StackDILI:通过使用有效分子表示的堆叠策略,提高药物诱导的肝损伤预测
Jiahui Guan1, Danhong Dong2, Peilin Xie1,3
1Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
Journal of chemical information and modeling
|January 9, 2025
概括
这项研究介绍了StackDILI,这是一个计算工具,可以预测药物诱导的肝损伤 (DILI) 风险. 它使用机器学习来加速毒性评估,帮助开发更安全的药物.
科学领域:
- 计算毒理学计算毒理学
- 药物开发 药物开发
- 机器学习应用程序 机器学习应用程序
背景情况:
- 药物诱导性肝损伤 (DILI) 在药物开发中带来了重大挑战,导致试验失败和退出市场.
- 准确和早期预测DILI对于减轻风险和确保药物安全至关重要.
研究的目的:
- 开发和验证StackDILI,这是一个用于预测DILI风险的计算框架.
- 加速评估潜在候选药物的肝毒性.
- 提供与DILI相关的分子特征的见解.
主要方法:
- 多种分子描述物的整合 (构成,药理,MACCS,E状态).
- 应用遗传算法,以优化特征选择.
- 使用基于树的机器学习算法开发一个堆叠组合模型.
主要成果:
- 在DILIrank测试集中,StackDILI表现出强大的预测性能.
- 该模型在交叉验证实验中表现出强度.
- 解释性分析确定了与DILI风险相关的关键分子特征.
结论:
- StackDILI提供了一个有效的计算工具来预测DILI,支持更安全的药物开发.
- 开发的Web界面提高了快速毒性预测的可访问性.
- 该框架为药物诱导的肝毒性机制提供了宝贵的见解.
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