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They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https:\/\/awi.cuhk.edu.cn\/\u223cdbAMP\/AVP\/.<\/jats:p>","DOI":"10.1093\/bib\/bbae208","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T06:19:16Z","timestamp":1714976356000},"source":"Crossref","is-referenced-by-count":27,"title":["A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy"],"prefix":"10.1093","volume":"25","author":[{"given":"Jiahui","family":"Guan","sequence":"first","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong, Shenzhen , 2001 Longxiang Road, 518172 Shenzhen , China"},{"name":"Kobilka Institute of Innovative Drug Discovery , School of Medicine, , 2001 Longxiang Road, 518172 Shenzhen , China"},{"name":"The Chinese University of Hong Kong , School of Medicine, , 2001 Longxiang Road, 518172 Shenzhen , 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