Integrating Evolutionary and Compositional Features with ML and DL for Robust and Interpretable Druggable Protein Prediction

Integrating Evolutionary and Compositional Features with ML and DL for Robust and Interpretable Druggable Protein Prediction | 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 Integrating Evolutionary and Compositional Features with ML and DL for Robust and Interpretable Druggable Protein Prediction Mujeebu Rehman, Qinghua Liu, Muhammad Javed, Ali Ghulam, Teerath Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8264495/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Druggable proteins are essential targets in contemporary therapeutic discovery, and their precise identification is necessary for the progression of rational drug design. Traditional biochemical screening techniques are costly, laborious, and protracted, whereas current computational models typically attain only moderate efficacy and seldom offer robust statistical validation or biological interpretability. To mitigate these limitations, we offer a hybrid computational approach that amalgamates evolutionary and compositional information by merging Average Block-based Position-Specific Scoring Matrix (AB-PSSM) characteristics with Dipeptide Composition (DPC) into a 600-dimensional representation. The hybrid feature space was assessed utilizing three machine learning algorithms (support vector machine, random forest, and XGBoost) alongside three deep learning architectures (CapsBiLSTM, ResCapsNetPlus, and ResNet1D) following a rigorous five-fold out-of-fold cross-validation protocol to guarantee fairness and reproducibility. A thorough assessment revealed that the hybrid attributes significantly surpassed individual descriptors, with the SVM and CapsBiLSTM classifiers attaining classification accuracies of 90% and ROC-AUC and PR-AUC values surpassing 95%. The robustness and reliability were further validated by statistical analyses, including DeLong’s test, McNemar’s test, and bootstrap confidence intervals, demonstrating that the observed enhancements were substantial and consistent. Furthermore, interpretability investigations employing SHAP feature attribution and t-SNE visualization underscored biologically significant sequence descriptors as pivotal factors influencing druggability, thus mitigating the "black box" constraint inherent in numerous deep learning methodologies. This study presents a statistically validated, interpretable, and high-performing framework for predicting druggable proteins, which outperforms current machine learning and ensemble-based methods, establishing a clear basis for future applications in precision medicine and drug discovery. Computational Drug Discovery Deep Learning Models Machine Learning Models Druggable Proteins Hybrid Feature Representation Position-Specific Scoring Matrix (PSSM) Dipeptide Composition (DPC) Protein Sequence Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviews received at journal 03 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 02 Dec, 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. 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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-8264495","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557454229,"identity":"564c597a-9ec3-414e-acbe-e0267188cb66","order_by":0,"name":"Mujeebu Rehman","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Mujeebu","middleName":"","lastName":"Rehman","suffix":""},{"id":557454230,"identity":"22ae152f-80f2-4451-88dd-b6e9ccf1d5c7","order_by":1,"name":"Qinghua 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