{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:15:54Z","timestamp":1753892154751,"version":"3.41.2"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Drug development is a complex and lengthy process, and drug-disease association prediction aims to significantly improve research efficiency and success rates by precisely identifying potential associations. However, existing methods for drug-disease association prediction still face limitations in feature representation, feature integration, and generalization capabilities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To address these challenges, we propose a novel model named AMFGNN (Adaptive Multi-View Fusion Graph Neural Network). This model leverages an adaptive graph neural network and a graph attention network to extract drug features and disease features, respectively. These features are then used as the initial representations of nodes in the drug-disease association network to enable efficient information fusion. Additionally, the model incorporates a contrastive learning mechanism, which enhances the similarity and differentiation between drugs and diseases through cross-view contrastive learning, thereby improving the accuracy of association prediction. Furthermore, a Kolmogorov-Arnold network is employed to perform weighted fusion of various final features, optimizing prediction performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>AMFGNN demonstrates a significant advantage in predictive performance, achieving an average AUC value of 0.9453, which reflects the model\u2018s high accuracy in prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Cross-validation results across multiple datasets indicate that AMFGNN outperforms seven advanced drug-disease association prediction methods. Additionally, case studies on Hepatoblastoma, asthma and Alzheimer\u2018s disease further confirm the model\u2018s effectiveness and potential value in real-world applications.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1543966","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T05:32:01Z","timestamp":1745818321000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction"],"prefix":"10.3389","volume":"16","author":[{"given":"Fang","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojing","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixia","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengnan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"742408","DOI":"10.3389\/fnagi.2022.742408","article-title":"Alzheimer\u2019s disease: an update and insights into pathophysiology","volume":"14","author":"Abubakar","year":"2022","journal-title":"Front. aging Neurosci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"D1160","DOI":"10.1093\/nar\/gkaa997","article-title":"Drugcentral 2021 supports drug discovery and repositioning","volume":"49","author":"Avram","year":"2021","journal-title":"Nucleic acids Res."},{"key":"B3","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.3390\/electronics12122688","article-title":"Mkgcn: multi-modal knowledge graph convolutional network for music recommender systems","volume":"12","author":"Cui","year":"2023","journal-title":"Electronics"},{"key":"B4","doi-asserted-by":"publisher","first-page":"784171","DOI":"10.3389\/fphar.2021.784171","article-title":"Dda-skf: predicting drug\u2013disease associations using similarity kernel fusion","volume":"12","author":"Gao","year":"","journal-title":"Front. 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