Machine Learning and Computational Insights into Nanoparticle-Based Drug Delivery

Abstract

Nanoparticle-based drug delivery systems have emerged as a promising strategy to overcome the limitations of conventional therapeutic approaches, including poor bioavailability, lack of target specificity, and systemic toxicity. The unique physicochemical properties of nanoparticles enable controlled drug release, enhanced targeting, and improved therapeutic efficacy. However, the design and optimization of these systems remain highly complex, requiring the integration of multiple physicochemical and biological parameters. In recent years, computational modeling and Machine Learning (ML) have gained significant attention as powerful tools to address these challenges. ML techniques facilitate the prediction of nanoparticle properties, optimization of formulation parameters, and analysis of nanoparticle–biological interactions. Moreover, data-driven approaches contribute to improved nanotoxicity assessment and more efficient drug delivery system design. This review provides a comprehensive overview of nanoparticle-based drug delivery, with a particular focus on computational methods, ML applications, available data sources, and recent case studies. Key challenges, including data scarcity, model interpretability, and reproducibility, are critically discussed. In addition, emerging trends such as personalized nanomedicine, generative artificial intelligence, and adaptive drug delivery systems are highlighted. Overall, the integration of ML with nanoparticle-based drug delivery offers a transformative pathway toward precision medicine, enabling the development of more effective, safe, and targeted therapeutic strategies.