拓回归作为一种可解释和有效的工具,用于定量结构-活动关系建模
Ruibo Zhang1, Daniel Nolte1, Cesar Sanchez-Villalobos1
1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
拓回归 (TR) 为药物发现提供了复杂的定量结构-活性关系 (QSAR) 模型的可解释替代方案. 这种基于相似性的方法可以实现可比或优越的预测性能,同时增强分子设计洞察力.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 定量结构-活性关系 (QSAR) 模型在药物发现中至关重要,但往往缺乏可解释性,限制了它们在分子设计中的使用.
- 当前可解释的QSAR方法可能与深度学习等复杂模型的预测能力不匹配.
研究的目的:
- 引入拓回归 (TR),一种基于相似性的回归框架,用于药物反应预测.
- 评估TR的性能和可解释性与深度学习QSAR模型相比.
- 证明TR在提取化学空间和生物活动之间的有意义关系中的实用性.
主要方法:
- 开发了一个基于相似性的回归框架,称为拓回归 (TR).
- 使用530个ChEMBL人类目标活动数据集,将TR的预测性能与深度学习QSAR模型进行了比较.
- 通过检查化学和活动空间之间的提取的映射来分析TR的解释性.
主要成果:
- 稀疏的TR模型实现了与基于深度学习的QSAR模型相比或超过的预测性能.
- 通过揭示药物化学空间和活性空间之间的近似同度,TR提供了更直观的解释.
- TR证明了统计基础和计算效率.
结论:
- 拓回归 (TR) 为药物发现中的定量结构-活性关系 (QSAR) 建模提供了一个强大,可解释和高效的替代方案.
- 通过提供对化学结构和生物活动之间的关系的可解释的见解,TR增强了分子设计.
- 该框架促进了对药物反应预测的更深入的理解,超出了纯粹的预测准确性.
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