Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence. GDL bears particular promise in molecular modeling applications, in which various molecular representations with different symmetry properties and levels of abstraction exist. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned molecular features and their complementarity to well-established molecular descriptors. This review provides an overview of current challenges and opportunities, and presents a forecast of the future of GDL for molecular sciences.

翻译：以包含和处理对称信息的神经网络结构为基础的几何深学(GDL)已成为人造智能的近期范例,GDL在分子建模应用方面特别有希望,在分子建模应用中存在各种分子代表,具有不同的对称性质和抽象程度,该审查对分子GDL进行了结构化和统一的概览,突出其在药物发现、化学合成预测和量子化学方面的应用,强调所学分子特征的相关性及其与久经确立的分子描述器的互补性,该审查概述了当前的挑战和机遇,并预测GDL对分子科学的未来。