Drug design is a costly and time-consuming process, often taking more than 12 years and
costing up to billions of dollars. The COVID-19 pandemic has signified the urgent need for …

Being able to identify regions within or around proteins, to which ligands can potentially
bind, is an essential step to develop new drugs. Binding site identification methods can now …

Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for
drug repositioning and virtual drug screening. Existing approaches transform a 3D protein …

Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Most existing deep-learning methods consider a protein as a 3D image by spatially …

Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for
drug repositioning and virtual drug screening. Existing approaches usually transform a 3D …

Accurately predicting the binding affinity between proteins and ligands is crucial for drug
discovery. Recent advances in graph neural networks (GNNs) have made significant …

Predicting Protein-ligand binding affinities using Deep Learning can significantly shorten the
drug development cycle. Recently, Graph neural network models have been developed, and …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

In drug design, compound potency prediction is a popular machine learning application.
Graph neural networks (GNNs) predict ligand affinity from graph representations of protein …

Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …