Document Type : Review Article
Authors
1
Department of Pharmaceutical Nanotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
2
Center for Nanotechnology in Drug Delivery, Shiraz University of Medical Sciences, Shiraz, Iran
10.30476/tips.2025.107583.1306
Abstract
Artificial intelligence (AI) is transforming pharmaceutical nanotechnology by enabling rapid, intelligent, and precise solutions to challenges in drug delivery, diagnosis, and material design. By using machine learning (ML) or deep learning (DL) to simulate how nanoparticles behave, researchers can better adjust formulation details and interpret complex biological information more accurately than ever before. This review provides a thorough overview of key AI methods, including supervised, unsupervised, and combined learning approaches, as well as deep neural networks, and their applications in areas such as tumor imaging, mRNA vaccine development, and the assessment of nanomaterial safety. Selected case studies illustrate measurable progress: NanoMASK achieved correlation coefficients above 0.99 for pharmacokinetic analysis, AI-guided lipid screening identified candidates with up to 10-fold higher transfection efficiency, and optimized oral nanoformulations delivered a 6.2-fold increase in plasma drug levels. These examples highlight how AI can improve treatment effectiveness, reduce experimental workload, and accelerate translation from laboratory research to clinical implementation. Although issues such as algorithm transparency and regulatory harmonization remain, the overall impact is clear—AI is driving faster, smarter, and more reliable advances in nanomedicine toward personalized and ethically sound healthcare.
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