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 …

Prediction of the drug–target affinity (DTA) plays an important role in drug discovery. Existing
deep learning methods for DTA prediction typically leverage a single modality, namely …

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due
to the limited computational resources in practical applications and is a crucial basis for drug …

Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …

The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug
discovery, for which computational predictive approaches have been developed. As a …

Motivation The capability to predict the potential drug binding affinity against a protein target
has always been a fundamental challenge in silico drug discovery. The traditional …

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming
and costly to determine DTI experimentally. Hence, it is necessary to develop computational …

The computational prediction of novel drug-target interactions (DTIs) may effectively speed
up the process of drug repositioning and reduce its costs. Most previous methods integrated …

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

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 …