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 …

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

Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning
as it reduces experimental validation costs if done right. Thus, developing in-silico methods …

Identifying interactions between known drugs and targets is a major challenge in drug
repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive …

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

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

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

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 …

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …