MKG-FENN: A Multimodal Knowledge Graph Fused End-To-End Neural Network for Accurate Drug-Drug Interaction Prediction
Document Type
Conference Paper
Publication Date
2024
DOI
10.1609/aaai.v38i9.28887
Publication Title
Proceedings of the AAAI Conference on Artificial Intelligence
Volume
38
Issue
9
Pages
10216-10224
Conference Name
Thirty-Eighth AAAI Conference on Artificial Intelligence, February 20-27, 2024, Vancouver, Canada
Abstract
Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI events. However, DDI events are associated with complex relationships and mechanisms among drugs, targets, enzymes, transporters, molecular structures, etc. Existing approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. Different from them, this paper proposes a Multimodal Knowledge Graph Fused End-to-end Neural Network (MKGFENN) that consists of two main parts: multimodal knowledge graph (MKG) and fused end-to-end neural network (FENN). First, MKG is constructed by comprehensively exploiting DDI events-associated relationships and mechanisms from four knowledge graphs of drugs-chemical entities, drug-substructures, drugs-drugs, and molecular structures. Correspondingly, a four channels graph neural network is designed to extract high-order and semantic features from MKG. Second, FENN designs a multi-layer perceptron to fuse the extracted features by end-to-end learning. With such designs, the feature extractions and fusions of DDI events are guaranteed to be comprehensive and optimal for prediction. Through extensive experiments on real drug datasets, we demonstrate that MKG-FENN exhibits high accuracy and significantly outperforms state-of-the-art models in predicting DDI events. The source code and supplementary file of this article are available on: https://github.com/wudi1989/MKG-FENN.
Rights
Copyright © 2024, Association for the Advancement of Artificial Intelligence. All rights reserved.
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Metadata record included in accordance with publisher policy.
Original Publication Citation
Wu, D., Sun, W., He, Y., Chen, Z., & Luo, X. (2024). MKG-FENN: A multimodal knowledge graph fused end-to-end neural network for accurate drug–drug interaction prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10216-10224. https://doi.org/10.1609/aaai.v38i9.28887
Repository Citation
Wu, D., Sun, W., He, Y., Chen, Z., & Luo, X. (2024). MKG-FENN: A multimodal knowledge graph fused end-to-end neural network for accurate drug–drug interaction prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10216-10224. https://doi.org/10.1609/aaai.v38i9.28887
ORCID
0000-0002-5357-6623 (He)