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

Motivation Accurate and rapid prediction of protein–ligand binding affinity is a great
challenge currently encountered in drug discovery. Recent advances have manifested a …

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

Biomolecular recognition between ligand and protein plays an essential role in drug
discovery and development. However, it is extremely time and resource consuming to …

The expertise accumulated in deep neural network-based structure prediction has been
widely transferred to the field of protein–ligand binding pose prediction, thus leading to the …

The cornerstone of computational drug design is the calculation of binding affinity between
two biological counterparts especially a chemical compound, ie a ligand, and a protein …

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

In silico prediction of the ligand binding pose to a given protein target is a crucial but
challenging task in drug discovery. This work focuses on blind flexible selfdocking, where …

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps
in understanding the degree of protein-ligand interactions and is a useful measure in drug …

Motivation The capability to predict the potential drug binding affinity against a protein target
has always been a fundamental challenge in silico drug discovery. The traditional …