Motivation Recently, deep learning has become the mainstream methodology for drug–
target binding affinity prediction. However, two deficiencies of the existing methods restrict …

Drug-target affinity prediction is a challenging task in drug discovery. The latest
computational models have limitations in mining edge information in molecule graphs …

Accurate and effective drug–target interaction (DTI) prediction can greatly shorten the drug
development lifecycle and reduce the cost of drug development. In the deep-learning-based …

Background Obtaining accurate drug–target binding affinity (DTA) information is significant
for drug discovery and drug repositioning. Although some methods have been proposed for …

Drug-target binding affinity prediction plays an important role in the early stages of drug
discovery, which can infer the strength of interactions between new drugs and new targets …

Predicting the docking between proteins and ligands is a crucial and challenging task for
drug discovery. However, traditional docking methods mainly rely on scoring functions, and …

Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery
and repositioning. Although deep learning methods are widely used in DTA prediction, two …

Drug–target interactions (DTIs) are a key part of drug development process and their
accurate and efficient prediction can significantly boost development efficiency and reduce …

Protein–ligand binding affinity prediction plays an important role in the field of drug
discovery. Existing deep learning-based approaches have significantly improved the …

The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical
development. The deep learning model achieves more accurate results in DTI prediction …