# Artificial intelligence and machine learning in nanoparticle drug delivery systems - Nature *Источник: Nature* *Дата: 2026-09-17* *Язык: en* **Кратко:** Abstract Artificial intelligence (AI), including machine learning (ML) methods, is shifting drug development pipelines from traditional, trial-and-error discovery towards computationally guided architectures that accelerate the identification of optimal formulations. In this Review, we outline the role of ML in the discovery and preclinical phases of the development pipeline for nanomedicine, in particular, focusing on nanoparticle-based delivery systems. Abstract Artificial intelligence (AI), including machine learning (ML) methods, is shifting drug development pipelines from traditional, trial-and-error discovery towards computationally guided architectures that accelerate the identification of optimal formulations. In this Review, we outline the role of ML in the discovery and preclinical phases of the development pipeline for nanomedicine, in particular, focusing on nanoparticle-based delivery systems. During the formulation design and optimization stage, ML can predict the physico-chemical properties of lipid, polymeric and self-assembling nanoparticles, screen vast virtual libraries and guide the discovery of new excipients through autonomous platforms. In preclinical evaluation, ML can predict in vitro interactions, such as cellular uptake, and functional outcomes, such as transfection and cytotoxicity. Notably, ML can also model in vivo performance by integrating nanoparticle characteristics with complex biological datasets, ranging from tumour genomics to medical imaging, to predict biodistribution, tumour targeting and therapeutic outcomes. This Review also discusses the limitations of ML-driven approaches and the ongoing efforts to address these challenges. We highlight how these advancements could transform the development pipeline and improve the translation of nanoparticle-based delivery systems for clinical use. Key points - Machine learning (ML) is shifting nanoparticle drug delivery from empirical trial and error towards predictive, data-driven design, accelerating the optimization of formulations with complex, high-dimensional parameter spaces. - The choice of ML model is constrained by dataset size: tree-based ensembles and Gaussian processes perform well in the small-data regime typical of nanomedicine, whereas deep learning architectures become competitive only with large, standardized datasets. - Integration of ML with molecular dynamics simulations, physics-informed neural networks and autonomous ‘self-driving’ laboratories enables closed-loop design–make–test–analyse workflows that bridge in silico predictions and experimental validation. - ML models can predict not only physico-chemical attributes, such as nanoparticle size and encapsulation efficiency, but also downstream biological outcomes, including cellular uptake, biodistribution, tumour accumulation and crossing of biological barriers, such as the blood–brain barrier. - Realizing the full potential of ML in nanomedicine requires community-wide adoption of FAIR (findable, accessible, interoperable and reusable) data principles, standardized reporting (for example, minimum information frameworks), open-source code sharing and large-scale repositories built through public–private partnerships. 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Author information Authors and Affiliations Contributions M.P. researched the literature, wrote the manuscript and created the figures and tables. A.G. contributed to literature research and writing of specific sections, as well as creation of figures and tables. D.A.H., D.R. and C.A. provided guidance on the scope and structure of the Review. All authors reviewed and edited the manuscript before submission. Corresponding authors Ethics declarations Competing interests D.A.H. is a co-founder, officer and board member with equity interest in Nine Diagnostics Inc.; a co-founder with equity interest in Lime Therapeutics Inc.; a co-founder with equity and intellectual property interests in Selectin Therapeutics Inc.; an advisor with equity and intellectual property interests in Block Code Protected Ltd; an advisor with equity interest in Celine Therapeutics Inc., Nano-robotics Inc., Mediphage Bioceuticals Inc. and Concarlo Therapeutics Inc.; and a consultant for Metis Therapeutics, Inc. C.A. is a co-founder and CEO of Intrepid Labs Inc. D.R. acts as a consultant to the pharmaceutical and biotechnology industry, and as a mentor for Start2. The other authors declare no competing interests. Peer review Peer review information Nature Reviews Bioengineering thanks Adam Gormley, Paolo Decuzzi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Related links Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Panagiotakopoulou, M., Goren, A., Reker, D. et al. Artificial intelligence and machine learning in nanoparticle drug delivery systems. Nat Rev Bioeng (2026). https://doi.org/10.1038/s44222-026-00495-7 - Accepted: - Published: - Version of record: - DOI: https://doi.org/10.1038/s44222-026-00495-7 [Оригинал](https://www.nature.com/articles/s44222-026-00495-7)