超越QSARs:用于增强药物毒性预测的定量知识-活动关系 (QKARs)
Ting Li1, Yanyan Qu1,2, Alexander Chen1
1National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, United States.
Toxicological sciences : an official journal of the Society of Toxicology
|September 30, 2025
概括
在预测药物毒性方面,定量知识-活动关系 (QKAR) 优于传统的定量结构-活动关系 (QSAR). QKAR利用领域知识,提高药物诱导的肝损伤和心脏毒性预测的准确性.
科学领域:
- 计算毒理学计算毒理学
- 药物安全评估 药物安全评估
- 药理学中的人工智能
背景情况:
- 量化结构-活性关系 (QSARs) 传统上基于化学结构来预测毒性.
- 对于具有轻微结构变化但有显著毒性差异的药物,QSARs的结构中心方法限制了准确性.
- 人工智能的进步为整合更广泛的化学知识提供了新的方法,以提高毒性预测.
研究的目的:
- 引入一个新的框架,定量知识-活动关系 (QKARs),用于预测药物毒性.
- 评估QKAR与药物诱导性肝损伤 (DILI) 和药物诱导性心脏毒性 (DICT) 的QSAR的性能.
- 探索基于知识和基于结构的数据的整合,以提高预测准确度.
主要方法:
- 开发了QKAR模型,使用DILI和DICT的领域特定知识表示.
- 应用了五种不同的机器学习算法来评估模型复杂度对性能的影响.
- 使用相同的数据集和终点,比较QKAR与QSAR的性能.
- 研究的综合知识和基于结构的模型 (Q(K+S) AR).
主要成果:
- 使用全面药物知识的QKAR模型显示出卓越的预测准确性.
- 模型复杂性与不同机器学习算法的性能之间存在最小的关联.
- 在DILI和DICT方面,QKAR的表现始终优于QSAR.
- QKARs有效地区分了具有类似结构但具有不同肝脏毒性概况的药物.
结论:
- 在药物毒性评估中,QKARs是传统QSARs的强有力的替代品.
- 利用特定领域的知识显著提高了毒性预测的准确性.
- 整合基于知识和基于结构的数据为预测建模提供了进一步的改进.
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