{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:35:23Z","timestamp":1782545723252,"version":"3.54.5"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004569","name":"Ministry of Science and Higher Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004569","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AGH University in Krakow"},{"name":"Excellence Initiative\u2014Research University (IDUB) for AGH University of Krakow"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding peptide properties is often assumed to require modeling long-range molecular interactions, motivating complex graph neural networks and pretrained transformers. Whether such long-range dependencies are essential remains unclear. We investigate if simple, domain-specific molecular fingerprints can capture peptide function without these assumptions. Atomic-level representations aim to provide richer information than purely sequence-based models and better efficiency than structural ones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across 132 datasets, including LRGB and five additional peptide benchmarks, models using count-based ECFP, Topological Torsion, and RDKit fingerprints with LightGBM achieve state-of-the-art accuracy. Despite encoding only short-range molecular features, these models outperform GNNs and transformer-based approaches. Control experiments confirm that fingerprints, though inherently local, suffice for robust peptide property prediction. Our results challenge the presumed necessity of long-range interaction modeling and highlight molecular fingerprints as efficient, interpretable, and lightweight alternatives.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>All code and data are available on GitHub and Zenodo: https:\/\/github.com\/scikit-fingerprints\/peptides_molecular_fingerprints_classification \u00a0https:\/\/doi.org\/10.5281\/zenodo.19388783<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag179","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:28:19Z","timestamp":1775561299000},"source":"Crossref","is-referenced-by-count":2,"title":["Molecular fingerprints are strong models for peptide function 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