Geometric Deep Learning on Molecular Representations
It addresses molecular modeling challenges for researchers in drug discovery and chemistry, but is incremental as it is a review paper summarizing existing work.
This review tackles the application of geometric deep learning (GDL) to molecular modeling by providing a structured overview of its use in drug discovery, chemical synthesis prediction, and quantum chemistry, highlighting the complementarity of learned features with established molecular descriptors.
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.