Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient
and successful rational drug design. Therefore, many binding affinity prediction methods …

Evaluating the protein–ligand binding affinity is a substantial part of the computer-aided drug
discovery process. Most of the proposed computational methods predict protein–ligand …

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 …

Background Computer-aided drug design provides an effective method of identifying lead
compounds. However, success rates are significantly bottlenecked by the lack of accurate …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

Computational drug discovery provides an efficient tool for helping large-scale lead
molecule screening. One of the major tasks of lead discovery is identifying molecules with …

Accurate prediction of binding affinity between protein and ligand is a very important step in
the field of drug discovery. Although there are many methods based on different …

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

Deep learning methods, which can predict the binding affinity of a drug–target protein
interaction, reduce the time and cost of drug discovery. In this study, we propose a novel …

Prediction of protein-ligand interactions is a critical step during the initial phase of drug
discovery. We propose a novel deep-learning-based prediction model based on a graph …