The core of large-scale drug virtual screening is to select the binders accurately and
efficiently with high affinity from large libraries of small molecules in which non-binders are …

Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted
great interest in recent years. However, data-driven AI models unequivocally suffer from a …

Motivation Computational approaches for identifying the protein–ligand binding affinity can
greatly facilitate drug discovery and development. At present, many deep learning-based …

In this work, we present graph-convolutional neural networks for the prediction of binding
constants of protein–ligand complexes. We derived the model using multi task learning …

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

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

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 …

Motivation An essential part of drug discovery is the accurate prediction of the binding affinity
of new compound–protein pairs. Most of the standard computational methods assume that …

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

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 …