Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Abstract Generative Artificial Intelligence (GAI) is attracting the increasing attention of
materials community for its excellent capability of generating required contents. With the …

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 …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising
diffusion probabilistic models. While existing diffusion-based methods operate on images …

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

Molecular conformer generation is a fundamental task in computational chemistry. Several
machine learning approaches have been developed, but none have outperformed state-of …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

AI-based molecule generation provides a promising approach to a large area of biomedical
sciences and engineering, such as antibody design, hydrolase engineering, or vaccine …