{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:22:23Z","timestamp":1792790543927,"version":"4.5.0"},"reference-count":69,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Background: Machine learning models have been shown to be a time-saving and cost-effective tool for peptide-based drug discovery. In this regard, different graph learning-driven frameworks have been introduced to exploit graph representations derived from predicted peptide structures. Such graphs are always derived by applying a Euclidean distance threshold between amino acid pairs, despite the fact that there is no evidence other than intuitive reasoning that supports the Euclidean distance as the most suitable. Objective: In this work, we examined the use of different distance functions to derive graph representations from predicted peptide structures to train deep graph learning-based models to predict antiviral peptides. Methods: To this end, we first analyzed how differently the closeness of the amino acids is characterized by different distance functions. Then, we studied the similarity between the graphs derived with several distance functions, as well as between them and random graphs. Finally, we trained several models with the best graph representations and analyzed how different they are regarding their predictions. Comparisons regarding state-of-the-art models were also performed. Results and Conclusion: We demonstrated that only using Euclidean distance thresholds is not sufficient criterion to build graphs representing structural features of predicted peptide structures, since other distance functions enabled building dissimilar graphs codifying different chemical spaces, which were useful in the construction of better discriminative models.<\/jats:p>","DOI":"10.3390\/antibiotics15010039","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T11:11:46Z","timestamp":1767352306000},"page":"39","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1005-1995","authenticated-orcid":false,"given":"Greneter","family":"Cordoves-Delgado","sequence":"first","affiliation":[{"name":"Centro de Nanociencias y Nanotecnolog\u00eda, Universidad Nacional Aut\u00f3noma de Mexico, Km. 107 Carretera Tijuana-Ensenada, Ensenada 22860, Baja California, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3962-7658","authenticated-orcid":false,"given":"C\u00e9sar R.","family":"Garc\u00eda-Jacas","sequence":"additional","affiliation":[{"name":"Investigador por Mexico, Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda e Innovaci\u00f3n (Secihti), Ciudad de Mexico 03940, Mexico"},{"name":"Tecnol\u00f3gico Nacional de Mexico, Instituto Tecnol\u00f3gico de M\u00e9rida, Unidad de Posgrado e Investigaci\u00f3n, Av. Tecnol\u00f3gico, Km. 4.5 S\/N, M\u00e9rida 97000, Yucat\u00e1n, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2721-1142","authenticated-orcid":false,"given":"Yovani","family":"Marrero-Ponce","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Ju\u00e1rez, Ciudad de Mexico 03920, Mexico"},{"name":"Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades M\u00e9dicas, Universidad San Francisco de Quito (USFQ), Quito 170157, Pichincha, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8497-2821","authenticated-orcid":false,"given":"Sergio A.","family":"Aguila","sequence":"additional","affiliation":[{"name":"Centro de Nanociencias y Nanotecnolog\u00eda, Universidad Nacional Aut\u00f3noma de Mexico, Km. 107 Carretera Tijuana-Ensenada, Ensenada 22860, Baja California, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0092-3636","authenticated-orcid":false,"given":"Gabriel","family":"Lizama-Uc","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico Nacional de Mexico, Instituto Tecnol\u00f3gico de M\u00e9rida, Unidad de Posgrado e Investigaci\u00f3n, Av. Tecnol\u00f3gico, Km. 4.5 S\/N, M\u00e9rida 97000, Yucat\u00e1n, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Taylor, M.W. (2014). Introduction: A Short History of Virology. Viruses and Man: A History of Interactions, Springer International Publishing.","DOI":"10.1007\/978-3-319-07758-1_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41564-018-0296-2","article-title":"Tracking virus outbreaks in the twenty-first century","volume":"4","author":"Grubaugh","year":"2019","journal-title":"Nat. Microbiol."},{"key":"ref_3","unstructured":"CDC (2025, December 15). History of Ebola Disease Outbreaks, Available online: https:\/\/www.cdc.gov\/ebola\/outbreaks\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huremovi\u0107, D. (2019). Brief history of pandemics (pandemics throughout history). 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