ersiliaos/eos93h2 repository overview

⁠ImageMol GPCR ligand binding affinity

Scores a molecule against ten G protein-coupled receptors chosen for having the largest ligand sets reported in ChEMBL, covering serotonin, adenosine, dopamine, cannabinoid, histamine and opioid targets, each modelled as a separate regression rather than a classification. The encoder is ImageMol, which learns from rendered pictures of chemical structures and was pretrained on ten million unlabelled molecules. Ersilia carried out the per-receptor fine-tuning, since the authors published the ChEMBL benchmark but not these weights. Coverage mirrors the ligand bias of heavily studied receptors.

This model was incorporated on 2023-01-25. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos93h2
• Slug: image-mol-gpcr

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Any
• Target Organism: Homo sapiens
• Tags: Target identification, GPCR

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 10
• Output Consistency: Fixed
• Interpretation: Predicted binding affinity on a pKi-like scale across ten GPCR targets, higher values indicating stronger binding.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos93h2⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos93h2.zip⁠

⁠Resource Consumption

• Model Size (Mb): 428
• Environment Size (Mb): 1199
• Image Size (Mb): 1579.7

Computational Performance (seconds):

• 10 inputs: 26.03
• 100 inputs: 43.09
• 10000 inputs: 1574.24

⁠References

• Source Code: https://github.com/HongxinXiang/ImageMol⁠
• Publication: https://doi.org/10.1038/s42256-022-00557-6⁠
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: DhanshreeA⁠

⁠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 eos93h2

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

# serve the model
ersilia serve eos93h2
# 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!