ersiliaos/eos1vms repository overview

⁠Multi-target prediction based on ChEMBL data

This is a ligand-based target prediction model developed by the ChEMBL team. They trained the model using pairs of small molecules and their protein targets, and produced a multitask predictor. The thresholds of activity where determined by protein families (kinases: <= 30nM, GPCRs: <= 100nM, Nuclear Receptors: <= 100nM, Ion Channels: <= 10μM, Non-IDG Family Targets: <= 1μM). Here we provide the model trained on ChEMBL_28, which showed an accuracy of 85%.

This model was incorporated on 2021-05-07.Last packaged on 2026-03-09.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos1vms
• Slug: chembl-multitask-descriptor

⁠Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Bioactivity profile, Target identification, ChEMBL

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 616
• Output Consistency: Fixed
• Interpretation: Probability of having the protein (identified by ChEMBL ID), as target

Below are the Output Columns of the model:

10 of 616 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos1vms⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos1vms.zip⁠

⁠Resource Consumption

• Model Size (Mb): 27
• Environment Size (Mb): 593
• Image Size (Mb): 667.16

Computational Performance (seconds):

• 10 inputs: 34.36
• 100 inputs: 22.79
• 10000 inputs: 100.53

⁠References

• Source Code: https://github.com/chembl/chembl_multitask_model/⁠
• Publication: http://chembl.blogspot.com/2019/05/multi-task-neural-network-on-chembl.html⁠
• Publication Type: Other
• Publication Year: 2019
• Ersilia Contributor: miquelduranfrigola⁠

⁠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 eos1vms

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

# serve the model
ersilia serve eos1vms
# 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!