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 …

Numerous therapeutic approaches have been developed to enable interrogation and
modulation of protein isoforms, but often require laborious experimental development or …

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 …

Molecule generation is a very important practical problem, with uses in drug discovery and
material design, and AI methods promise to provide useful solutions. However, existing …

The design of\emph {de novo} functional proteins that bind with specific ligand molecules is
crucial in various domains like therapeutics and bio-engineering. One vital yet challenging …

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 …

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

Generating desirable molecular structures in 3D is a fundamental problem for drug
discovery. Despite the considerable progress we have achieved, existing methods usually …

Generating 3D ligand molecules that bind to specific protein targets via diffusion models has
shown great promise for structure-based drug design. The key idea is to disrupt molecules …

Designing molecules with desirable physiochemical properties and functionalities is a long-
standing challenge in chemistry, material science, and drug discovery. Recently, machine …