Despite increasing mechanistic understanding, undetected and underrecognized drug–drug
interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of …

The objective of this article is to review the application of informatics-driven approaches in
the pharmacovigilance field with focus on drug-drug interaction (DDI) safety signal discovery …

Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing
concern in both academia and industry. Many DDIs have been reported, but the underlying …

Recently, deep learning has become the mainstream methodology for Compound-Protein
Interaction (CPI) prediction. However, the existing compound-protein feature extraction …

Objective The coronavirus disease 2019 (COVID-19) health crisis that began at the end of
2019 made researchers around the world quickly race to find effective solutions. Related …

Predicting the target-drug interactions (DITs) is of great important for screening new drug
candidate and understanding biological processes. However, identifying the drug-target …

As more drugs are developed and the incidence of polypharmacy increases, it is becoming
critically important to anticipate potential DDIs before they occur in the clinic, along with …

While computational methods offer great potential in predicting drug-drug interactions
(DDIs), such predictions as of yet have limited utility in supporting clinical decision-making; …

Objectives Orally disintegrating tablets (ODTs) can be utilized without any drinking water;
this feature makes ODTs easy to use and suitable for specific groups of patients. Oral …

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects
caused by DDIs to a certain extent. This paper proposes the new hybrid method for …