SARS-CoV inhibition

This model was developed to support the early efforts in the identification of novel drugs against SARS-CoV2. It predicts the probability that a small molecule inhibits SARS-3CLpro-mediated peptide cleavage. It was developed using a high-throughput screening against the 3CL protease of SARS-CoV1, as no data was yet available for the new virus (SARS-CoV2) causing the COVID-19 pandemic. It uses the ChemProp model.

Identifiers

• EOS model ID: eos9f6t
• Slug: chemprop-sars-cov-inhibition

Characteristics

• Input: Compound
• Input Shape: Single
• Task: Classification
• Output: Probability
• Output Type: Float
• Output Shape: Single
• Interpretation: Probability of 3CL protease inhibition (%) The classifier was trained using a threshold of 12% of inhibition

References

• Publication
• Source Code
• Ersilia contributor: miquelduranfrigola

Ersilia model URLs

• GitHub
• AWS S3
• DockerHub (AMD64, ARM64)

Citation

If you use this model, please cite the original authors of the model and the Ersilia Model Hub.

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 these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.

About Us

The Ersilia Open Source Initiative is a Non Profit Organization (1192266) with the mission is to equip labs, universities and clinics in LMIC with AI/ML tools for infectious disease research.

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