{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:50:27Z","timestamp":1791946227488,"version":"4.3.4"},"reference-count":107,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https:\/\/research.timmons.eu\/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.<\/jats:p>","DOI":"10.1093\/bib\/bbab258","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T15:12:47Z","timestamp":1624029167000},"source":"Crossref","is-referenced-by-count":81,"title":["ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides"],"prefix":"10.1093","volume":"22","author":[{"given":"Patrick Brendan","family":"Timmons","sequence":"first","affiliation":[{"name":"UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandralal M","family":"Hewage","sequence":"additional","affiliation":[{"name":"UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"2021110815090603000_ref1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/1745-6150-1-29","article-title":"The ancient Virus World and evolution of cells","volume":"1","author":"Koonin","year":"2006","journal-title":"Biol Direct"},{"key":"2021110815090603000_ref2","doi-asserted-by":"crossref","first-page":"12411","DOI":"10.1073\/pnas.210382297","article-title":"Emerging viral diseases","volume":"97","author":"Nichol","year":"2000","journal-title":"Proc Natl Acad Sci U S 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