RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.

ABSTRACT: Drug-drug interaction (DDI) refers to the interaction between two or more drugs in the body, altering their efficacy or pharmacokinetics. Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients. In recent years, many deep learning-based methods have been proposed to predict DDI. However, most existing computational models tend to oversimplify the fusion of drug structural and topological information, often relying on methods such as splicing or weighted summation, which fail to adequately capture the potential complementarity between structural and topological features. This loss of information may lead to models that do not fully leverage these features, thus limiting their performance in DDI prediction. To address these ch

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12512993 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature