Many processes in biology and drug discovery involve various 3D interactions between
molecules, such as protein and protein, protein and small molecule, etc. Given that different …

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 …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

We consider representation learning for proteins with 3D structures. We build 3D graphs
based on protein structures and develop graph networks to learn their representations …

Unlike vision and language data which usually has a unique format, molecules can naturally
be characterized using different chemical formulations. One can view a molecule as a 2D …

Spatial structures in the 3D space are important to determine molecular properties. Recent
papers use geometric deep learning to represent molecules and predict properties. These …

Molecular complexes formed by proteins and small-molecule ligands are ubiquitous, and
predicting their 3D structures can facilitate both biological discoveries and the design of …

Self-supervised molecular representation learning is critical for molecule-based tasks such
as AI-assisted drug discovery. Recent studies consider leveraging both 2D and 3D …

Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …

Rich data and powerful machine learning models allow us to design drugs for a specific
protein target\textit {in silico}. Recently, the inclusion of 3D structures during targeted drug …