This comprehensive review examines the multifaceted applications of Artificial Intelligence (AI) across the pharmaceutical industry, including drug discovery, clinical trials, pharmacovigilance, personalized medicine, and pharmaceutical manufacturing. The article also investigates the technical, ethical, regulatory, and economic challenges associated with AI implementation. Future prospects such as digital twin technologies, quantum computing, and biologically interfaced AI are discussed. The study includessystematic analysis of 157 Scopus-indexed publications, expert assessments, and policy frameworks. The paper concludes with specific strategic recommendations for Uzbekistan to establish itself as a regional AI leader in pharmaceutical innovation
This comprehensive review examines the multifaceted applications of Artificial Intelligence (AI) across the pharmaceutical industry, including drug discovery, clinical trials, pharmacovigilance, personalized medicine, and pharmaceutical manufacturing. The article also investigates the technical, ethical, regulatory, and economic challenges associated with AI implementation. Future prospects such as digital twin technologies, quantum computing, and biologically interfaced AI are discussed. The study includessystematic analysis of 157 Scopus-indexed publications, expert assessments, and policy frameworks. The paper concludes with specific strategic recommendations for Uzbekistan to establish itself as a regional AI leader in pharmaceutical innovation
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Raxmonov E.D. | Head of the Center for Digital Educational Technologies | Tashkent Pharmaceutical Institute, Tashkent |
| № | Name of reference |
|---|---|
| 1 | 1.Zhavoronkov, A., et al. (2022). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 40(3), 123–135. https://doi.org/10.1038/s41587-021-01013-32.Topol, E. (2023). AI in Clinical Medicine: A Practical Guide. Wiley-Blackwell.3.Mak, K.K., & Pichika, M.R. (2023). Artificial intelligence in drug development. Drug Discovery Today, 28(1), 103–115. https://doi.org/10.1016/j.drudis.2022.10.0174.FDA (2023). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device Action Plan.5.EMA (2024). Reflection Paper on the Use of AI in the Medicinal Product Lifecycle. https://www.ema.europa.eu6.McKinsey & Company (2023). Pharma 4.0: How AI is Transforming Drug Development.7.Deloitte (2024). Global AI in Pharma Market Forecast 2025–2030.8.NeurIPS (2023). Federated Learning for Healthcare Data Privacy. https://proceedings.neurips.cc |