ersiliaos/eos7jlv repository overview

⁠GDBMedChem similarity search

Returns the 100 closest neighbours of a query in GDBMedChem, ten million molecules distilled from the 166.4 billion structures of GDB17 by stripping problematic functional groups and overly complex scaffolds, then sampling evenly across size, stereochemistry and polarity. Awale and colleagues report lower complexity and better synthetic accessibility than GDB17 while keeping a higher sp3 fraction and natural product likeness than known drugs. Neighbours are ranked by ECFP4 similarity using approximate search on the authors public server, and are enumerated rather than purchasable compounds.

This model was incorporated on 2022-08-18.Last packaged on 2026-10-15.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos7jlv
• Slug: gdbmedchem-similarity

⁠Domain

• Task: Sampling
• Subtask: Similarity search
• Biomedical Area: Any
• Target Organism: Any
• Tags: Similarity, ChEMBL

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 100
• Output Consistency: Fixed
• Interpretation: 100 nearest neighbours in the ten-million-molecule GDBMedChem library, ranked by ECFP4 similarity.

Below are the Output Columns of the model:

10 of 100 columns are shown

⁠Source and Deployment

• Source: Online
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos7jlv⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7jlv.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 316
• Image Size (Mb): 307.84

Computational Performance (seconds):

• 10 inputs: 33.91
• 100 inputs: 457.17
• 10000 inputs: -1

⁠References

• Source Code: https://gdb-medchem-simsearch.gdb.tools/⁠
• Publication: https://doi.org/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 eos7jlv

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

# serve the model
ersilia serve eos7jlv
# 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!