Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …

Geometric graph is a special kind of graph with geometric features, which is vital to model
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …

Understanding the 3D structures of protein multimers is crucial, as they play a vital role in
regulating various cellular processes. It has been empirically confirmed that the multimer …

Proteins can be represented in various ways, including their sequences, 3D structures, and
surfaces. While recent studies have successfully employed sequence-or structure-based …

The knowledge of protein interactions is crucial but challenging for drug discovery
applications. This work focuses on protein interface prediction, which aims to determine …

Protein complex formation, a pivotal challenge in contemporary biology, has recently gained
interest from the machine learning community, particularly concerning protein-ligand …

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions,
including signal transduction, transportation, and immune defense. As the accuracy of multi …

Recent advancements in machine learning (ML) are transforming the field of structural
biology. For example, AlphaFold, a groundbreaking neural network for protein structure …

Proteins can be represented in different data forms, including sequence, structure, and
surface, each of which has unique advantages and certain limitations. It is promising to fuse …

Deep generative models (DGMs) have shown great potential in structured-based drug
design (SBDD). However, existing methods overlook a crucial physical constraint during …