Pharmacophore-Driven Kinase Profiling: Application to the PKIS2 Chemogenomic Dataset

Pharmacophore-Driven Kinase Profiling: Application to the PKIS2 Chemogenomic Dataset | 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 Pharmacophore-Driven Kinase Profiling: Application to the PKIS2 Chemogenomic Dataset Ronan Bureau, Jean-Luc Lamotte, Bertrand Cuissart, Alban Lepailleur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7785597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract We present a novel and efficient approach for extracting 3D pharmacophores, without any supervised compound selection, from a chemogenomic kinase dataset (406 kinases and 645 compounds, PKIS2) using the NORNS software. For each pharmacophore, we introduce a metric called NEM, which quantifies the change in the proportion of active compounds consistent with the pharmacophore compared to the original dataset. Based on this metric, we can identify pharmacophores associated with specific kinases and, conversely, determine all kinases that share similar metric values. This approach enables the characterization of polypharmacological profiles linked to individual pharmacophores. The consistency of our results with various biological datasets, including ChEMBL, DrugBank, LINCS, KINOMEscan, and Kinobeads, was evaluated and showed strong correlations in selected case studies. This study highlights the potential of our approach for elucidating relationships between pharmacophores and kinase selectivity profiles, offering valuable insights into kinase–target interactions. Scientific contribution Starting from our previous software, NORNS (see Availability of data and materials for the current version), which focused on 2D pharmacophores extracted from a global chemical dataset and associated with a single biological profile, this article now explores 3D pharmacophores across multiple conformations, linked to a polypharmacological profile with a focus on kinases. We demonstrate the ability to extract pharmacophores associated with a specific kinase, and define a selectivity profile for these pharmacophores with respect to other kinases. We also show the possibility of extracting pharmacophores based on Boolean logic, for instance, identifying pharmacophores linked to one kinase but not another. Using the kinase chemogenomic dataset PKIS2 as a starting point, several experimental results predicted were confirmed. Biological sciences/Cancer Physical sciences/Chemistry Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Cheminformatics pharmacophores pattern mining isomorphism polypharmacology kinase inhibitors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor invited by journal 08 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Submission checks completed at journal 06 Oct, 2025 First submitted to journal 05 Oct, 2025 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-7785597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534479592,"identity":"5261f00a-151e-4eb1-adb1-b898d44bec81","order_by":0,"name":"Ronan 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