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Update 2024/1
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whitead authored Aug 20, 2024
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25. Amirkulova, D. B. & White, A. D. Recent advances in maximum entropy biasing techniques for molecular dynamics. Molecular Simulation 45, 1285–1294 (2019).

26. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019).
26. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020).

27. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020).
27. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. D. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, (2020).

28. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. D. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, (2020).
28. Li, Z., Wellawatte, G. P., Chakraborty, M., Gandhi, H. A., Xu, C. & White, A. D. Graph neural network based coarse-grained mapping prediction. Chemical science 11, 9524–9531 (2020).

29. Li, Z., Wellawatte, G. P., Chakraborty, M., Gandhi, H. A., Xu, C. & White, A. D. Graph neural network based coarse-grained mapping prediction. Chemical science 11, 9524–9531 (2020).
29. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020).

30. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020).
30. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020).

31. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020).
31. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020).

32. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020).
32. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020).

33. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020).
33. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020).

34. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020).
34. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022).

35. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022).
35. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021).

36. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021).
36. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021).

37. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021).
37. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022).

38. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022).
38. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. AIChE Journal 68, e17695 (2022).

39. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. AIChE Journal 68, e17695 (2022).
39. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022).

40. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022).
40. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. Physical Review E 106, 014306 (2022).

41. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. Physical Review E 106, 014306 (2022).
41. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science Advances 8, eabj0305 (2022).

42. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science Advances 8, eabj0305 (2022).
42. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. Patterns 3, (2022).

43. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. Patterns 3, (2022).
43. Cox, S. & White, A. D. Symmetric molecular dynamics. Journal of Chemical Theory and Computation 18, 4077–4081 (2022).

44. Cox, S. & White, A. D. Symmetric molecular dynamics. Journal of Chemical Theory and Computation 18, 4077–4081 (2022).
44. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022).

45. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022).
45. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. Journal of Chemical Information and Modeling 63, 2546–2553 (2023).

46. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. Journal of Chemical Information and Modeling 63, 2546–2553 (2023).
46. Zhu, W., Luo, J. & White, A. D. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 100521 (2022).

47. Zhu, W., Luo, J. & White, A. D. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 100521 (2022).
47. White, A. D. Deep learning for molecules and materials. Living journal of computational molecular science 3, (2022).

48. White, A. D. Deep learning for molecules and materials. Living Journal of Computational Molecular Science 3, 1499–1499 (2021).
48. Yang, Z., Milas, K. A. & White, A. D. Now what sequence? Pre-trained ensembles for bayesian optimization of protein sequences. bioRxiv 2022–08 (2022).

49. Yang, Z., Milas, K. A. & White, A. D. Now what sequence? Pre-trained ensembles for bayesian optimization of protein sequences. bioRxiv 2022–08 (2022).
49. Gandhi, H. A. & White, A. D. Explaining molecular properties with natural language. (2022).

50. Gandhi, H. A. & White, A. D. Explaining molecular properties with natural language. (2022).
50. Seshadri, A., Gandhi, H. A., Wellawatte, G. P. & White, A. D. Why does that molecule smell? (2022).

51. Seshadri, A., Gandhi, H. A., Wellawatte, G. P. & White, A. D. Why does that molecule smell? (2022).
51. Wellawatte, G. P., Gandhi, H. A., Seshadri, A. & White, A. D. A perspective on explanations of molecular prediction models. Journal of Chemical Theory and Computation 19, 2149–2160 (2023).

52. Wellawatte, G. P., Gandhi, H. A., Seshadri, A. & White, A. D. A perspective on explanations of molecular prediction models. Journal of Chemical Theory and Computation 19, 2149–2160 (2023).
52. White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2, 368–376 (2023).

53. White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2, 368–376 (2023).
53. Wellawatte, G. P., Hocky, G. M. & White, A. D. Neural potentials of proteins extrapolate beyond training data. The Journal of Chemical Physics 159, (2023).

54. Wellawatte, G. P., Hocky, G. M. & White, A. D. Neural potentials of proteins extrapolate beyond training data. (2023).
54. Bran, A. M., Cox, S., Schilter, O., Baldassari, C., White, A. D. & Schwaller, P. ChemCrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376 (2023).

55. Bran, A. M., Cox, S., White, A. D. & Schwaller, P. ChemCrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376 (2023).
55. Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023).

56. Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023).
56. Medina, J. & White, A. D. Bloom filters for molecules. Journal of Cheminformatics 15, 95 (2023).

57. Medina, J. & White, A. D. Bloom filters for molecules. arXiv preprint arXiv:2304.05386 (2023).
57. Campbell, Q. L., Herington, J. & White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 (2023).

58. Lo, A., Pollice, R., Nigam, A., White, A. D., Krenn, M. & Aspuru-Guzik, A. Recent advances in the self-referencing embedding strings (SELFIES) library. arXiv preprint arXiv:2302.03620 (2023).
58. Medina, J. & White, A. D. Active learning in symbolic regression performance with physical constraints. arXiv preprint arXiv:2305.10379 (2023).

59. Campbell, Q. L., Herington, J. & White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 (2023).
59. Ansari, M. & White, A. D. Learning peptide properties with positive examples only. Digital Discovery 3, 977–986 (2024).

60. Medina, J. & White, A. D. Active learning in symbolic regression performance with physical constraints. arXiv preprint arXiv:2305.10379 (2023).
60. White, A. D. The future of chemistry is language. Nature Reviews Chemistry 7, 457–458 (2023).

61. Ansari, M. & White, A. D. Learning peptide properties with positive examples only. bioRxiv 2023–06 (2023).
61. Ramos, M. C. & White, A. D. Predicting small molecules solubility on endpoint devices using deep ensemble neural networks. Digital Discovery 3, 786–795 (2024).

62. White, A. D. The future of chemistry is language. Nature Reviews Chemistry 1–2 (2023).
62. Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon. Digital Discovery 2, 1233–1250 (2023).

63. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019).

64. Lo, A., Pollice, R., Nigam, A., White, A. D., Krenn, M. & Aspuru-Guzik, A. Recent advances in the self-referencing embedded strings (SELFIES) library. Digital Discovery 2, 897–908 (2023).

65. Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G. & White, A. D. Paperqa: Retrieval-augmented generative agent for scientific research. arXiv preprint arXiv:2312.07559 (2023).

66. Cox, S., Hammerling, M., Lála, J., Laurent, J., Rodriques, S., Rubashkin, M. & White, A. WikiCrow: Automating synthesis of human scientific knowledge (2024).

67. Ramos, M. C., Collison, C. J. & White, A. D. A review of large language models and autonomous agents in chemistry. arXiv preprint arXiv:2407.01603 (2024).

68. Laurent, J. M., Janizek, J. D., Ruzo, M., Hinks, M. M., Hammerling, M. J., Narayanan, S., Ponnapati, M., White, A. D. & Rodriques, S. G. LAB-bench: Measuring capabilities of language models for biology research. arXiv preprint arXiv:2407.10362 (2024).
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labbench:
key:
- laurent
- lab-bench
- 2024
tweet: https://x.com/andrewwhite01/status/1813235301973921873
link: https://doi.org/10.48550/arXiv.2407.10362
pdf: https://arxiv.org/pdf/2407.10362
code: https://huggingface.co/datasets/futurehouse/lab-bench

wikicrow:
key:
- cox
- hammerling
- wikicrow
- knowledge
- 2024
tweet: https://x.com/SGRodriques/status/1733193844110557432
link: https://www.futurehouse.org/wikicrow
code: https://github.com/Future-House/WikiCrow

llmhackathon2023:
key:
- llm
- hackathon
- 2023
tweet: https://x.com/kmjablonka/status/1668506349465116673
link: https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00113j
pdf: https://pubs.rsc.org/en/content/articlepdf/2023/dd/d3dd00113j

llmreview:
key:
- ramos
- collison
- autonomous
- review
- 2024
tweet: https://x.com/andrewwhite01/status/1808505284291375380
link: https://doi.org/10.48550/arXiv.2407.01603
pdf: https://arxiv.org/pdf/2407.01603
code: https://github.com/ur-whitelab/LLMs-in-science

deepensemble:
key:
- ramos
- solubility
- ensemble
- 2024
tweet: https://x.com/andrewwhite01/status/1780944395656744962
link: https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00217a
pdf: https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00217a
app: https://mol.dev/
code: https://github.com/ur-whitelab/mol.dev

paperqa:
key:
- paperqa
- 2023
tweet:
link: https://doi.org/10.48550/arXiv.2312.07559
pdf: https://arxiv.org/pdf/2312.07559
code: https://github.com/Future-House/paper-qa

alcfd:
key:
- ansari
Expand All @@ -20,7 +83,6 @@ serverless:
app: https://peptide.bio
code: https://github.com/ur-whitelab/peptide-dashboard


maxent:
key:
- ansari
Expand Down Expand Up @@ -311,7 +373,7 @@ nnff:
- 2023
- extrapolate
tweet: https://twitter.com/andrewwhite01/status/1605937402509492225
link: https://chemrxiv.org/engage/chemrxiv/article-details/63a3bfac81e4ba77ec56166a
link: https://pubs.aip.org/aip/jcp/article/159/8/085103/2908350
pdf: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/63a3bfac81e4ba77ec56166a/original/neural-potentials-of-proteins-extrapolate-beyond-training-data.pdf

alsr:
Expand Down Expand Up @@ -340,8 +402,8 @@ chemcrow:
- cox
- 2023
tweet: https://twitter.com/andrewwhite01/status/1670794000398184451
link: https://arxiv.org/abs/2304.05376
pdf: https://arxiv.org/pdf/2304.05376
link: https://www.nature.com/articles/s42256-024-00832-8
pdf: https://www.nature.com/articles/s42256-024-00832-8.pdf
code: https://github.com/ur-whitelab/chemcrow-public/
outputs: https://github.com/ur-whitelab/chemcrow-runs

Expand All @@ -355,15 +417,14 @@ chemfuture:
link: https://www.nature.com/articles/s41570-023-00502-0
pdf: https://www.nature.com/articles/s41570-023-00502-0.pdf


bloom:
key:
- bloom
- 2023
- medina
tweet: https://twitter.com/andrewwhite01/status/1646171800940969984
link: https://arxiv.org/abs/2304.05386
pdf: https://arxiv.org/pdf/2304.05386.pdf
link: https://link.springer.com/article/10.1186/s13321-023-00765-1%20
pdf: https://link.springer.com/content/pdf/10.1186/s13321-023-00765-1.pdf
code: https://github.com/whitead/molbloom

icl:
Expand All @@ -387,10 +448,10 @@ selfies-code:

pul:
key:
- 2023
- positive
- ansari
- 2024
tweet: https://twitter.com/andrewwhite01/status/1666104177360084995
link: https://www.biorxiv.org/content/10.1101/2023.06.01.543289v1.abstract
pdf: https://www.biorxiv.org/content/10.1101/2023.06.01.543289v1.full.pdf+html
link: https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00218g
pdf: https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00218g
code: https://github.com/ur-whitelab/pu-peptides
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