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A computational approach to drug repurposing using graph neural networks

Doshi, S and Chepuri, SP (2022) A computational approach to drug repurposing using graph neural networks. In: Computers in Biology and Medicine, 150 .

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Official URL: https://doi.org/10.1016/j.compbiomed.2022.105992


Drug repurposing is an approach to identify new medical indications of approved drugs. This work presents a graph neural network drug repurposing model, which we refer to as GDRnet, to efficiently screen a large database of approved drugs and predict the possible treatment for novel diseases. We pose drug repurposing as a link prediction problem in a multi-layered heterogeneous network with about 1.4 million edges capturing complex interactions between nearly 42,000 nodes representing drugs, diseases, genes, and human anatomies. GDRnet has an encoder–decoder architecture, which is trained in an end-to-end manner to generate scores for drug–disease pairs under test. We demonstrate the efficacy of the proposed model on real datasets as compared to other state-of-the-art baseline methods. For a majority of the diseases, GDRnet ranks the actual treatment drug in the top 15. Furthermore, we apply GDRnet on a coronavirus disease (COVID-19) dataset and show that many drugs from the predicted list are being studied for their efficacy against the disease.

Item Type: Journal Article
Publication: Computers in Biology and Medicine
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Forecasting; Graph neural networks; Heterogeneous networks; Network layers, Computational approach; Computational pharmacology; Drug repositioning; Drug repurposing; Graph neural networks; Large database; Link prediction; Multi-layered; Prediction problem; Repurposing, Diseases
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 27 Oct 2022 09:19
Last Modified: 27 Oct 2022 09:19
URI: https://eprints.iisc.ac.in/id/eprint/77623

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