人工智能驱动的转录学,量子力学和生理学的整合,用于预测药物诱导的肝损伤在数据有限的场景中
Roshan Bhave1, Babatunde Bello1, Divesh Bhatt1
1Verisim Life Inc, 1 Sansome Street, Suite 3500, San Francisco, California 94104, United States.
Chemical research in toxicology
|July 2, 2025
概括
由于复杂的机制和有限的数据,预测药物诱导性肝损伤 (DILI) 是一个挑战. 这项研究用各种数据增强了机器学习模型,显著提高了DILI风险预测能力.
科学领域:
- 药理学 药理学是指药理学的学科.
- 毒理学 毒理学 毒理学
- 计算化学计算化学
背景情况:
- 药物诱导性肝损伤 (DILI) 对药物和补充剂提出了重大安全问题.
- 开发DILI的准确预测模型受到复杂的损伤机制和稀缺数据的阻碍.
研究的目的:
- 增强机器学习/人工智能 (ML/AI) 模型用于预测DILI风险.
- 通过结合各种数据类型来克服DILI预测中的数据限制.
主要方法:
- 用药物的结构参数丰富ML/AI模型.
- 整合大鼠肝脏转录组学数据和量子力学衍生的分子特征.
- 使用衡量物种间药物暴露变化的指标.
主要成果:
- 丰富的ML / AI模型显示了显著改善的DILI预测能力.
- 即使在严重限制DILI数据的场景中,也实现了有效的预测.
- 结合基于知识的功能显著提高了模型的性能.
结论:
- 提出的方法,将基于知识的特征丰富AI模型,显示了DILI风险评估的巨大前景.
- 这种方法可以帮助评估药物候选药物的安全性和毒性在早期发展阶段.
- 该方法还可以支持对新药候选药物的目标参与度和有效性的评估.
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