MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder

Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitiga...

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Published in:Computer methods in biomechanics and biomedical engineering Vol. 28; no. 7; pp. 1098 - 1110
Main Authors: Deng, Zengqian, Xu, Jie, Feng, Yinfei, Dong, Liangcheng, Zhang, Yuanyuan
Format: Journal Article
Language:English
Published: England Taylor & Francis 19.05.2025
Subjects:
Algorithms
Artificial intelligence
asymmetric drug-drug interaction
Autoencoder
Drug Interactions
multimodal
variational autoencoder
multimodal
asymmetric drug-drug interaction
Artificial intelligence
variational autoencoder
ISSN:1025-5842, 1476-8259, 1476-8259
Online Access:Get full text
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Summary:Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.
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ISSN:1025-5842
1476-8259
1476-8259
DOI:10.1080/10255842.2024.2311315