From Sequence to Hit: Reliable Virtual Screening via Interaction Entropy Enables HCAR1 Antagonist Discovery

From Sequence to Hit: Reliable Virtual Screening via Interaction Entropy Enables HCAR1 Antagonist Discovery | 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 From Sequence to Hit: Reliable Virtual Screening via Interaction Entropy Enables HCAR1 Antagonist Discovery Qingchao Jiang, Liang Li, Rongchao Wang, Wenli Gu, Keru Wu, Xiaoyu You, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9324746/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 Sequence-based virtual screening has emerged as a scalable approach for drug discovery by enabling compound prioritization directly from protein sequences without requiring 3D structures. However, its practical application remains limited by unreliable predictions on new targets and unexplored chemical space, largely due to the uncertainty introduced when inferring structure-dependent protein–ligand interactions from sequence information alone. Here, we propose to quantify interaction uncertainty using an entropy-based estimator and leverage an ensemble mechanism to reconcile complementary information across heterogeneous sequence-based interaction predictors. Based on this, we propose ReqVS, a reliability-aware sequence-based virtual screening framework that prioritizes compounds maximizing predicted interaction scores while minimizing the uncertainty to produce robust compound rankings. Benchmark evaluations further show that ReqVS achieves performance comparable to structure-based methods while improving ranking robustness across targets. When applied to the challenging GPCR target HCAR1, ReqVS identified a high-affinity antagonist (IC50 = 2.5 ± 1.2 μM), representing, to our knowledge, the first experimentally validated HCAR1 antagonist discovered using sequence-based virtual screening. Our results highlight reliability-aware sequence-based screening as a practical paradigm for structure-independent drug discovery. Biological sciences/Drug discovery/Drug screening/Virtual screening Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Virtual drug screening Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Information for “From Sequence to Hit: Reliable Virtual Screening via Interaction Entropy Enables HCAR1 Antagonist Discovery” 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-9324746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626843430,"identity":"f701a07f-0d11-417c-a6c3-50e7d3f2a3e7","order_by":0,"name":"Qingchao 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