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 …

Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A
number of deep learning approaches have been developed in recent years to improve the …

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 …

Background Accurate prediction of protein–ligand binding affinity is important for lowering
the overall cost of drug discovery in structure-based drug design. For accurate predictions …

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

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

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

Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …

Accurate in silico prediction of protein–ligand binding affinity is important in the early stages
of drug discovery. Deep learning-based methods exist but have yet to overtake more …

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 …