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Deep Learning Model for Prediction of Compound Activities Over a Panel of Major Toxicity-Related Proteins

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Machine Learning and Deep Learning in Computational Toxicology

Part of the book series: Computational Methods in Engineering & the Sciences ((CMES))

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Abstract

One of the main reasons for high attrition rate for drug candidates is their toxicity mediated through off-target binding. Thus, in vitro screening against major toxicity-related proteins is conventionally used to filter out problematic compounds at an early stage of drug development. However, in vitro testing is expensive and labour intense. Herein, we present a deep learning model that can effectively predict interaction between a small molecule of interest and a panel of 44 toxicity-associated proteins. Contrary to conventional toxicity prediction models (with one model per one endpoint), we employed a unified approach where a single model predicts activity over multiple targets. We demonstrate that this approach is superior, especially for datasets with unbalanced classes. The model is proposed for large-scale toxicity screening as well as for the elucidation of toxicity mechanisms of drug candidates.

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Authors and Affiliations

  1. Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada

    Mariia Radaeva, Mohit Pandey, Hazem MsLati & Artem Cherkasov

Authors
  1. Mariia Radaeva
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  2. Mohit Pandey
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  3. Hazem MsLati
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  4. Artem Cherkasov
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Corresponding author

Correspondence to Artem Cherkasov .

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Editors and Affiliations

  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Radaeva, M., Pandey, M., MsLati, H., Cherkasov, A. (2023). Deep Learning Model for Prediction of Compound Activities Over a Panel of Major Toxicity-Related Proteins. In: Hong, H. (eds) Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-20730-3_25

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