ersiliaos/eos2ta5 repository overview

⁠Ligand-based prediction of hERG blockade

Predicts blockade of the hERG potassium channel, the cardiac liability most often responsible for late-stage attrition. CardioTox net stacks five deep learning base models, each on a different molecular representation, under a neural network that combines their outputs, an arrangement Karim and colleagues found more robust than any single model. Training used 12,620 compounds from BindingDB, ChEMBL and three further sources, labelled as blockers below an IC50 of 10 uM, with evaluation weighted towards compounds unlike that training set.

This model was incorporated on 2021-10-18. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos2ta5
• Slug: cardiotoxnet-herg

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: ADMET
• Target Organism: Homo sapiens
• Tags: hERG, Toxicity, Cardiotoxicity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability that a compound blocks the hERG potassium channel, with blockers defined by IC50 under 10 uM.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos2ta5⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2ta5.zip⁠

⁠Resource Consumption

• Model Size (Mb): 415
• Environment Size (Mb): 1566
• Image Size (Mb): 1692.43

Computational Performance (seconds):

• 10 inputs: 27.21
• 100 inputs: 30.9
• 10000 inputs: 952.53

⁠References

• Source Code: https://github.com/Abdulk084/CardioTox⁠
• Publication: https://doi.org/10.1186/s13321-021-00541-z⁠
• Publication Type: Peer reviewed
• Publication Year: 2021
• Ersilia Contributor: miquelduranfrigola⁠

⁠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 eos2ta5

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

# serve the model
ersilia serve eos2ta5
# 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!