The prediction of drug-target affinity (DTA) plays an increasingly important role in drug
discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and …

Identification of drug-target interaction (DTI) is the most important issue in the broad field of
drug discovery. Using purely biological experiments to verify drug-target binding profiles …

Exiting computational models for drug–target binding affinity prediction have much room for
improvement in prediction accuracy, robustness and generalization ability. Most deep …

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …

In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an
important role in virtual screening of drug discovery. DTI prediction, which have been treated …

Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …

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 …

Identifying drug-target affinity (DTA) has great practical importance in the process of
designing efficacious drugs for known diseases. Recently, numerous deep learning-based …

Computationally predicting drug-target binding affinity (DTA) has attracted increasing
attention due to its benefit for accelerating drug discovery. Currently, numerous deep …

Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …