细胞安全:一种机器学习工具,用于在药物发现中早期识别细胞毒性化合物
Francisco L Feitosa1,2,3, Victoria F Cabral1,2,3, Igor H Sanches1,2,3
1Laboratory for Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Goiás 74605-220, Brazil.
Journal of chemical information and modeling
|December 11, 2024
概括
本研究介绍了Cyto-Safe,这是一款使用AI-QSAR模型预测化合物细胞毒性的免费网络应用程序. 它有助于在发现早期优先考虑更安全的候选药物,减少毒理学风险和动物试验.
科学领域:
- 药物的发现和开发.
- 毒理学和风险评估
- 计算化学和化学信息学
背景情况:
- 细胞毒性评估在早期药物发现中至关重要,以减轻风险.
- 试管测试和AI-QSAR模型有助于高通量查和毒性化合物的预测.
- 早期优先考虑低风险化合物可以节省资源并减少动物试验.
研究的目的:
- 介绍一个免费可访问的网络应用程序,Cyto-Safe,用于预测潜在的细胞毒性化合物.
- 利用AI-QSAR模型和机器学习进行早期毒性评估.
- 为用户提供预测和可解释的AI分析,用于复合优先级.
主要方法:
- 使用机器学习和QSAR模型开发一个Web应用程序.
- 在对3T3和HEK 293细胞系的约9万个化合物的数据集上训练模型.
- 通过SMILES输入,文件上传 (CSV,SDF) 或分子素描实现用户交互.
主要成果:
- 该应用程序为两个细胞系 (3T3,HEK 293) 提供二进制细胞毒性预测.
- 对于每一个预测,都会生成信心百分比.
- 包括可解释AI (XAI) 分析来支持预测.
结论:
- 在药物发现过程中,Cyto-Safe为早期识别潜在的细胞毒性化合物提供了宝贵的工具.
- 该应用程序有助于有效选和优先考虑候选药物.
- 可访问的预测和可解释性功能有助于研究人员将毒理学风险降到最低.
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