建立一个综合模型来预测复合物致变性,并进行特征重要性分析
Chao-Hsu Yang1, Tony Eight Lin2,3, Jui-Hua Hsieh4
1Graduate Institute of Environmental Engineering, College of Engineering, National Taiwan University, 71, Chou-Shan Road, Da'an Dist., Taipei 106, Taiwan.
Journal of chemical information and modeling
|October 21, 2025
概括
深度学习模型快速选化学变异性,优于传统方法. 最好的模型实现了高精度,识别了突变性化合物的关键结构警报.
科学领域:
- 计算化学和毒理学计算化学和毒理学
- 化学安全评估中的人工智能
背景情况:
- 评估化学变异性对于公众健康和环境安全至关重要.
- 像艾姆斯测试这样的传统方法对于大规模查来说是缓慢而昂贵的.
- 深度学习提供了一种更快,更具成本效益的方法来预测突变性.
研究的目的:
- 开发和评估一个集成的深度学习框架,用于预测化合物突变性.
- 为了确定最佳的分子特征和模型组合,以进行准确的致变性评估.
- 提供与突变性潜力相关的结构特征的洞察力.
主要方法:
- 通过结合13种分子描述符和指纹,开发了78个集成的深度学习模型.
- 对5279种化合物进行了训练,对587种化合物进行了评估.
- 进行活动悬崖和适用性领域分析,以评估模型可靠性和识别错误预测来源.
主要成果:
- MACCS-Mordred深度学习模型以0.885平衡精度和0.922精度实现了最高的性能.
- 适用性领域分析证实了该模型对测试化合物的稳定性.
- 特性重要性分析突出了含和环子结构作为突变性关键指标.
结论:
- 支持人工智能的深度学习模型提供了一个强大的工具,用于快速和经济高效的突变致病性查.
- 开发的框架加强了早期化学风险评估,并有助于优先考虑危险化合物.
- 研究结果支持人工智能用于环境监测和监管决策.
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