New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …

Proteins participate in various essential processes in vivo via interactions with other
molecules. Identifying the residues participating in these interactions not only provides …

Predicting binding affinities between small molecules and the protein target is at the core of
computational drug screening and drug target identification. Deep learning-based …

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

Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …

Data driven computational approaches to predicting protein–ligand binding are currently
achieving unprecedented levels of accuracy on held-out test datasets. Up until now …

Recent advances in high-throughput experimental methods for the identification of protein
interactions have resulted in a large amount of diverse data that are somewhat incomplete …

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …

Accurately predicting protein–ligand binding affinities can substantially facilitate the drug
discovery process, but it remains as a difficult problem. To tackle the challenge, many …