Deep generative models have gained recent popularity for chemical design. Many of these
models have historically operated in 2D space; however, more recently explicit 3D …

Abstract 3D structure-based drug design (SBDD) is considered a challenging and rational
way for innovative drug discovery. Geometric deep learning is a promising approach that …

We propose a new score-based approach to generate 3D molecules represented as atomic
densities on regular grids. First, we train a denoising neural network that learns to map from …

Generative models for molecules based on sequential line notation (for example, the
simplified molecular-input line-entry system) or graph representation have attracted an …

High-quality protein–ligand complex structures provide the basis for understanding the
nature of noncovalent binding interactions at the atomic level and enable structure-based …

Learning over the domain of 3D graphs has applications in a number of scientific and
engineering disciplines, including molecular chemistry, high energy physics, and computer …

The incredible capabilities of generative artificial intelligence models have inevitably led to
their application in the domain of drug discovery. It is therefore of tremendous interest to …

Deep learning-based generative adversarial network (GAN) frameworks have recently been
developed to expedite the drug discovery process. These models generate novel molecules …

We propose an algorithm for learning a conditional generative model of a molecule given a
target. Specifically, given a receptor molecule that one wishes to bind to, the conditional …

In this paper we present a method that allows leveraging 3D electron density information to
train a deep neural network pipeline to segment regions of high, medium and low …