HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion

An Huang, Xiaolan Xie, Xiaojun Yao, Huanxiang Liu, Xiaoqi Wang, Shaoliang Peng

Research output: Contribution to journalArticlepeer-review


Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.

Original languageEnglish
Pages (from-to)961-971
Number of pages11
JournalJournal of Computational Biology
Issue number9
Publication statusPublished - 1 Sept 2023


  • drug-drug interaction
  • feature fusion
  • multimodal deep learning


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