M3Hep:一种多模式的肝毒性预测模型,结合了线粒体毒性和掩盖策略
Yang Liu1, Yu Xie1, Xiao Wang1
1School of Science, China Pharmaceutical University, Nanjing, P.R. China.
Toxicology mechanisms and methods
|November 11, 2025
概括
这项研究引入了M3Hep,这是一种通过整合分子结构和线粒体毒性数据来预测药物肝毒性的新框架. M3Hep提高了预测准确度,突出了线粒体功能障碍和肝损伤之间的联系.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 药物发现 药物发现
背景情况:
- 药物诱导性肝损伤 (DILI) 是药物失效的主要原因之一.
- 线粒体功能障碍是DILI的一个关键机制.
- 准确预测肝毒性对于药物开发至关重要.
研究的目的:
- 开发一个多式模式框架 (M3Hep) 来改善肝毒性预测.
- 为了整合分子结构 (SMILES,图表) 和线粒体毒性数据.
- 为了研究线粒体毒性在预测药物诱导的肝损伤中的作用.
主要方法:
- 收集了8459个线粒体毒性和6418个肝毒性样本.
- 开发了一种线粒体毒性预测模型 (AUC=0.96).
- 将分子特征和预测的线粒体毒性集成到M3Hep框架中,使用掩盖策略.
主要成果:
- 在肝毒性预测方面,M3Hep的AUC为0.81.
- 纳入线粒体毒性数据显著改善了预测性能.
- M3Hep的表现优于基线模型和现有的肝毒性预测工具 (MCC=0.49).
结论:
- M3Hep证明了线粒体毒性对肝毒性的显著贡献.
- 该框架为早期识别潜在的肝毒药物提供了一个有希望的方法.
- 使用GNNExplainer和SHAP进行的可解释性分析为M3Hep的预测提供了洞察力.
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