The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Background In recent years, since the molecular docking technique can greatly improve the
efficiency and reduce the research cost, it has become a key tool in computer-assisted drug …

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 …

Computational prediction of the interaction between drugs and targets is a standing
challenge in the field of drug discovery. A number of rather accurate predictions were …

In drug discovery, network‐based approaches are expected to spotlight our understanding
of drug action across multiple layers of information. On one hand, network pharmacology …

Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …

Computationally predicting drug–target interactions is useful to select possible drug (or
target) candidates for further biochemical verification. We focus on machine learning-based …

In silico prediction of drug-target interactions from heterogeneous biological data can
advance our system-level search for drug molecules and therapeutic targets, which efforts …

Motivation: Computational prediction of compound–protein interactions (CPIs) is of great
importance for drug design and development, as genome-scale experimental validation of …

Drug target interaction is a prominent research area in the field of drug discovery. It refers to
the recognition of interactions between chemical compounds and the protein targets in the …