AI-Guided Design of Personalized Nanomedicine: A Review of Data-Driven Approaches in Nanoparticle Formulation

The convergence of Artificial Intelligence (AI) and nanomedicine is revolutionizing the design of personalized therapeutic interventions. Nanomedicine leverages nanoscale carriers such as liposomes, polymeric nanoparticles, and inorganic systems to enhance drug delivery, improve targeting precision, and minimize adverse effects. Personalized medicine, which tailors treatments to an individual’s genetic, molecular, and physiological profile, can be significantly advanced through AI-driven formulation strategies. This review synthesizes current developments in AI-guided nanoparticle design, highlighting machine learning (ML), deep learning (DL), reinforcement learning (RL), and predictive modeling as pivotal tools for optimizing particle properties, predicting in vivo behavior, and enabling patient stratification. By integrating multi-omics, imaging, and clinical datasets, AI systems can accelerate discovery, reduce trial-and-error experimentation, and enable real-time adaptive treatment strategies. Platforms such as CURATE.AI exemplify individualized dosing approaches, while advances in graph neural networks and convolutional neural networks demonstrate the utility of AI in both structural modeling and diagnostic imaging. Furthermore, the review addresses ethical, regulatory, and data governance challenges inherent in implementing AI-driven nanomedicine, including bias, data privacy, and equitable access. The paper concludes by emphasizing the transformative potential of interdisciplinary collaboration, open-source innovation, and emerging technologies such as quantum computing and digital twins in realizing truly personalized, AI-powered nanotherapeutics for global healthcare applications.