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可解释的监督机器学习模型来预测溶解吉布斯能量

José Ferraz-Caetano¹, Filipe Teixeira², M Natália D S Cordeiro¹

¹Department of Chemistry and Biochemistry - Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007 Porto, Portugal.

Journal of chemical information and modeling

|August 21, 2023

概括

我们开发了一种新的机器学习模型,以高准确性和速度预测溶解自由能量 (ΔGsol). 这种模型提供了宝贵的化学见解,在不需要复杂的模拟的情况下,其性能优于现有的方法.

科学领域:

计算化学计算化学
机器学习应用机器学习应用

背景情况:

准确预测溶解自由能量 (ΔGsol) 仍然是计算模型面临的挑战.
现有的机器学习 (ML) 方法提供了速度,但缺乏广泛的化学预测的解释能力.

研究的目的:

开发一种新的监督ML模型,用于预测ΔGsol.
为了实现一个有利的速度-准确性权衡与增强的解释性见解.

主要方法:

在ML模型开发中使用了两个集合回归算法.
采用开源化学特征编码电子,结构和表面积描述符.
综合分子特性和化学相互作用特征用于分析.

主要成果:

在 ΔGsol 预测中实现了高精度,超过了基准神经网络方法.
确定了极地表面积的增加和极化能力的减少作为主要的溶液描述符.
证明最大绝对误差为0.22 ± 0.02 kcalmol-1.1,其中最大的绝对误差为0.22 ± 0.02 kcal.
在外部数据库和通过溶剂保留试验验证实模型性能.

结论:

开发的ML模型提供了一个快速而准确的方法来预测ΔGsol.
该模型为描述符的重要性提供了有价值的解释性见解.
这种方法有可能预测计算化学中的其他热力学性质.

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