Protein-ligand complex structures have been utilised to design benchmark machine learning
methods that perform important tasks related to drug design such as receptor binding site …

Modeling the interaction between proteins and ligands and accurately predicting their
binding structures is a critical yet challenging task in drug discovery. Recent advancements …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …

Protein (receptor)--ligand interaction prediction is a critical component in computer-aided
drug design, significantly influencing molecular docking and virtual screening processes …

Most widely used ligand docking methods assume a rigid protein structure. This leads to
problems when the structure of the target protein deforms upon ligand binding. In particular …

Machine learning scoring functions for protein–ligand binding affinity have been found to
consistently outperform classical scoring functions when trained and tested on crystal …

Understanding the structure of the protein-ligand complex is crucial to drug development.
However, existing virtual structure measurement methods are mainly docking and its derived …

Computational drug design relies on the calculation of binding strength between two
biological counterparts especially a chemical compound, ie, a ligand, and a protein …

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug
discovery. While deep learning models have been proposed to accelerate the identification …

Protein–ligand interactions are increasingly profiled at high-throughput, playing a vital role
in lead compound discovery and drug optimization. Accurate prediction of binding pose and …