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METHODS article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1525029
This article is part of the Research Topic Exploring Untapped Potential: Innovations in Drug Repurposing View all 6 articles

KGRDR: A deep learning model based on graph regularized integration and graph knowledge for drug repositioning

Provisionally accepted
Huimin Luo Huimin Luo 1Hui Yang Hui Yang 1Ge Zhang Ge Zhang 1Jianlin Wang Jianlin Wang 1Junwei Luo Junwei Luo 2Chaokun Yan Chaokun Yan 1*
  • 1 School of Computer and Information Engineering, Henan University, Kaifeng, China
  • 2 College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan Province, China

The final, formatted version of the article will be published soon.

    Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug similarity and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases.Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related interactions. Next, these two feature representations are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted through graph convolutional networks.Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

    Keywords: Drug Repositioning, drug-disease interaction prediction, Multi-similarity fusion, biomedical knowledge graph, Feature fusion

    Received: 08 Nov 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Luo, Yang, Zhang, Wang, Luo and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chaokun Yan, School of Computer and Information Engineering, Henan University, Kaifeng, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.