Aug 2026· ACS Nano· Vol 20 32, pp.
22247-22264
· 0 citations· 173 references
Medicine
TL;DR
An overview of the applications of AI in drug delivery is highlighted, focusing on AI-designed drug formulations, AI-driven prediction of ADMET properties, and AI-assisted drug delivery devices, which support the development of precision medicine.
Abstract
Drug delivery, serving as a pivotal link between pharmaceutical innovation and clinical implementation, faces numerous challenges in achieving optimal therapeutic outcomes. With the advancement of computational methodologies and technological tools, artificial intelligence (AI) has been increasingly applied in pharmaceutical sciences, ranging from target discovery to product management. In recent years, AI has been extensively used in drug delivery to tailor formulation design, enhance therapeutic efficacy, and reduce side effects. However, limitations in data quality and model interpretability frequently restrict AI's predictive performance and hinder its clinical applicability. This overview highlights the applications of AI in drug delivery, focusing on AI-designed drug formulations, AI-driven prediction of ADMET properties, and AI-assisted drug delivery devices, which support the development of precision medicine. Additionally, the translation challenges and future perspectives in this field are discussed.
Artificial-intelligence (AI) is transforming the pharmaceutical industry by simplifying data-driven innovations in drugs’ research and delivery systems. This paper examines and views the newinnovations in combining the use of AI into harmonized medication design and targeted delivery systems. AI-driven models speed-up molecular design, predict pharmacokinetic outcomes, and improve the nanocarrier preparations, reducing the costs and time associated to conventional experimental techniques. Furthermore, AI contentsmake use of computational chemistry and synthesis-based science, improving medicinal effectiveness, precision, and controlled release. It highlights ethical implications and the interaction between computational intelligence and chemical innovation as a means to advance next-generation intelligent therapies and precision medicine. Also, it analyzes AI-driven molecular design with synthetic level reports, emphasizing explainable AI, digital twin platforms, and translations that characterize the next generation of precision pharmaceuticals, distinguishing it from previous narrative reviews.
Artificial intelligence (AI) is rapidly reshaping pharmaceutics and drug delivery, offering computational alternatives to traditional trial-and-error approaches in formulation design, nanocarrier engineering, and manufacturing. This review examines the current applications of machine learning, deep learning, reinforcement learning, and generative AI architectures across the pharmaceutical product lifecycle, from target identification and lead optimization to dosage form development and clinical translation. Particular attention is given to AI-driven prediction of excipient compatibility, formulation stability, and bioavailability, as well as the design of smart nanocarriers, stimuli-responsive delivery systems, and 3D-printed dosage forms enabled by graph neural networks, transformer-based models, and Bayesian optimization techniques. The review also traces the historical evolution of computational pharmaceutics, from early rule-based prediction systems to current high-throughput, automation-integrated platforms, and highlights emerging tools such as digital twins, federated learning frameworks, and AI-supported nanorobotics for targeted intracellular delivery. Despite these advances, significant challenges persist, including limited data availability and standardization, algorithmic opacity and the demand for explainable AI, and the absence of harmonized regulatory frameworks for validating AI-driven decision-making in drug development. The review further considers data quality and interpretability concerns specific to nanomedicine, alongside the regulatory and ethical considerations necessary for clinical adoption. Future directions emphasize the integration of multi-omics data, real-time adaptive drug delivery systems, and quantum-AI convergence to advance personalized medicine. Addressing these barriers through interdisciplinary collaboration, standardized data repositories, and transparent regulatory guidance will be essential to translating AI-enabled pharmaceutical innovations from computational design into safe, effective clinical therapeutics.
Keywords: Artificial intelligence; machine learning; drug delivery; nanomedicine; formulation design; personalized medicine; deep learning; pharmaceutics
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