The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

Highlights•Chemical descriptors in modeling drug-target interaction.•Modeling approaches
in drug-target interaction prediction.•Machine learning and deep learning models in drug …

Abstract Background The Drug–Target Interaction (DTI) prediction uses a drug molecule and
a protein sequence as inputs to predict the binding affinity value. In recent years, deep …

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many
methods for predicting drug targets, these methods have limitations in using discrete or …

Abstract Motivation Identification of Drug–Target Interactions (DTIs) is an essential step in
drug discovery and repositioning. DTI prediction based on biological experiments is time …

The growing quantity of public and private data sets focused on small molecules screened
against biological targets or whole organisms provides a wealth of drug discovery relevant …

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In
vitro experimental methods are expensive, laborious, and time-consuming. Deep learning …

Motivation The identification of drug–target interactions (DTIs) plays a vital role for in silico
drug discovery, in which the drug is the chemical molecule, and the target is the protein …

Motivation Accurately identifying the drug–target interactions (DTIs) is one of the crucial
steps in the drug discovery and drug repositioning process. Currently, many computational …

Identification of drug–target interactions (DTIs) is an important step in drug discovery and
drug repositioning. In recent years, graph-based methods have attracted great attention and …