Proceedings

MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events

Tengfei Lyu, Jianliang Gao, Ling Tian, Zhao Li, Peng Zhang, Ji Zhang

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3536-3542.

https://doi.org/10.24963/ijcai.2021/487

Keywords

  • Machine Learning Applications: Bio/Medicine
  • Multidisciplinary Topics and Applications: Biology and Medicine
  • Multidisciplinary Topics and Applications: AI for Life Science

Abstract

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

Tengfei Lyu, Jianliang Gao, Ling Tian, Zhao Li, Peng Zhang, and Ji Zhang. 2021. MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3536–3542. https://doi.org/10.24963/ijcai.2021/487
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards