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Kadkhodaei, Mohammad, Monajati, Maryam. (1404). The Evolution of AI and Its Transformative Role in Pharmaceutical Nanotechnology. سامانه مدیریت نشریات علمی, 11(4), 293-308. doi: 10.30476/tips.2025.107583.1306
Mohammad Kadkhodaei; Maryam Monajati. "The Evolution of AI and Its Transformative Role in Pharmaceutical Nanotechnology". سامانه مدیریت نشریات علمی, 11, 4, 1404, 293-308. doi: 10.30476/tips.2025.107583.1306
Kadkhodaei, Mohammad, Monajati, Maryam. (1404). 'The Evolution of AI and Its Transformative Role in Pharmaceutical Nanotechnology', سامانه مدیریت نشریات علمی, 11(4), pp. 293-308. doi: 10.30476/tips.2025.107583.1306
Kadkhodaei, Mohammad, Monajati, Maryam. The Evolution of AI and Its Transformative Role in Pharmaceutical Nanotechnology. سامانه مدیریت نشریات علمی, 1404; 11(4): 293-308. doi: 10.30476/tips.2025.107583.1306

The Evolution of AI and Its Transformative Role in Pharmaceutical Nanotechnology

Trends in Pharmaceutical Sciences and Technologies
مقاله 2، دوره 11، شماره 4 - شماره پیاپی 44، اسفند 2025، صفحه 293-308    اصل مقاله (664.22 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.30476/tips.2025.107583.1306
نویسندگان
Mohammad Kadkhodaei1، 2؛ Maryam Monajati* 1، 2
1Center for Nanotechnology in Drug Delivery, Shiraz University of Medical Sciences, Shiraz, Iran
2Department of Pharmaceutical Nanotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
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.

تازه های تحقیق

Maryam Monajati (Google Scholar)

 

کلیدواژه‌ها
Artificial Intelligence (AI)؛ Machine Learning (ML)؛ Deep Learning (DL)؛ Nanomedicine؛ Targeted Drug Delivery؛ Predictive Modeling
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