Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Autor: Feitosa FL; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil., F Cabral V; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil., Sanches IH; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil., Silva-Mendonca S; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil., Borba JVVB; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil., Braga RC; InsilicAll Inc., São Paulo, São Paulo 04571-010, Brazil., Andrade CH; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.; Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 05508-220, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.
Jazyk: angličtina
Zdroj: Journal of chemical information and modeling [J Chem Inf Model] 2024 Dec 23; Vol. 64 (24), pp. 9056-9062. Date of Electronic Publication: 2024 Dec 11.
DOI: 10.1021/acs.jcim.4c01811
Abstrakt: Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. In vitro assays support high-throughput screening, allowing for efficient detection of toxic substances while considerably reducing the need for animal testing. Additionally, AI-based Quantitative Structure-Activity Relationship (AI-QSAR) models enhance early stage predictions by assessing the cytotoxic potential of molecular structures, which helps prioritize low-risk compounds for further validation. We present a freely accessible web application designed for identifying potential cytotoxic compounds utilizing QSAR models. This application utilizes machine learning techniques and is built on a data set of approximately 90,000 compounds, evaluated against two cell lines, 3T3 and HEK 293. Users can interact with the app by inputting a SMILES representation, uploading CSV or SDF files, or sketching molecules. The output includes a binary prediction for each cell line, a confidence percentage, and an explainable AI (XAI) analysis. Cyto-Safe web-app version 1.0 is available at http://insightai.labmol.com.br/.
Databáze: MEDLINE
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