LLM-Enhanced Multimodal Framework for Drug-Drug Interaction Prediction.

ABSTRACT: Background: Drug-drug interactions (DDIs) involve pharmacokinetic or pharmacodynamic changes that occur when multiple drugs are co-administered, potentially leading to reduced efficacy or adverse effects. As polypharmacy becomes more prevalent, especially among patients with chronic diseases, scalable and accurate DDI prediction has become increasingly important. Although numerous computational approaches have been proposed to predict DDIs using various modalities such as chemical structure and biological networks, the intrinsic heterogeneity of these data complicates unified modeling; Methods: We address this challenge with a multimodal deep learning framework that integrates three complementary, heterogeneous modalities: (i) chemical structure, (ii) BioBERT-derived semantic

SUBMITTER: Im S 

PROVIDER: S-EPMC12561088 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature