Abstract
Background: Tailoring nanoparticle properties (size, surface charge, polydispersity, yield, and biological performance) to specific biomedical applications remains time-consuming and resource-intensive. Recent studies have demonstrated promise for AI and metaheuristic methods to accelerate experimental design and optimize synthesis conditions. Methods: We assembled a multi-protocol experimental dataset (n = 420 synthesis runs) spanning chemical, green, and enzyme-mediated routes, measuring physicochemical outputs (size, PDI, zeta potential, yield) and application-specific bioendpoints (in vitro cell viability, antibacterial efficacy). Surrogate models (Random Forest, Gaussian Process Regression for Bayesian Optimization, and feedforward ANN) were trained with nested cross-validation. Metaheuristic search (genetic algorithm) and Bayesian optimization were applied to identify Pareto-optimal synthesis recipes for two target profiles: drug-delivery nanoparticles (small size, low PDI, high biocompatibility) and antibacterial nanoparticles (small size, high surface charge magnitude, high antimicrobial activity). Model interpretability utilized SHAP and sensitivity analysis. Results: AI-driven surrogate models achieved high predictive performance (best R2 on hold-out test: Random Forest R2 = 0.86 for particle size; Gaussian Process R2 = 0.83 for zeta potential). Optimization produced synthesis parameter sets that improved targeted metrics by 18–42% relative to baseline DOE-optimized recipes. Feature importance and SHAP analysis identified precursor concentration, pH, capping agent ratio, and reaction temperature as primary levers across endpoints. Tables and figures summarize model performance, regression coefficients/importance rankings, and optimized parameter sets. Conclusions: The integrated AI + metaheuristic framework substantially reduces experimental search space and yields application-specific synthesis protocols with demonstrable gains in physicochemical and biological performance. The approach is generalizable across nanoparticle classes and supports rapid translation to tailored biomedical use cases. Future work should expand datasets, integrate active learning with closed-loop experimentation, and validate optimized recipes in relevant in vivo models.