Accurate determination of target-ligand interactions is crucial in the drug discovery process.
In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting …

We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

Predicting accurate protein–ligand binding affinities is an important task in drug discovery
but remains a challenge even with computationally expensive biophysics-based energy …

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

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 …

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force
search over a compound database is financially infeasible. We have witnessed the …

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 …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …

Abstract Development of deep-learning models for intermolecular noncovalent (NC)
interactions between proteins and ligands has great potential in the chemical and …