{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:50:36Z","timestamp":1773687036526,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":15,"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":["82495183"],"award-info":[{"award-number":["82495183"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32571204 to J.L."],"award-info":[{"award-number":["32571204 to J.L."]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>G protein-coupled receptors (GPCRs) are among the most important drug targets, and peptide therapeutics are rapidly emerging. However, accurate prediction of peptide\u2013GPCR interactions (PepGI) remains challenging due to the scarcity of high-quality data and the poor generalization of existing drug\u2013target interaction (DTI) models, which are largely trained on small molecule data. Here, we introduce a progressive fine-tuning framework with a dynamic parameter selection strategy that adaptively selects critical fine-tuning parameters using Fisher information. Our method begins with pretraining on a large small molecule-GPCR dataset, followed by intermediate fine-tuning on peptide\u2013target data to alleviate the representation mismatch across heterogeneous ligand modalities. Finally, the task-specific fine-tuning is performed on the low-resource PepGI scenario. Extensive experiments show that our approach significantly outperforms baselines across multiple evaluation metrics, and exhibits robust generalization under few-shot and practical cold-start settings. Overall, this work offers an effective solution for low-resource peptide\u2013GPCR prediction and presents a transferable framework for cross-structure DTI modeling.<\/jats:p>","DOI":"10.1093\/bib\/bbag116","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T12:35:27Z","timestamp":1771936527000},"source":"Crossref","is-referenced-by-count":0,"title":["A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide\u2013GPCR interaction prediction"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7314-6472","authenticated-orcid":false,"given":"Mingqing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , No. 100 Fuxing Road, High-Tech District, Hefei, 230026 Anhui,","place":["China"]},{"name":"Institute of Artificial Intelligence, Hefei Comprehensive National Science Center , No. 5089 Wangjiang West Road, Shushan District, Hefei, 230026 Anhui,","place":["China"]}]},{"given":"Jinhui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China , No. 100 Fuxing Road, High-Tech District, Hefei, 230026 Anhui,","place":["China"]},{"name":"Institute of Artificial Intelligence, Hefei Comprehensive National Science Center , No. 5089 Wangjiang West Road, Shushan District, Hefei, 230026 Anhui,","place":["China"]}]},{"given":"Ji","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology , No. 1 Gongchang Road, Guangming District, Shenzhen, 518107 Guangdong,","place":["China"]},{"name":"Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China , No. 96 Jinzhai Road, Baohe District, Hefei, 230026 Anhui,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"2026031613535252900_ref1","doi-asserted-by":"publisher","first-page":"S55","DOI":"10.1038\/d41586-018-05267-x","article-title":"How artificial intelligence is changing drug discovery","volume":"557","author":"Fleming","year":"2018","journal-title":"Nature"},{"key":"2026031613535252900_ref2","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.omtn.2023.02.019","article-title":"Artificial intelligence for drug discovery: resources, methods, and applications","volume":"31","author":"Chen","year":"2023","journal-title":"Mol Ther Nucleic Acids"},{"key":"2026031613535252900_ref3","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharm Sin 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