ersiliaos/eos19dk repository overview

⁠MolCompass Chemical Space Projection

Projects any molecule, given as a SMILES string, onto a 2D map of chemical space using a pre-trained parametric t-SNE neural network. The projection is deterministic: structurally similar compounds consistently cluster together, based on 2048-bit ECFP fingerprints (radius=3) and a network trained on 1.56 million ChEMBL v.23 structures. Useful for exploring chemical space, checking a QSAR/QSPR models applicability domain, and spotting model cliffs where structurally similar compounds get inconsistent predictions.

This model was incorporated on 2026-08-06.Last packaged on 2026-08-06.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos19dk
• Slug: molcompass

⁠Domain

• Task: Representation
• Subtask: Projection
• Biomedical Area: Any
• Target Organism: Any
• Tags: Embedding, Fingerprint, Similarity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 2
• Output Consistency: Fixed
• Interpretation: X and Y coordinates locating the molecule within the pretrained parametric t-SNE chemical-space map.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos19dk⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos19dk.zip⁠

⁠Resource Consumption

• Model Size (Mb): 17
• Environment Size (Mb): 510
• Image Size (Mb): 570.39

Computational Performance (seconds):

• 10 inputs: 28.82
• 100 inputs: 21.71
• 10000 inputs: 52.8

⁠References

• Source Code: https://github.com/sergsb/molcomplib⁠
• Publication: https://doi.org/10.1186/s13321-024-00888-z⁠
• Publication Type: Peer reviewed
• Publication Year: 2024
• Ersilia Contributor: arnaucoma24⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a MIT⁠ 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 eos19dk

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

# serve the model
ersilia serve eos19dk
# 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!