The prediction of drug–target interactions (DTI) is a crucial preliminary stage in drug
discovery and development, given the substantial risk of failure and the prolonged validation …

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

Compound–protein interactions (CPI) play significant roles in drug development. To avoid
side effects, it is also crucial to evaluate drug selectivity when binding to different targets …

Motivation Accurate prediction of drug–target binding affinity (DTA) is crucial for drug
discovery. The increase in the publication of large-scale DTA datasets enables the …

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery.
Computerized virtual screening techniques have been used for DTA prediction, greatly …

In the field of drug discovery, the large-scale prediction of drug-target affinity (DTA) is
essential. Despite recent advancements in deep learning enhancing DTA prediction, many …

Molecular property prediction is an important task in drug discovery, and with help of self-
supervised learning methods, the performance of molecular property prediction could be …

The prediction of drug–target interaction (DTI) is crucial to drug discovery. Although the
interactions between the drug and target can be accurately verified by traditional …

Recently, deep learning has become the essential methodology for Drug–Target Interaction
(DTI) prediction. However, the existing learning-based representation methods embed the …

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 …