慢性DPi预测器:一种可解释的深度学习框架,用于化学慢性和次慢性毒性评估
Xuelin Sun1,2, Jiaqi Chu3, Rong Ni4
1Department of Pharmacy, Beijing Hospital, National Center of Gerontology, Beijing, 100730, China.
Molecular diversity
|February 12, 2026
概括
这项研究介绍了ChronicDPipredictor,这是一个用于预测化学慢性和亚慢性毒性的机器学习工具. 该框架实现了高准确性,并为风险评估提供了可解释的结果.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 机器学习用于药物安全.
背景情况:
- 评估长期 (慢性) 和中期 (亚慢性) 化学毒性至关重要,但由于复杂的机制和多样化的结构,具有挑战性.
- 为这些毒性终点开发准确的预测计算机模型仍然是化学品安全评估中的一个重大障碍.
研究的目的:
- 开发一种可解释的机器学习框架,ChronicDPipredictor,用于评估化学品的慢性和次慢性毒性.
- 使用SHAP分析提高毒性预测的可解释性.
- 为毒性预测提供一个公开可访问的网络服务器,并识别与毒性相关的结构性警报.
主要方法:
- 开发了使用机器学习与MACCS,PubChem和KRFP指纹的ChronicDPipredictor.
- 在慢性和次慢性毒性三类和二元分类中评估模型性能.
- 应用 SHAP 分析以了解模型的可解释性,并从 KRFP 指纹中提取结构性警报.
主要成果:
- 基于MACCS指纹的模型取得了最高的性能,在三类分类中精度高达0.82 (慢性) 和0.80 (亚慢性).
- 二元分类的准确性达到0.93的慢性和0.83的亚慢性毒性.
- 确定了18个慢性毒性和7个亚慢性毒性结构性警报,其中几个与已知的毒理机制有关.
结论:
- 慢性DPi预测器提供了一个准确和可解释的方法来评估化学慢性和亚慢性毒性.
- 该框架及其识别的结构性警报有助于评估复合物重复剂量毒性的风险.
- 开发的Web服务器为研究人员和监管机构提供了一个实用的工具.
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