ToxinPredictor: Computational models to predict the toxicity of molecules.

Autor: Goel M; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Center of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India., Amawate A; Department of Computer Science, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India., Singh A; Department of Computer Science, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India., Bagler G; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India; Center of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India. Electronic address: bagler@iiitd.ac.in.
Jazyk: angličtina
Zdroj: Chemosphere [Chemosphere] 2024 Dec 24; Vol. 370, pp. 143900. Date of Electronic Publication: 2024 Dec 24.
DOI: 10.1016/j.chemosphere.2024.143900
Abstrakt: Predicting the toxicity of molecules is essential in fields like drug discovery, environmental protection, and industrial chemical management. While traditional experimental methods are time-consuming and costly, computational models offer an efficient alternative. In this study, we introduce ToxinPredictor, a machine learning-based model to predict the toxicity of small molecules using their structural properties. The model was trained on a curated dataset of 7550 toxic and 6514 non-toxic molecules, leveraging feature selection techniques like Boruta and PCA. The best-performing model, a Support Vector Machine (SVM), achieved state-of-the-art results with an AUROC of 91.7%, F1-score of 84.9%, and accuracy of 85.4%, outperforming existing solutions. SHAP analysis was applied to the SVM model to identify the most important molecular descriptors contributing to toxicity predictions, enhancing interpretability. Despite challenges related to data quality, ToxinPredictor provides a reliable framework for toxicity risk assessment, paving the way for safer drug development and improved environmental health assessments. We also created a user-friendly webserver, ToxinPredictor (https://cosylab.iiitd.edu.in/toxinpredictor) to facilitate the search and prediction of toxic compounds.
Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest or any academic issues that could affect the publication of this manuscript.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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