Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Artificial intelligence for scientific discovery has recently generated significant interest within
the machine learning and scientific communities, particularly in the domains of chemistry …

A pervasive challenge in drug design is determining how to expand a ligand─ a small
molecule that binds to a target biomolecule─ in order to improve various properties of the …

Motivation: Despite its great success in various physical modeling, differential geometry
(DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex …

Deep learning has made great strides in tackling chemical problems, but still lacks full‐
fledged representations for three‐dimensional (3D) molecular structures for its inner …

Recently, machine learning (ML) has established itself in various worldwide benchmarking
competitions in computational biology, including Critical Assessment of Structure Prediction …