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@article{205804,
        author = {Anuj Gagare and Paras Tak and Aditi Thorat and Shlok Kaule and Prof. Urmila Pawar},
        title = {A Graph Attention Network-Based Approach for Drug Repurposing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9373-9381},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205804},
        abstract = {Drug repurposing, an innovative approach that aims to find new uses of existing medications, can be considered an efficient solution to facilitate drug discovery, cutting down dramatically on its expenses, time consumption, and risks associated with developing novel drugs from scratch. The abundance of biomedical data in combination with recent advances in data processing enabled scientists to develop various computational methods to analyze available data and derive drug repurposing hypotheses. The present survey provides an overview of modern computational drug repurposing techniques with particular emphasis on those based on network medicine and machine learning. We discuss popular methods, including pathway-based prediction, network proximity-based analysis, matrix factorization, as well as deep learning-based approaches, especially Graph Neural Networks (GNN). The paper highlights how heterogeneous biomedical data is used by various models to derive novel repurposing hypotheses. Furthermore, we point out major challenges in the field, including problems related to data integration, model generalization, and explainability, and propose possible directions for further research.},
        keywords = {Computational Drug Discovery, Graph Attention Networks, Network-based Prediction, Heterogeneous Graphs, Machine Learning.},
        month = {June},
        }