Development of Machine Learning Models for Precise Prediction of Bioactive Supramolecular Nucleoside Hydrogels

Supramolecular hydrogels hold significant potential in drug delivery and tissue engineering, with standing out for their unique properties. Despite their promise, predicting nucleoside bioactivity remains challenges. This study aims to predict the biological activity of nucleosides to guide the rational synthesis of hydrogels. Specifically, nine predictive models and databases for various biological activities were built with feature-selected machine learning methods including decision trees, logistic regression, random forest, and extreme gradient boosting. Then, the Molecular Bioactivity Specificity Index (MBSI) was introduced to gauge the primary bioactivity of nucleoside derivatives, and the Composite Molecular Attribute Score (CMAS) was devised to measure the overall performance of nucleoside derivatives. Subsequently, screening strategies for bioactive nucleoside hydrogels was established, and two candidate hydrogels (GMP and dGMP) with high hydrogel-forming ability, biocompatibility, and antibacterial activity were identified. Finally, two hydrogels were validated for antibacterial treatment of periodontitis. This study highlights the feasibility of ML-based strategies and MBSI/CMAS in rationally designing bioactive nucleoside hydrogels for biomedical applications.