通过新型SMILES碎片化和可解释的深度学习提高合成化学品的毒性预测
Yumian Zhou1, Yu He1, Wenzheng Zhou1
1Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing 21166, Jiangsu, China.
Journal of chemical information and modeling
|August 26, 2025
概括
使用SMILES碎片化的新深度学习框架预测化学毒性并识别结构性警报. 这种可解释的工具有助于对合成化学品进行安全评估.
科学领域:
- 计算化学
- 毒理学
- 人工智能
背景情况:
- 传统的毒性预测方法缺乏解释性.
- 识别结构性警报对于化学风险评估至关重要.
研究的目的:
- 开发一种可解释的深度学习框架,用于化学毒性预测和SA识别.
- 评估不同的SMILES碎片化策略.
主要方法:
- 开发了一个集成SMILES碎片化与1D卷积神经网络 (SFDL) 的框架.
- 在PubChem化合物上评估了四种碎片化方法 (单原子,单符号,以原子为中心,以符号为中心).
- 应用最有效的模型 (以符号为中心的碎片化) 来预测28,160种合成化学物质的毒性.
主要成果:
- 以符号为中心的碎片化方法实现了突变性预测的卓越性能 (AUC = 0.87,PRAUC = 0.90).
- 在10个毒性终点中的6个中,SFDL-GenTok策略显示出强大的预测性能 (AUC=0. 810. 93).
- 确定了与内分泌干扰,突变性和线粒体毒性相关的关键结构警报,包括基碎片,基/基和电友基.
结论:
- SFDL-GenTok框架提供了一个可解释和有效的化学毒性和SA识别工具.
- 这种方法有助于评估合成化学品对环境和人类健康的风险.
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