Recent advances and accomplishments of artificial intelligence (AI) and deep generative
models have established their usefulness in medicinal applications, especially in drug …

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

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 …

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 …

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

Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …

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

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Most molecular generative models based on artificial intelligence for de novo drug design
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …

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 …