Artificial Intelligence in Pharmaceutics and Drug Delivery: Current Applications and Future Perspectives
TL;DR
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.
Abstract
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