Abstract Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious
experiment via biochemical approaches. Machine learning based methods have been …

With the emergence of large-scale experimental data on genes and proteins, drug discovery
and repositioning will be more difficult in the field of biomedical research. More and more …

Targeted drugs have been applied to the treatment of cancer on a large scale, and some
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …

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 …

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF)
algorithm to predict potential drug-target interactions (DTI). The prediction procedure …

In medicine research, drug discovery aims to develop a drug to patients who will benefit from
it and try to avoid some side effects. However, the tradition experiment is time consuming …

The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …

Background Drug-target networks are receiving a lot of attention in late years, given its
relevance for pharmaceutical innovation and drug lead discovery. Different in silico …

Drug–drug interactions are one of the major concerns of drug discovery, and the accurate
prediction of drug–drug interactions is important for drug safety surveillance. However, most …

Background The key to modern drug discovery is to find, identify and prepare drug
molecular targets. However, due to the influence of throughput, precision and cost …