogP</td>\n",
              "      <td>479.0</td>\n",
              "      <td>0.015004</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73cfa87f-8cb8-44a7-8265-9901108dc8d1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-73cfa87f-8cb8-44a7-8265-9901108dc8d1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-73cfa87f-8cb8-44a7-8265-9901108dc8d1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p value is lesser than 0.05, reject null hypothesis. Difference in LogP of active vs inactive compounds is statistically significant"
      ],
      "metadata": {
        "id": "VXO-xPKvH5af"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.boxplot(x = 'bioactivity_class', y = 'NumHDonors', data = df_final)\n",
        "\n",
        "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('NumHDonors', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.savefig('plot_NumHDonors.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "luDJXCu7frtP",
        "outputId": "c691cea8-f132-42f6-a10d-4c1bdb2058d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 6. Bar graph representing difference in Number of H donors values in active vs. inactive classes"
      ],
      "metadata": {
        "id": "DmRa7UbSH1cz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mannwhitney('NumHDonors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "3pGz-6L5f2bZ",
        "outputId": "01b03b37-ac51-48bf-8337-859f6582f9dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Descriptor  Statistics             p  alpha  \\\n",
              "0  NumHDonors      1297.5  3.196225e-11   0.05   \n",
              "\n",
              "                       Interpretation  \n",
              "0  Different distribution (reject H0)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef99fb42-c4e1-4121-a69a-84c5e3a2e418\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NumHDonors</td>\n",
              "      <td>1297.5</td>\n",
              "      <td>3.196225e-11</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef99fb42-c4e1-4121-a69a-84c5e3a2e418')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef99fb42-c4e1-4121-a69a-84c5e3a2e418 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef99fb42-c4e1-4121-a69a-84c5e3a2e418');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p value is lesser than 0.05, reject null hypothesis. Difference in Number of H donors in active vs inactive compounds is statistically significant"
      ],
      "metadata": {
        "id": "Z49uZG0cIO72"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5.5, 5.5))\n",
        "\n",
        "sns.boxplot(x = 'bioactivity_class', y = 'NumHAcceptors', data = df_final)\n",
        "\n",
        "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('NumHAcceptors', fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.savefig('plot_NumHAcceptors.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "9gt8nDJif4ZY",
        "outputId": "6e08351f-2a30-42ae-ab48-6477162d93f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Img 7. Bar graph representing difference in Number of H acceptors values in active vs. inactive classes"
      ],
      "metadata": {
        "id": "082tDtGJIZyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mannwhitney('NumHAcceptors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "IGRLxYHtf6aW",
        "outputId": "fc9db3fc-a034-433b-ed14-b19653b14f39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Descriptor  Statistics         p  alpha  \\\n",
              "0  NumHAcceptors      1280.0  0.000003   0.05   \n",
              "\n",
              "                       Interpretation  \n",
              "0  Different distribution (reject H0)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17296521-f316-4cdf-a31e-206cf81069a5\">\n",
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              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NumHAcceptors</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
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          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p value is lesser than 0.05, reject null hypothesis. Difference in Number of H acceptors in active vs inactive compounds is statistically significant"
      ],
      "metadata": {
        "id": "fV3-Y0oqIiR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df_final.loc[df.bioactivity_class == \"active\"]\n",
        "df_new.to_csv(r'active_compounds.csv', index=False)\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "jzFq6nZ1nj4v",
        "outputId": "a0fe27bf-5513-4ce0-cc1a-566af02d4b5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                              canonical_smiles  \\\n",
              "118       CHEMBL400279   CC1=C(O)C(C)(Cc2ccc(-c3cccc(Cl)c3)cc2)SC1=O   \n",
              "119       CHEMBL509952        Cc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1   \n",
              "120       CHEMBL491752                  CS(=O)(=O)c1cc(O)c2ccccc2c1O   \n",
              "121       CHEMBL455414           O=C(O)CCS(=O)(=O)c1cc(O)c2ccccc2c1O   \n",
              "122       CHEMBL462205           O=S(=O)(c1ccccc1)c1cc(O)c2ccccc2c1O   \n",
              "123       CHEMBL512926  CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1   \n",
              "124       CHEMBL460104       O=S(=O)(c1ccc(Cl)cc1)c1cc(O)c2ccccc2c1O   \n",
              "125       CHEMBL445555      CCCc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1   \n",
              "126       CHEMBL462374     O=S(=O)(c1ccc2ccccc2c1)c1cc(O)c2ccccc2c1O   \n",
              "127       CHEMBL451059  Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1   \n",
              "128       CHEMBL462375              Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "129       CHEMBL462376        CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1   \n",
              "130       CHEMBL462553            Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1   \n",
              "131       CHEMBL454282     Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1   \n",
              "\n",
              "    bioactivity_class    MolWt     LogP  NumHDonors  NumHAcceptors     pIC50  \n",
              "118            active  344.863  5.41360         1.0            3.0  3.806875  \n",
              "119            active  314.362  3.39222         2.0            4.0  9.214670  \n",
              "120            active  238.264  1.65450         2.0            4.0  8.452225  \n",
              "121            active  296.300  1.49940         3.0            5.0  8.452225  \n",
              "122            active  300.335  3.08380         2.0            4.0  8.546682  \n",
              "123            active  357.387  3.04220         3.0            5.0  8.424812  \n",
              "124            active  334.780  3.73720         2.0            4.0  8.442493  \n",
              "125            active  342.416  4.03630         2.0            4.0  8.510042  \n",
              "126            active  350.395  4.23700         2.0            4.0  8.826814  \n",
              "127            active  364.422  4.54542         2.0            4.0  8.072117  \n",
              "128            active  264.302  2.23902         2.0            4.0  7.463442  \n",
              "129            active  307.327  1.88900         3.0            5.0  7.503070  \n",
              "130            active  282.364  3.98102         0.0            2.0  6.859806  \n",
              "131            active  312.346  2.73182         0.0            4.0  8.701147  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>bioactivity_class</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>LogP</th>\n",
              "      <th>NumHDonors</th>\n",
              "      <th>NumHAcceptors</th>\n",
              "      <th>pIC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>CHEMBL400279</td>\n",
              "      <td>CC1=C(O)C(C)(Cc2ccc(-c3cccc(Cl)c3)cc2)SC1=O</td>\n",
              "      <td>active</td>\n",
              "      <td>344.863</td>\n",
              "      <td>5.41360</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.806875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>CHEMBL509952</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>314.362</td>\n",
              "      <td>3.39222</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.214670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>CHEMBL491752</td>\n",
              "      <td>CS(=O)(=O)c1cc(O)c2ccccc2c1O</td>\n",
              "      <td>active</td>\n",
              "      <td>238.264</td>\n",
              "      <td>1.65450</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.452225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>CHEMBL455414</td>\n",
              "      <td>O=C(O)CCS(=O)(=O)c1cc(O)c2ccccc2c1O</td>\n",
              "      <td>active</td>\n",
              "      <td>296.300</td>\n",
              "      <td>1.49940</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.452225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>CHEMBL462205</td>\n",
              "      <td>O=S(=O)(c1ccccc1)c1cc(O)c2ccccc2c1O</td>\n",
              "      <td>active</td>\n",
              "      <td>300.335</td>\n",
              "      <td>3.08380</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.546682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>CHEMBL512926</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>357.387</td>\n",
              "      <td>3.04220</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.424812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>CHEMBL460104</td>\n",
              "      <td>O=S(=O)(c1ccc(Cl)cc1)c1cc(O)c2ccccc2c1O</td>\n",
              "      <td>active</td>\n",
              "      <td>334.780</td>\n",
              "      <td>3.73720</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.442493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>CHEMBL445555</td>\n",
              "      <td>CCCc1ccc(S(=O)(=O)c2cc(O)c3ccccc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>342.416</td>\n",
              "      <td>4.03630</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.510042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>CHEMBL462374</td>\n",
              "      <td>O=S(=O)(c1ccc2ccccc2c1)c1cc(O)c2ccccc2c1O</td>\n",
              "      <td>active</td>\n",
              "      <td>350.395</td>\n",
              "      <td>4.23700</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.826814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>CHEMBL451059</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)c3cc4ccccc4cc3c2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>364.422</td>\n",
              "      <td>4.54542</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.072117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>CHEMBL462375</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>264.302</td>\n",
              "      <td>2.23902</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.463442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>CHEMBL462376</td>\n",
              "      <td>CC(=O)Nc1ccc(S(=O)(=O)c2cc(O)ccc2O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>307.327</td>\n",
              "      <td>1.88900</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.503070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>CHEMBL462553</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)c2ccc3ccccc3c2)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>282.364</td>\n",
              "      <td>3.98102</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.859806</td>\n",
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              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>CHEMBL454282</td>\n",
              "      <td>Cc1ccc(S(=O)(=O)C2=CC(=O)c3ccccc3C2=O)cc1</td>\n",
              "      <td>active</td>\n",
              "      <td>312.346</td>\n",
              "      <td>2.73182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Discussion and Conclusion\n",
        "\n",
        "###The above discribed bioinformatic tool is built to identify bioactive compounds using molecular discriptors from the ChEMBL database. We perform a Q-SAR (Quantitative structure activity relationship) analysis on our protein target of interest from the pathogenic bacterium *Mycobacterium tuberculosis.* The Lipinski rule of 5, which is a gold standard for determining the pharmacokinetic properties of drugs acts as a guide to establishing criteria that a compound needs to meet to be biologically active.\n",
        "\n",
        "###Our target protein, 3-oxoacyl-[acyl-carrier-protein] synthase III is critical in the mycolic acid synthesis pathway and its protein crystal structure has also been determined. Here we report 14 compounds that have the required pharmacokinetic properties to be perform well as potential drugs. First a cohort of all possible compounds having activity with our target protein was determined using the ChEMBL web source client. Then data was pre-processed by dropping missing values and classifying drugs into active, inactive and intermediate bioactivity based on the IC50 values also obtained from the ChEMBL database. The lipinksi discriptors were collected for all the drug molecules and dataset was cleaned by only keeping the active and inactive compounds to make analysis easier. Following this exploratory data analysis was performed on the discriptors and data was analysed using box plots and scatter plots depicting information about the discriptors corresponding to the drugs. As expected active compounds had lower molecular weights, LogP values and higher pIC50 compared to their inactive counterparts. the Mann-Whitney U statistical test was used to determine statistical significance of data. This test is useful in small datasets with data that are continous, non-normal and independent of each other as seen with our dataset (McKnight, P. E., & Najab, J. (2010). Mann‐Whitney U Test. The Corsini encyclopedia of psychology, 1-1.). Since the p value for all the tests are very low (<<0.05) we can reject null hypothesis safetly assume that active compounds show significant bioactive properties compared to inactive labeled compounds. These tests validate our data since active compounds already had the expected pharmacokinetic signature desirable for active compounds.\n",
        "\n",
        "###Any protein target from a model organism present on the ChEMBL database can be added as an input to identify target bioactive compounds. In our case 3-oxoacyl-[acyl-carrier-protein] synthase III is just one of the hundreds of TB target proteins that can be analysed. This makes the above code multifunctional, adaptable and reliable since it also checks whether bioactivity is statistically significant. Shortlisted compounds can further be analysed; since the smiles notation is included in the output dataset it can be used to further determine the PADEL discriptors and molecular fingerprints of the molecules. This collection of data can then be used to build in a random forest regression model to be able to predict activity in a compound based on its discriptors. This would be the future scope of the project. Several other bioinformatic (molecular docking studies)and in-vitro (drug screen assays) experiemnts can be done with this shortlist of compounds, making this a compelling tool to begin identifying potential therapeutic targets."
      ],
      "metadata": {
        "id": "cABJWkoPIudW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Acknowledgment\n",
        "\n",
        "###I would like to thank Dr. Chanin Nantasenamat for providing the code backbone to run this project."
      ],
      "metadata": {
        "id": "wXEeO4P8a5m8"
      }
    }
  ]
}