共价键
代表(政治)
水准点(测量)
分子
财产(哲学)
分子模型
分子描述符
事实上
计算机科学
深度学习
人工智能
化学
生物系统
机器学习
数量结构-活动关系
立体化学
生物
有机化学
大地测量学
地理
哲学
认识论
政治
政治学
法学
作者
Cong Shen,Jiawei Luo,Kelin Xia
标识
DOI:10.1016/j.crmeth.2023.100621
摘要
Molecular representation learning plays an important role in molecular property prediction. Existing molecular property prediction models rely on the de facto standard of covalent-bond-based molecular graphs for representing molecular topology at the atomic level and totally ignore the non-covalent interactions within the molecule. In this study, we propose a molecular geometric deep learning model to predict the properties of molecules that aims to comprehensively consider the information of covalent and non-covalent interactions of molecules. The essential idea is to incorporate a more general molecular representation into geometric deep learning (GDL) models. We systematically test molecular GDL (Mol-GDL) on fourteen commonly used benchmark datasets. The results show that Mol-GDL can achieve a better performance than state-of-the-art (SOTA) methods. Extensive tests have demonstrated the important role of non-covalent interactions in molecular property prediction and the effectiveness of Mol-GDL models.
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