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MolSHAP:使用R组的Shapley值解释定量结构-活动关系
Tingzhong Tian1, Shuya Li1, Meng Fang1
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
Journal of chemical information and modeling
|August 16, 2023
概括
MolSHAP为药物发现提供了一个新的解释框架,重点关注R组对优化化合物的贡献. 这种方法增强了定量结构-活性关系分析,并帮助药物化学家在合理的分子设计中.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 机器学习在药物发现中的作用
背景情况:
- 在药物发现中,优化化合物至关重要.
- 当前的机器学习方法往往缺乏对预测的解释性解释.
- 现有的解释技术可能与药物化学家的基于碎片的优化原则不一致.
研究的目的:
- 开发一个解释框架,MolSHAP,用于定量结构-活动关系 (QSAR) 分析.
- 估计R组对分子活性的贡献,与基于片段的药物设计保持一致.
- 为解释和优化候选药物提供一种模型不可知工具.
主要方法:
- 提出了MolSHAP,这是一个解释框架,用于估计R组对分子活性的贡献.
- MolSHAP是无模型的,处理各种输入格式和模型架构.
- 在R组排名任务上使用代表性活动回归模型评估MolSHAP.
主要成果:
- 与现有方法相比,MolSHAP表现出明显优越的解释能力.
- 开发了一个复合优化算法,利用MolSHAP的见解.
- 通过独立的案例研究验证了MolSHAP优化化合物的可靠性.
结论:
- MolSHAP为准确的QSAR解释提供了一个有价值的工具.
- 该框架有助于合理优化药物发现中的化合物活动.
- 摩尔SHAP弥合了机器学习预测和药物化学实践之间的差距.
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