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评估分子相似性测量:相似性测量是否反映了电子结构特性?

Rebekah Duke¹, Chih-Hsuan Yang², Baskar Ganapathysubramanian²

¹Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States.

Journal of chemical information and modeling

|April 29, 2025

概括

评估分子相似性对于AI在化学发现中至关重要. 这项研究引入了一个新的框架来评估相似性测量与分子性质的相关性,使用大型数据集进行可靠的评估.

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
化学领域的人工智能

背景情况:

大数据,机器学习 (ML) 和生成人工智能 (AI) 越来越多地用于化学发现.
分子相似性,通常用分子指纹测量,对于数据库策划,多样性分析和属性预测至关重要.
目前对相似度的评估主要使用生物数据,限制其适用于非生物领域,如电子结构属性.

研究的目的:

开发和提出一个框架来评估分子相似性测量和分子性质之间的相关性.
通过专注于非生物特性来解决现有评估方法的局限性.
为化学界提供公开可用的数据集和评估框架.

主要方法:

利用了超过3.5亿个分子对的数据集,以及相关的电子结构,氧化还原和光学特性.
开发了一个基于邻近行为和内核密度估计 (KDE) 分析的框架.
系统地评估了各种分子指纹生成器,距离函数和分子性质之间的相关性.

主要成果:

证明了拟议框架在量化相似度和属性之间的关系方面的有效性.
确定了不同相似度测量如何捕捉特定分子性质的相关性方面的变化.
建立了基于指纹的相似性指标的全面评估,使用大规模的非生物性质数据集.

结论:

拟议的框架为评估超越生物活动的分子相似性测量提供了一个强大的方法.
精确量化分子相似性对于可靠的AI驱动化学发现至关重要.
公开可用的数据集和框架将促进化学信息学和人工智能领域的进一步研究和开发.

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