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 …

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 …

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 pharmaceutical sciences, a crucial step of the drug discovery process is the identification
of drug-target interactions. However, only a small portion of the drug-target interactions have …

Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

Drug–target interactions (DTIs) are central to current drug discovery processes and public
health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse …

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

As a traditional medical intervention in Asia and a complementary and alternative medicine
in western countries, traditional Chinese medicine (TCM) has attracted global attention in …

We address the problem of predicting new drug-target interactions from three inputs: known
interactions, similarities over drugs and those over targets. This setting has been considered …

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 …