Rational structure-based drug design relies on accurate predictions of protein–ligand
binding affinity from structural molecular information. Although deep learning-based …

Understanding the interactions between a ligand and its molecular target is crucial in
guiding the optimization of molecules for any in-silico drug-design workflow. Multiple …

Structure-based virtual screening plays a critical role in drug discovery. However, numerous
docking programs, such as AutoDock Vina and Glide, are time-consuming due to the …

Accurate prediction of the protein-ligand binding affinities is an essential challenge in the
structure-based drug design. Despite recent advance in data-driven methods in affinity …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, several deep learning …

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in
identifying potential ligands for target disease-related proteins. Accurate affinity predictions …

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 …

Predicting protein–ligand binding affinity or free energy can save time and resources in drug
discovery and development by helping to predict new drug candidates and better …

Understanding the interactions formed between a ligand and its molecular target is key to
guiding the optimization of molecules. Different experimental and computational methods …