Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

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 …

Fragment‐based drug discovery (FBDD) is now established as a complementary approach
to high‐throughput screening (HTS). Contrary to HTS, where large libraries of drug‐like …

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 …

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 …

Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-
based methods design drugs by generating novel molecules from scratch, avoiding …

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

In recent years, AI-assisted drug design methods have been proposed to generate
molecules given the pockets' structures of target proteins. Most of them are {\em atom-level …

Drug discovery, which plays a vital role in maintaining human health, is a persistent
challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery …