Drug discovery, which aids to identify potential novel treatments, entails a broad range of
fields of science, including chemistry, pharmacology, and biology. In the early stages of drug …

As a necessary process of modern drug development, finding a drug compound that can
selectively bind to a specific protein is highly challenging and costly. Exploring drug‐target …

The identification of drug–target relations (DTRs) is substantial in drug development. A large
number of methods treat DTRs as drug-target interactions (DTIs), a binary classification …

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

Background Accurate identification of potential interactions between drugs and protein
targets is a critical step to accelerate drug discovery. Despite many relative experimental …

Background A critical aspect of in silico drug discovery involves the prediction of drug-target
affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and …

Drug-target interactions provide insight into the drug-side effects and drug repositioning.
However, wet-lab biochemical experiments are time-consuming and labor-intensive, and …

Drug–target binding affinity (DTA) prediction has drawn increasing interest due to its
substantial position in the drug discovery process. The development of new drugs is costly …

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

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 …