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通过采样合规空间来增强原子机器学习的化学数据库

Luis Itza Vazquez-Salazar^1,2, Markus Meuwly¹

¹Department of Chemistry, University of Basel, Basel CH-4056, Switzerland.

Journal of chemical information and modeling

|August 4, 2025

概括

为化学任务优化机器学习 (ML) 模型需要仔细选择数据库. 增加初始数据库的不同形状的小百分比大大提高了预测的准确性.

科学领域:

计算化学是一种计算化学.
化学信息学化学信息学
机器学习应用程序机器学习应用程序

背景情况:

机器学习 (ML) 是化学空间探索的组成部分.
模型的性能高度依赖于培训数据库.
现有的数据库对于特定的化学预测任务可能不是最佳的.

研究的目的:

调查数据库增强对化学任务的ML模型性能的影响.
识别最佳策略来选择分子和构造,以改进数据库.
建立一个合理的化学数据库创建的基准.

主要方法:

系统地增加初始受限数据库 (iRD) 的新分子和结构.
在不同温度下生成分子构造.
使用诸如Kullback-Leibler (D_KL) 和Jensen-Shannon (D_JS) 差异以及瓦瑟斯坦距离 (W_1) 等指标评估预测性能.

主要成果:

在300K产生的一小部分 (1%) 构造的添加,在各种化学任务中始终提高了模型性能.
增强集中的多余分子和高度变形的结构对预测质量产生了负面影响.
使用分歧和距离指标有效分析了能源和债券分布.

结论:

化学数据库的合理增强对于提高ML模型性能至关重要.
仔细选择分子和构造,特别是包括低百分比,多样化的构造,是提高预测准确性的关键.
该研究为ML应用程序创建或增强合成化学数据库提供了一个框架.

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