Predicting Selective Serotonin Re-Uptake Inhibitors Potency: Machine Learning and Molecular Docking Approach

Predicting Selective Serotonin Re-Uptake Inhibitors Potency: Machine Learning and Molecular Docking Approach | 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 Predicting Selective Serotonin Re-Uptake Inhibitors Potency: Machine Learning and Molecular Docking Approach Isaiah A. Adejoro, Inioluwa J. Adewara, Damilare Babatunde, Chinonyerem A. G. Johnson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6160025/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Selective Serotonin Re-uptake Inhibitors are medications employed for the treatment of mental diseases such as depression, anxiety disorders, and OCD. They enhance neurotransmission and mood control by increasing serotonin levels in the brain. This study employs ensemble machine learning and molecular docking to identify novel SSRIs. The potential ligands were obtained from CHEMBL database, which were then used to build a machine-learning model predicting the inhibitory constant (Ki) values of SSRIs. Molecular fingerprints served as feature descriptors in the model, which was trained using various ensemble machine-learning algorithms. The ExtraTrees Regressor with KlekothaRothFingerPrint as molecular fingerprint achieved the highest accuracy of 92% r-squared and 0.01 RMSE values. The ZINC database was queried to identify novel SSRIs, and the compounds with favorable Ki values underwent molecular docking simulations with the target molecule 7lwd. The study identified ZINC00427761, ZINC00427746, ZINC00425581, and ZINC00427764 as potential SERT inhibitors with strong molecular interactions and docking binding energies of -10.9, -10.8, 10.6, -10.1 kcal/mol respectively. These findings contribute to drug discovery efforts, particularly in the development of novel SSRIs. Additional in vitro and in vivo research are advised. Physical sciences/Chemistry/Cheminformatics Physical sciences/Chemistry/Chemical biology/Biophysical chemistry Physical sciences/Chemistry/Chemical biology/Computational chemistry Biological sciences/Drug discovery/Drug delivery Full Text Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYINFORMATION.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 Jun, 2025 Reviews received at journal 30 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 18 Apr, 2025 Editor assigned by journal 25 Mar, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 12 Mar, 2025 First submitted to journal 05 Mar, 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-6160025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427774044,"identity":"f686d5eb-9388-44f2-9747-f65452ed6cc8","order_by":0,"name":"Isaiah A. 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