Geometric deep learning on molecular representations

doi:10.1038/s42256-021-00418-8

Geometric deep learning on molecular representations

2021-12-15

160

PRE

AI

DOI：10.1038/s42256-021-00418-8

Original

Original request for help

Save

Abstract

En 中文

Geometric deep learning (GDL) is based on neural network architectures that incorporate and process symmetry information. GDL bears promise for molecular modelling applications that rely on molecular representations with different symmetry properties and levels of abstraction. This Review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction and quantum chemistry. It contains an introduction to the principles of GDL, as well as relevant molecular representations, such as molecular graphs, grids, surfaces and strings, and their respective properties. The current challenges for GDL in the molecular sciences are discussed, and a forecast of future opportunities is attempted. Geometric representations are becoming more important in molecular deep learning as the spatial structure of molecules contains important information about their properties. Kenneth Atz and colleagues review current progress and challenges in this emerging field of geometric deep learning.

Keywords:

NEURAL-NETWORKS

ORGANIC-CHEMISTRY

DRUG DISCOVERY

PREDICTION

LANGUAGE

DESIGN

TRANSFORMER

BINDING

SMILES

MODEL

Geometric deep learning on molecular representations

Abstract

Journal

Organization