{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T16:59:03Z","timestamp":1765817943065,"version":"3.48.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371423"],"award-info":[{"award-number":["62371423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan","doi-asserted-by":"crossref","award":["252300421226"],"award-info":[{"award-number":["252300421226"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug\u2013target interaction (DTI) prediction is a critical task in drug discovery. However, the differences in size between drugs and proteins present significant challenges in accurately predicting binding sites. Additionally, the issue of modality imbalance, which arises from modality learning biases, undermines the contribution of multimodal representations to DTI prediction. To address these challenges, we propose DDGR-DTI, which is based on an innovative Decoupled Dual-Granularity Framework and a Rebalanced pyramid network (RPN). This framework divides the DTI task into two levels of granularity. The macro level, which decomposes it into subtasks based on modality, and the micro level further decomposes the representation within each subtask. Furthermore, the dual-stream attention module is utilized to perform fine-grained substructure-level interactions within each subtask, thereby enabling accurate identification of binding sites. Simultaneously, we employ an RPN, which effectively alleviates the bias towards the dominant modality in multimodal fusion through a hierarchical aggregation mechanism, emphasizing the synergistic advantages brought by modality balance. Benchmark results demonstrate that DDGR-DTI outperforms existing state-of-the-art models in both prediction performance and generalization ability.<\/jats:p>\n                  <jats:p>Availability: The source code and dataset can be found at https:\/\/github.com\/ZZUzy\/DDGR-DTI.<\/jats:p>","DOI":"10.1093\/bib\/bbaf670","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T13:00:55Z","timestamp":1764162055000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoupled dual-granularity rebalanced pyramid network for drug\u2013target interaction prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Zhiyuan","family":"Dong","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450000,","place":["China"]}]},{"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Dalian Medical University , 467 Zhongshan Road, Shahekou District, Dalian 116023,","place":["China"]}]},{"given":"Yang","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology , Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, Beijing 100191,","place":["China"]}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Dalian Medical University , 467 Zhongshan Road, Shahekou District, Dalian 116023,","place":["China"]}]},{"given":"Chaoyang","family":"Han","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Dalian Medical University , 467 Zhongshan Road, Shahekou District, Dalian 116023,","place":["China"]}]},{"given":"Hongfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology , Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, Beijing 100191,","place":["China"]}]},{"given":"Yajuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Radiology , Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing 100191,","place":["China"]},{"name":"Beijing Key Laboratory of Magnetic Resonance lmaging Devices and Technology , Peking University Third Hospital, No. 49 Huayuan North Road, Haidian District, Beijing 100191,","place":["China"]}]},{"given":"Wanyi","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology , Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, Beijing 100191,","place":["China"]}]},{"given":"Yanye","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology , Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, Beijing 100191,","place":["China"]}]},{"given":"Ping","family":"Ren","sequence":"additional","affiliation":[{"name":"The 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