There has recently been a rapid progress in computational methods for determining protein
targets of small molecule drugs, which will be termed as compound protein interaction (CPI) …

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary
methods to create safe and effective medicines. Although considerable progress has been …

We introduce a deep learning architecture for structure-based virtual screening that
generates fixed-sized fingerprints of proteins and small molecules by applying learnable …

Identifying potential associations between drugs and targets is a critical prerequisite for
modern drug discovery and repurposing. However, predicting these associations is difficult …

Predicting drug–target interaction using computational approaches is an important step in
drug discovery and repositioning. To predict whether there will be an interaction between a …

Existing machine learning-based approaches to investigate and design concrete mainly use
the mixture design variables to predict concrete properties and do not consider the …

The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern
drug discovery in terms of suggesting new drug candidates and repositioning old drugs …

Background During the identification of potential candidates, computational prediction of
drug-target interactions (DTIs) is important to subsequent expensive validation in wet-lab …

WRKY, a plant-specific transcription factor family, plays vital roles in pathogen defense,
abiotic stress, and phytohormone signalling. Little is known about the roles and function of …

Highlights•Computational drug repositioning is a serious support tool to shortlist candidate
drugs.•Up-to-date review of drug repositioning methodologies.•Example of a drug …