利用DFT和分子碎片化进行化学精确的pKa预测,使用机器学习
Alec J Sanchez1, Sarah Maier1, Krishnan Raghavachari1
1Department of Chemistry, Indiana University?, Bloomington, Indiana 47405, United States.
Journal of chemical information and modeling
|February 1, 2024
概括
我们开发了一个机器学习 (ML) 模型,使用随机森林来预测分子酸度 (pKa). 这个框架将密度函数理论 (DFT) 的计算与一种新的碎片化方法相结合,用于准确的预测.
科学领域:
- 计算化学的计算化学
- 机器学习在化学中的应用
- 物理有机化学 有机化学
背景情况:
- 预测分子酸度 (pKa) 在化学中至关重要.
- 现有的方法通常依赖于计算上昂贵的计算或缺乏准确性.
- 密度函数理论 (DFT) 的计算显示了pKa预测的系统错误.
研究的目的:
- 开发一个准确和高效的机器学习 (ML) 框架,用于预测复杂有机分子的pKa值.
- 用 ML 纠正 DFT 计算中的系统错误.
- 探索基于连接的层次结构 (CBH) 碎片化协议用于生成分子描述符的实用性.
主要方法:
- 采用了一个基于森林的随机ML模型.
- 从低级别的DFT计算中结合了基于物理的特征.
- 使用基于连接的层次结构 (CBH) 碎片化协议来导出结构特征.
- 该框架在SAMPL6和诺华的基准数据集上得到了验证.
主要成果:
- ML框架准确地预测复杂有机分子的pKa值.
- DFT和CBH特征的结合有效地纠正了系统的DFT错误.
- 该模型证明了对基准数据集的良好概括性和性能.
- 基于物理的功能减少了数据依赖和复杂深度学习架构的需求.
结论:
- 拟议的量子力学/ML框架为pKa预测提供了一个准确和高效的方法.
- 扩展了CBH碎片化协议,用于ML应用程序生成新型分子描述符.
- 这种方法为计算化学家和药物发现研究人员提供了宝贵的工具.
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