{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T20:04:58Z","timestamp":1789761898729,"version":"4.0.1"},"reference-count":39,"publisher":"American Chemical Society (ACS)","issue":"9","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"name":"National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2024ZD0524300"],"award-info":[{"award-number":["2024ZD0524300"]}]},{"name":"National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2024ZD0524302"],"award-info":[{"award-number":["2024ZD0524302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate prediction of drug-target interactions is pivotal for accelerating drug discovery and drug repurposing. However, existing advanced methods often fail to effectively characterize the many-to-many interactions between drugs and targets. Furthermore, they struggle to fully mine the structural features of drugs and proteins. To address these limitations, we propose IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization. First, we construct drug, protein, and drug\u2212protein pair hypergraphs, where shared-drug and shared-target relationships are used to characterize multi-target activity and shared-target regulation. We then optimize hyperedge weights using a Laplacian matrix to enhance biologically meaningful high-order associations and suppress potential noise. Second, we use simplified graph convolution and graph convolutional network to extract global and local features, enabling efficient modeling of multi-level semantic information for drugs and targets. Next, we introduce a cross-attention mechanism and a dynamic gating module to perform fine-grained fusion of multi-channel features, improving the representation of cross-modal information interactions. Finally, we jointly train the model with contrastive learning and cross-entropy loss to enhance the consistency and discriminability of the learned representations. IHLO-DTI achieves mean AUROC and AUPR values of 0.9777 and 0.9716, respectively, on two public datasets. IHLO-DTI can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness. It provides a more reliable computational tool for clinical drug screening, repurposing, and precision medicine research.<\/jats:p>","DOI":"10.1021\/acssynbio.6c00326","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:02:50Z","timestamp":1786546970000},"page":"3686-3701","source":"Crossref","is-referenced-by-count":0,"title":["IHLO-DTI: Drug-Target Interaction Prediction Based on Improved Hypergraph Neural Network and Laplacian Matrix Optimization"],"prefix":"10.1021","volume":"15","author":[{"given":"Guolongwei","family":"Dai","sequence":"first","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5374-4226","authenticated-orcid":true,"given":"Tao","family":"Luo","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dandan","family":"Li","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeping","family":"Liu","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Gu","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjie","family":"Fan","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Wei","sequence":"additional","affiliation":[{"name":"West China Hospital of Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610041"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengyi","family":"Wang","sequence":"additional","affiliation":[{"name":"West China Hospital of Sichuan University , , , ,","place":["Chengdu, Sichuan, China, 610041"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"issue":"1","key":"2026091815075702900_cit1","doi-asserted-by":"publisher","DOI":"10.2174\/1570163818666210316114331","article-title":"Drug repositioning: A unique approach to refurbish drug discovery","volume":"19","author":"Kale","year":"2022","journal-title":"Curr. 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