Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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

This work introduces a diffusion model for molecule generation in 3D that is equivariant to
Euclidean transformations. Our E (3) Equivariant Diffusion Model (EDM) learns to denoise a …

Generative models, especially diffusion models (DMs), have achieved promising results for
generating feature-rich geometries and advancing foundational science problems such as …

Predicting molecular conformations from molecular graphs is a fundamental problem in
cheminformatics and drug discovery. Recently, significant progress has been achieved with …

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

Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …

The generation of 3D molecules requires simultaneously deciding the categorical features
(atom types) and continuous features (atom coordinates). Deep generative models …

Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …

Deep learning (DL)-based de novo molecular design has recently gained considerable
traction. Many DL-based generative models have been successfully developed to design …