Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis.

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 challenging. 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 were 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. The discovery of GMP and dGMP hydrogels and their successful validation in periodontitis models highlight the potential of this strategy for developing targeted therapies for oral diseases.