Geometric Deep Learning on Molecular Representations
[article]
Kenneth Atz, Francesca Grisoni, Gisbert Schneider
2021
arXiv
pre-print
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
more »
... 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.
arXiv:2107.12375v4
fatcat:sgxlqdxiavbinly4s3zthysxbq