P 3: A Framework for Predicting Protein-Protein Interactions Using Large Language Models

P 3: A Framework for Predicting Protein-Protein Interactions Using Large Language Models | 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 Research Article P 3: A Framework for Predicting Protein-Protein Interactions Using Large Language Models Lamiaa Basyoni, Jovana Aleksic, Stephanie Schaefer-Ramadan, Yue Guan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6309474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prediction of protein-protein interactions (PPIs) is essential for understanding biological functions and disease mechanisms. Traditional methods for PPI prediction often focus on physical interactions, overlooking the complex indirect relationships mediated by intermediate proteins. The recent advances in Large Language Models (LLMs) present a novel opportunity to address these challenges. By treating protein sequences as natural language, LLMs can capture both direct and indirect interactions, enhancing prediction capabilities. In this paper, we propose a new framework that leverages a BERT-based LLM fine-tuned specifically for PPI prediction. Our model encodes protein sequences into high-dimensional embeddings, capturing long-range dependencies between amino acids, which are critical for identifying PPIs. By fine-tuning the LLM on a novel PPI dataset, we achieve an accuracy of 93% and a F1-score of 83%. The proposed framework shows improved performance in terms of both prediction accuracy and generalization, demonstrating the potential of LLMs to revolutionize PPI prediction and provide valuable insights for fields such as drug discovery and molecular biology. Large Language Models Protein-Protein Interaction Prediction Text Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. 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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-6309474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459670879,"identity":"83b236f5-4702-4dc6-9a69-aa28dec9f78f","order_by":0,"name":"Lamiaa Basyoni","email":"","orcid":"","institution":"Weill Cornell Medicine - Qatar","correspondingAuthor":false,"prefix":"","firstName":"Lamiaa","middleName":"","lastName":"Basyoni","suffix":""},{"id":459670880,"identity":"e98e6059-9aa9-4d23-88e4-174605f90b29","order_by":1,"name":"Jovana Aleksic","email":"","orcid":"","institution":"Weill Cornell Medicine - Qatar","correspondingAuthor":false,"prefix":"","firstName":"Jovana","middleName":"","lastName":"Aleksic","suffix":""},{"id":459670881,"identity":"14825ada-53c7-40a5-a1ed-ca8376644ca5","order_by":2,"name":"Stephanie Schaefer-Ramadan","email":"","orcid":"","institution":"Weill Cornell Medicine - 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