We study a fundamental problem in structure-based drug design---generating molecules
that bind to specific protein binding sites. While we have witnessed the great success of …

The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …

A persistent goal for de novo drug design is to generate novel chemical compounds with
desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models …

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 …

Deep generative models have been applied with increasing success to the generation of
two dimensional molecules as SMILES strings and molecular graphs. In this work we …

Deep generative models are attracting much attention in the field of de novo molecule
design. Compared to traditional methods, deep generative models can be trained in a fully …

Generating molecules with high binding affinities to target proteins (aka structure-based
drug design) is a fundamental and challenging task in drug discovery. Recently, deep …

Rational compound design remains a challenging problem for both computational methods
and medicinal chemists. Computational generative methods have begun to show promising …

Generative models have achieved impressive results in many domains including image and
text generation. In the natural sciences, generative models have led to rapid progress in …

Designing ligand molecules that bind to specific protein binding sites is a fundamental
problem in structure-based drug design. Although deep generative models and geometric …