Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

The exponential growth of biomedical data in recent years has urged the application of
numerous machine learning techniques to address emerging problems in biology and …

Identification of new drug–target interactions (DTIs) is an important but a time-consuming
and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers …

The prediction of interactions in protein networks is very critical in various biological
processes. In recent years, scientists have focused on computational approaches to predict …

Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …

Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …

Background Technological and research advances have produced large volumes of
biomedical data. When represented as a network (graph), these data become useful for …

Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another
drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions …

This article reviews the current knowledge and recent advancements in computational
modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms …

Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug
repositioning. To reduce the experimental cost, a large number of computational …