{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T15:02:24Z","timestamp":1788274944278,"version":"build-2803163510"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-scale protein\u2013protein interaction networks with a multi-modal graph neural network (GNN) to identify and prioritize multi-target drug repurposing candidates for PD. Network analysis and advanced clustering methods delineated functional modules, and a novel Functional Centrality Index was employed to pinpoint key nodes within the PD interactome. The GNN model, incorporating molecular descriptors, network topology, and uncertainty quantification, predicted candidate drugs that simultaneously target critical proteins implicated in lysosomal dysfunction, mitochondrial impairment, synaptic disruption, and neuroinflammation. Among the top hits were compounds such as dithiazanine, ceftolozane, DL-\u03b1-tocopherol, bromisoval, imidurea, medronic acid, and modufolin. These findings provide mechanistic insights into PD pathology and demonstrate that a polypharmacology approach can reveal repurposing opportunities for existing drugs. Our results highlight the potential of network-based deep learning frameworks to accelerate the discovery of multi-target therapies for PD and other multifactorial neurodegenerative diseases.<\/jats:p>","DOI":"10.3390\/ijms26094453","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T11:46:19Z","timestamp":1746618379000},"page":"4453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Multi-Modal Graph Neural Network Framework for Parkinson\u2019s Disease Therapeutic Discovery"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7061-2534","authenticated-orcid":false,"given":"\u00d6mer","family":"Akg\u00fcller","sequence":"first","affiliation":[{"name":"Faculty of Science, Department of Mathematics, Mugla Sitki Kocman University, Mugla 48000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1465-7153","authenticated-orcid":false,"given":"Mehmet Ali","family":"Balc\u0131","sequence":"additional","affiliation":[{"name":"Faculty of Science, Department of Mathematics, Mugla Sitki Kocman University, Mugla 48000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1165-1389","authenticated-orcid":false,"given":"Gabriela","family":"Cioca","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Preclinical Department, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Keane, H., Ryan, B.J., Jackson, B., Whitmore, A., and Wade-Martins, R. 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