Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …

Abstract Background Drug-drug interactions (DDIs) are one of the major concerns in drug
discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions …

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …

In silico drug-target interaction (DTI) prediction is an important and challenging problem in
biomedical research with a huge potential benefit to the pharmaceutical industry and …

Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently,
deep learning (DL) models for show promising performance for DTI prediction. However …

Background Identification of novel drug–target interactions (DTIs) is important for drug
discovery. Experimental determination of such DTIs is costly and time consuming, hence it …

The identification of drug-target interactions (DTIs) is an important process in drug
repositioning and drug discovery. However, it is very expensive and time-consuming to …

Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …

The properties of the drug may be altered by the combination, which may cause unexpected
drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs …

Motivation Identifying drug–target interactions is a crucial step for drug discovery and
design. Traditional biochemical experiments are credible to accurately validate drug–target …