A Novel Perspective on Using Artificial Intelligence and Nanoinformatics to Develop Nanomedicines

Developing novel pharmacological compounds for disease treatment is an inherently time-consuming and costly process, yet research continues unabated. Leveraging existing data resources and identifying innovative therapeutic leads are critical steps in drug design. The integration of artificial intelligence (AI) and machine learning (ML) offers powerful tools for designing and developing translational nanomedicines. The biological activity of a nanomedicine is largely determined by its physicochemical properties, including size, shape, surface charge, and chemical composition. These properties can be systematically optimized using nanoinformatics approaches, such as quantitative structure-activity/property relationship (QSAR/QSPR) models, enabling enhanced functionality of engineered nanomedicines while minimizing potential health and environmental risks during development. Physiologically based pharmacokinetic (PBPK) models further complement these approaches by predicting drug and nanomedicine distribution in body fluids, extrapolating experimental data, and establishing correlations between physicochemical properties and biodistribution. Such models are particularly valuable for toxicity assessment. This review focuses on the implementation of nanoinformatics tools and AI to facilitate the translation of nanomedicines from bench to clinic. Computational strategies for designing nanodelivery systems are highlighted, including selecting suitable nanomaterials, assessing potential nanotoxicity, and developing simulation models for in vitro and in vivo analyses. Additionally, the review examines the contributions of AI and ML to the development of translational nanomedicines, as well as the associated challenges and future research directions. The compiled insights are highly relevant to research groups involved in drug discovery, nanotechnology, and the development of advanced drug delivery systems for biomedical applications. Importantly, the methodologies discussed have broad applicability across multiple scientific disciplines.