Chemical space is impractically large, and conventional structure-based virtual screening
techniques cannot be used to simply search through the entire space to discover effective …

Ligand-based drug design has recently benefited from the development of deep generative
models. These models enable extensive explorations of the chemical space and provide a …

Generative models have increasingly been proposed as a solution to the molecular design
problem. However, it has proved challenging to control the design process or incorporate …

Deep learning (DL)-based de novo molecular design has recently gained considerable
traction. Many DL-based generative models have been successfully developed to design …

Computer-aided drug discovery has been around for decades, although the past few years
have seen a tectonic shift towards embracing computational technologies in both academia …

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

Deep generative modeling has a strong potential to accelerate drug design. However,
existing generative models often face challenges in generalization due to limited data …

We report for the first time the use of experimental electron density (ED) as training data for
the generation of drug-like three-dimensional molecules based on the structure of a target …

Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
Existing structured-based drug design methods treat all ligand atoms equally, which ignores …

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 …