Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

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

Motivation The identification of novel drug–target (DT) interactions is a substantial part of the
drug discovery process. Most of the computational methods that have been proposed to …

Motivation In bioinformatics, machine learning-based methods that predict the compound–
protein interactions (CPIs) play an important role in the virtual screening for drug discovery …

Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …

Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …

Results Inspired by recent advance of information passing and aggregation techniques that
generalize the convolution neural networks to mine large-scale graph data and greatly …

Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery.
Most of the computational methods developed for predicting DTIs use binary classification …

Background Traditional methods for drug discovery are time-consuming and expensive, so
efforts are being made to repurpose existing drugs. To find new ways for drug repurposing …

The computational prediction of interactions between drugs and targets is a standing
challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction …