{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T04:37:22Z","timestamp":1792816642240,"version":"4.5.0"},"reference-count":32,"publisher":"Wiley","issue":"20","license":[{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20530"],"award-info":[{"award-number":["U23A20530"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["chemistry-europe.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ChemMedChem"],"published-print":{"date-parts":[[2026,10,28]]},"abstract":"<jats:p>\n                    <jats:bold>Motivation:<\/jats:bold>\n                    Dipeptidyl peptidase IV (DPP\u2010IV) is a key therapeutic target for type 2 diabetes, and its inhibitory peptides are a promising class of therapeutic agents. However, the discovery of these promising drugs has been restricted, as many existing computational predictors are based on datasets that have remained static for several years.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Here, we first presented a newly curated and expanded benchmark dataset, then introduced Bilingua\u2010DPP\u2010IV, a bimodal deep learning framework that integrates features from both amino acid sequences and molecular structures. Our approach leveraged embeddings from the large\u2010scale pretrained models ESM C and Uni\u2010Mol2, processed through a convolutional neural network with an optimized gated fusion mechanism. On an independent test set, the Bilingua\u2010DPP\u2010IV ensemble model achieved a Matthews correlation coefficient of 0.764 and an area under the receiver operating characteristic curve of 0.963. On an external validation set, the model also demonstrated an accuracy of 0.898, exceeding the performance of existing state\u2010of\u2010the\u2010art predictors, such as StructuralDPPIV.\n                  <\/jats:p>","DOI":"10.1002\/cmdc.70551","type":"journal-article","created":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:58:07Z","timestamp":1792814287000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bilingua\u2010DPP\u2010IV: A Bimodal Fusion Convolutional Neural Network Model for Prediction of Dipeptidyl Peptidase IV Inhibitory Peptides"],"prefix":"10.1002","volume":"21","author":[{"given":"Zhixing","family":"Zhang","sequence":"first","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiule","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Duan","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"Fang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changda","family":"Gong","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-9836","authenticated-orcid":false,"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9648-844X","authenticated-orcid":false,"given":"Guixia","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2340-1109","authenticated-orcid":false,"given":"Yun","family":"Tang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy East China University of Science and Technology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfaf177"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"135249","DOI":"10.1016\/j.molstruc.2023.135249","article-title":"Medicinal Chemistry of Non\u2010Peptidomimetic Dipeptidyl Peptidase IV (DPP IV) Inhibitors for Treatment of Type\u20102 Diabetes Mellitus: Insights on Recent Development","volume":"1284","author":"Ambhore J. P.","year":"2023","journal-title":"Journal of Molecular Structure"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1021\/acs.jproteome.0c00590","article-title":"iDPPIV\u2010SCM: A Sequence\u2010Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP\u2010IV) Inhibitory Peptides Using a Scoring Card Method","volume":"19","author":"Charoenkwan P.","year":"2020","journal-title":"Journal of Proteome Research"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ymeth.2021.12.001","article-title":"StackDPPIV: A Novel Computational Approach for Accurate Prediction of Dipeptidyl Peptidase IV (DPP\u2010IV) Inhibitory Peptides","volume":"204","author":"Charoenkwan P.","year":"2022","journal-title":"Methods"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","first-page":"39662","DOI":"10.1021\/acsomega.3c05571","article-title":"Exploration of DPP\u2010IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline that Identifies the Restriction Enzyme Cutting Site","volume":"8","author":"Guan C.","year":"2023","journal-title":"ACS Omega"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"btae057","DOI":"10.1093\/bioinformatics\/btae057","article-title":"StructuralDPPIV: A Novel Deep Learning Model Based on Atom Structure for Predicting Dipeptidyl Peptidase\u2010IV Inhibitory Peptides","volume":"40","author":"Wang D.","year":"2024","journal-title":"Bioinformatics"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3389\/fendo.2018.00329","article-title":"In Silico Approaches Applied to the Study of Peptide Analogs of Ile\u2010Pro\u2010Ile in Relation to Their Dipeptidyl Peptidase IV Inhibitory Properties","volume":"9","author":"Nongonierma A. 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