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Related Experiment Video

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.

Salvatore Galati1, Miriana Di Stefano1,2, Elisa Martinelli1

  • 1Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.

International Journal of Molecular Sciences
|February 26, 2022
PubMed
Summary

VenomPred is a new web tool using machine learning (ML) models to predict small molecule toxicity, aiding drug discovery. Its consensus approach improves accuracy for mutagenic, hepatotoxic, carcinogenic, and estrogenic effects.

Keywords:
artificial intelligencecarcinogenicityestrogenicityhepatoxicityin silico toxicitymachine learningmutagenicity

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Area of Science:

  • Computational toxicology
  • Drug discovery and development
  • Bioinformatics

Background:

  • In silico toxicity prediction is crucial for lead compound selection and ADMET studies due to limitations of in vitro and in vivo methods.
  • Existing methods face constraints in ethics, time, and resources, necessitating advanced computational approaches.

Purpose of the Study:

  • To introduce VenomPred, a user-friendly web tool for predicting potential mutagenic, hepatotoxic, carcinogenic, and estrogenic effects of small molecules.
  • To develop and validate novel in-house Machine Learning (ML) models for toxicity prediction.

Main Methods:

  • Developed in-house ML models using datasets from VEGA QSAR, a reference software for toxicity modeling.
  • Employed a consensus approach combining multiple ML models to enhance predictive performance.
  • Implemented the models on a freely accessible webserver accepting SMILES strings as input.

Main Results:

  • The in-house ML models demonstrated performance equal to or better than reference models in VEGA QSAR.
  • The consensus approach significantly improved the prediction accuracy of chemical toxicity compared to single models.
  • VenomPred provides probability scores for potential toxicity of small molecules.

Conclusions:

  • VenomPred offers a reliable and accessible platform for in silico toxicity assessment.
  • The consensus ML approach enhances the accuracy of predicting diverse toxicological endpoints.
  • This tool supports efficient and ethical lead compound selection in drug development.