  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.
