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 …

Experimental determination of drug-target interactions is expensive and time-consuming.
Therefore, there is a continuous demand for more accurate predictions of interactions using …

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

Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery.
Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs …

Drug–drug interaction (DDI) prediction prepares substantial information for drug discovery.
As the exact prediction of DDIs can reduce human health risk, the development of an …

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 …

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 …

Motivation: In silico methods provide efficient ways to predict possible interactions between
drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently …

Interactions between drugs and target proteins provide important information for the drug
discovery. Currently, experiments identified only a small number of drug-target interactions …

Motivation: The in silico prediction of potential interactions between drugs and target
proteins is of core importance for the identification of new drugs or novel targets for existing …