Protein complexes are vital to many biological processes and their understanding can lead
to the development of new drugs and therapies. Although the structure of individual protein …

Conventional protein–protein docking algorithms usually rely on heavy candidate sampling
and reranking, but these steps are time‐consuming and hinder applications that require high …

Antibody-antigen complex modelling is an important step in computational workflows for
therapeutic antibody design. While experimentally determined structures of both antibody …

Computational docking methods can provide structural models of protein–protein
complexes, but protein backbone flexibility upon association often thwarts accurate …

Motivation Computational prediction of protein–protein complex structure by docking can
provide structural and mechanistic insights for protein interactions of biomedical interest …

Molecular docking, given a ligand molecule and a ligand binding site (called``pocket'') on a
protein, predicting the binding mode of the protein-ligand complex, is a widely used …

Protein-ligand docking is an essential tool in structure-based drug design with applications
ranging from virtual high-throughput screening to pose prediction for lead optimization. Most …

Molecular docking, a pivotal computational tool for drug discovery, predicts the binding
interactions between small molecules (ligands) and target proteins (receptors) …

Protein complex structure prediction plays important roles in various applications, such as
drug discovery and antibody design. However, due to limited prediction accuracy, there are …

Protein-ligand docking is an important task in drug discovery and structure-based drug
design. Generative deep learning models have recently emerged as a new approach to …