Similarity search in ChEMBL, DrugBank and UNPD

Given a molecule, this model for its 100 nearest neighbors, according to ECFP4 Tanimoto similarity, in the medicinal chemistry database ChEMBL17_DrugBank17_UNPD17. This combined database contains all the compounds from the three collections (DrugBank, ChEMBL22 and Universal natural product directory (UNPD)) with up to 17 heavy atoms. It features a total of 128k compounds. The whole ChEMBL17_DrugBank17_UNPD17 database is not downloaded with the model, by using it you post queries to an online server external to Ersilia.

This model was incorporated on 2022-08-18.

Information

Identifiers

• Ersilia Identifier: eos9c7k
• Slug: medchem17-similarity

Domain

• Task: Sampling
• Subtask: Similarity search
• Biomedical Area: Any
• Target Organism: Not Applicable
• Tags: Similarity, ChEMBL, DrugBank

Input

• Input: Compound
• Input Dimension: 1

Output

• Output Dimension: 100
• Output Consistency: Fixed
• Interpretation: List of 100 nearest neighbors

Below are the Output Columns of the model:

Name	Type	Direction	Description
smiles_00	string		Sampled smiles 0 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_01	string		Sampled smiles 1 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_02	string		Sampled smiles 2 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_03	string		Sampled smiles 3 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_04	string		Sampled smiles 4 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_05	string		Sampled smiles 5 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_06	string		Sampled smiles 6 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_07	string		Sampled smiles 7 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_08	string		Sampled smiles 8 from a similarity search in ChEMBL17_DrugBank17_UNPD17
smiles_09	string		Sampled smiles 9 from a similarity search in ChEMBL17_DrugBank17_UNPD17

10 of 100 columns are shown

Source and Deployment

• Source: Online
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9c7k
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9c7k.zip

Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 248
• Image Size (Mb): 210.25

Computational Performance (seconds):

• 4 inputs: 67.05
• 20 inputs: 201.93
• 100 inputs: 916.69

References

• Source Code: https://gdb-medchem-simsearch.gdb.tools/
• Publication: https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.201900031
• Publication Type: Peer reviewed
• Publication Year: 2019
• Ersilia Contributor: Amna-28

License

This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a None license.

Notice: Ersilia grants access to models as is, directly from the original authors, please refer to the original code repository and/or publication if you use the model in your research.

Use

To use this model locally, you need to have the Ersilia CLI installed. The model can be fetched using the following command:

# fetch model from the Ersilia Model Hub
ersilia fetch eos9c7k

Then, you can serve, run and close the model as follows:

# serve the model
ersilia serve eos9c7k
# generate an example file
ersilia example -n 3 -f my_input.csv
# run the model
ersilia run -i my_input.csv -o my_output.csv
# close the model
ersilia close

About Ersilia

The Ersilia Open Source Initiative is a tech non-profit organization fueling sustainable research in the Global South. Please cite the Ersilia Model Hub if you've found this model to be useful. Always let us know if you experience any issues while trying to run it. If you want to contribute to our mission, consider donating to Ersilia!