The Role of Artificial Intelligence in Designing Next-Generation Nanoformulations: A Comprehensive Review

The integration of artificial intelligence (AI) into pharmaceutical nanoformulation designs represents a paradigmatic shift from traditional empirical approaches to data-driven, predictive methodologies. This comprehensive review examines the current state and future prospects of AI applications in nanoformulation development, encompassing machine learning algorithms, deep learning architectures, and optimization techniques. We analyze how AI addresses critical challenges in nanoparticle design, including property prediction, material selection, process optimization, and personalized medicine applications. The review covers key AI methodologies such as supervised and unsupervised learning, neural networks, genetic algorithms, and ensemble methods, while discussing their specific applications in lipid nanoparticles, polymeric systems, inorganic nanocarriers, and hybrid formulations. Current limitations, including data scarcity, model interpretability, and regulatory considerations, are critically evaluated alongside emerging solutions. We conclude by identifying future research directions that could revolutionize nanoformulation development through enhanced AI integration, ultimately accelerating the translation of nanomedicines from laboratory to clinic.