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, there has been a …

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 …

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 …

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 …

Background Accurately predicting the binding affinities of large sets of protein-ligand
complexes is a key challenge in computational biomolecular science, with applications in …

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

Background: Calculation of ligand-binding affinity is an open problem in computational
medicinal chemistry. The ability to computationally predict affinities has a beneficial impact …

Accurately predicting protein–ligand binding affinities is an important problem in
computational chemistry since it can substantially accelerate drug discovery for virtual …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …