ersiliaos/eos4tcc repository overview

⁠BayeshERG: hERG channel blockade

Flags compounds likely to block the hERG potassium channel, the main cause of drug-induced QT prolongation and a routine preclinical hurdle. A Bayesian graph neural network with Monte Carlo dropout was pre-trained on 304,045 inhibition measurements and fine-tuned on 14,322 compounds labelled active at an IC50 of 10 uM or below, returning aleatoric and epistemic uncertainty alongside the score so that predictions on unfamiliar chemistry can be discounted. Of twelve high-scoring library compounds taken into automated patch-clamp, one blocked below 1 uM and three between 1 and 10 uM.

This model was incorporated on 2022-07-25.Last packaged on 2026-10-15.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos4tcc
• Slug: bayesherg

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 3
• Output Consistency: Variable
• Interpretation: Probability of hERG blockade at an IC50 cut-off of 10 uM, with aleatoric and epistemic uncertainty estimates.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos4tcc⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos4tcc.zip⁠

⁠Resource Consumption

• Model Size (Mb): 2
• Environment Size (Mb): 4065
• Image Size (Mb): 4035.25

Computational Performance (seconds):

• 10 inputs: 24.94
• 100 inputs: 19.68
• 10000 inputs: 347.57

⁠References

• Source Code: https://github.com/GIST-CSBL/BayeshERG⁠
• Publication: https://doi.org/10.1093/bib/bbac211⁠
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: azycn⁠

⁠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 eos4tcc

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

# serve the model
ersilia serve eos4tcc
# 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!