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 …

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

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 …

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis,
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …

Rich data and powerful machine learning models allow us to design drugs for a specific
protein target\textit {in silico}. Recently, the inclusion of 3D structures during targeted drug …

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 …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with
high affinity and specificity to pre-determined protein targets. In this paper, we formulate …

A fundamental problem in drug discovery is to design molecules that bind to specific
proteins. To tackle this problem using machine learning methods, here we propose a novel …

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