ersiliaos/eos9p4a repository overview

⁠Drug-likeness scoring based on unsupervised learning

This model evaluates drug-likeness using an unsupervised learning approach, eliminating the need for labeled data and avoiding biases from incomplete negative sets. It extracts features directly from known drug molecules using a recurrent neural network (RNN) language model on SMILES strings, learning the probability distribution of known drugs. New molecules are scored based on their likelihood of belonging to this space, offering a robust evaluation of drug-likeness that outperforms supervised models like TCC across diverse datasets.

This model was incorporated on 2024-09-04.Last packaged on 2026-04-23.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9p4a
• Slug: deep-dl

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Drug-likeness

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Higher score indicates higher drug likeness

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9p4a⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9p4a.zip⁠

⁠Resource Consumption

• Model Size (Mb): 187
• Environment Size (Mb): 1266
• Image Size (Mb): 1496.28

Computational Performance (seconds):

• 10 inputs: 32.71
• 100 inputs: 35.65
• 10000 inputs: 517.14

⁠References

• Source Code: https://github.com/SeonghwanSeo/DeepDL⁠
• Publication: https://pubs.rsc.org/en/content/articlehtml/2022/sc/d1sc05248a⁠
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: miquelduranfrigola⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a GPL-3.0-or-later⁠ 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 eos9p4a

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

# serve the model
ersilia serve eos9p4a
# 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!