Efficient discovery of anticancer peptides via cost-aware ranking learning

Efficient discovery of anticancer peptides via cost-aware ranking learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Efficient discovery of anticancer peptides via cost-aware ranking learning Ying Wang, Jianda Yue, Jiawei Xu, Zihui Chen, Tingting Li, Zhaoyang Tang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8539436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract While anticancer peptides (ACPs) have emerged as a promising class of next-generation therapeutics, efficiently identifying potent candidates across the vast peptide sequence space remains a pressing challenge. Here, we present ACPRank, a computational framework based on cost-aware ranking learning tailored for discovering potent ACPs. By integrating a unified activity scoring system encompassing multidimensional pharmacological metrics and employing a customized ranking loss, the framework directly embeds the core objective of targeting high-activity candidates under resource-limited conditions into the optimization process, thereby enabling precise candidate prioritization. Systematic screening of the human secretome facilitated the identification of two novel human-derived ACPs (termed DRP and KRP), both of which exhibited robust broad-spectrum antitumor efficacy in vitro and in vivo , alongside a favorable safety profile. Mechanistic investigations revealed that KRP induces marked cell cycle arrest by downregulating key mitotic regulators, including Aurora Kinase B (AURKB), Budding Uninhibited by Benzimidazoles 1 (BUB1), Cyclin B1 (CCNB1), and Non-SMC Condensin I Complex Subunit H (NCAPH). Overall, ACPRank enables rapid identification of highly active candidates and thus holds great potential for addressing the efficiency bottleneck in large-scale peptide screening. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Virtual drug screening Biological sciences/Cancer/Cancer therapy/Drug development Biological sciences/Drug discovery/Drug screening/Virtual screening Biological sciences/Biochemistry/Peptides anticancer peptide ACPRank ranking learning cell cycle human secretome Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8539436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573897999,"identity":"4a378e3d-2ebc-4a6a-9824-de5b3bc78326","order_by":0,"name":"Ying 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