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

IF 5.6 2区化学 Q1 CHEMISTRY, MEDICINAL

Journal of Chemical Information and Modeling Pub Date : 2024-12-11 DOI:10.1021/acs.jcim.4c0181110.1021/acs.jcim.4c01811

Francisco L. Feitosa, Victoria F. Cabral, Igor H. Sanches, Sabrina Silva-Mendonca, Joyce V. V. B. Borba, Rodolpho C. Braga and Carolina Horta Andrade*,

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引用次数: 0

Abstract

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/.

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细胞安全：在药物发现中早期识别细胞毒性化合物的机器学习工具

细胞毒性在药物发现中至关重要，可以在筛选过程中对有毒化合物进行早期评估，以尽量减少毒理学风险。体外分析支持高通量筛选，允许有效检测有毒物质，同时大大减少对动物试验的需求。此外，基于人工智能的定量构效关系（AI-QSAR）模型通过评估分子结构的细胞毒性潜力来增强早期预测，这有助于优先考虑低风险化合物进行进一步验证。我们提出了一个免费访问的web应用程序，旨在利用QSAR模型识别潜在的细胞毒性化合物。该应用程序利用机器学习技术，建立在大约90,000个化合物的数据集上，对两种细胞系3T3和HEK 293进行了评估。用户可以通过输入SMILES表示、上传CSV或SDF文件或绘制分子草图来与应用程序进行交互。输出包括每个细胞系的二进制预测、置信度百分比和可解释的AI （XAI）分析。Cyto-Safe web应用程序1.0版本可在http://insightai.labmol.com.br/获得。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.