Dendrimer Nanotechnology in Targeted Therapy

Abstract

Dendrimer-based nanocarriers have emerged as promising platforms for targeted therapy because of their highly branched architecture, tunable surface chemistry, and exceptional drug encapsulation capacity. Despite rapid advances in nanomedicine, the optimization of dendrimer formulations remains largely dependent on empirical experimentation, increasing development time and cost. This study identifies a critical research gap in the absence of explainable, multi-objective artificial intelligence systems capable of simultaneously optimizing therapeutic efficacy, toxicity, targeting specificity, and manufacturing feasibility. To address this limitation, an Explainable Multi-Agent Dendrimer Optimization Framework (EMDOF) is proposed, integrating Graph Neural Networks, Multi-Agent Reinforcement Learning, Bayesian Optimization, and Explainable Artificial Intelligence techniques. The framework predicts optimal dendrimer configurations using multimodal physicochemical and biological descriptors while maintaining transparency in decision-making. A comprehensive experimental workflow based on publicly available nanomaterial datasets is designed to evaluate predictive reliability and clinical relevance. The proposed approach demonstrates the potential of AI-assisted dendrimer engineering for accelerating precision medicine while reducing experimental burden. This research establishes a computational pathway toward safer, more personalized, and scalable nanotherapeutic development