{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:10:16Z","timestamp":1783437016637,"version":"3.54.6"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011597","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000}}],"reference-count":72,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["grant nos. 62072384, 61872309, 62072385, 61772441"],"award-info":[{"award-number":["grant nos. 62072384, 61872309, 62072385, 61772441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Lab","award":["2022RD0AB02"],"award-info":[{"award-number":["2022RD0AB02"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces <jats:bold>D<\/jats:bold>rug-<jats:bold>S<\/jats:bold>ubstructures relationship into <jats:bold>M<\/jats:bold>olecular interaction <jats:bold>N<\/jats:bold>etworks to construct the micro-to-macro drug centric heterogeneous network (<jats:bold>DSMN<\/jats:bold>), and develop a multi-branches <jats:bold>H<\/jats:bold>yper<jats:bold>G<\/jats:bold>raph learning model, called <jats:bold>HGDrug<\/jats:bold>, for <jats:bold>Drug<\/jats:bold> multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011597","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T18:42:41Z","timestamp":1699900961000},"page":"e1011597","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":24,"title":["A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical 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