1. Vyas M., T. S. Asian J Pharmaceutics, [2018]72-76.
2. M, M.Research gate,[2015].
3. Davies, N. M. Adapting artificial intelligence into the evolution of pharmaceutical sciences and publishing: Technological darwinism. Pharm Science.[2023]
4. Fleming, N. How artificial intelligence is changing drug discovery,[2018].
5. Digital Authority Partners. (2019 November 23). Retrieved from https://www.digitalauthority.me/resources/artificial-intelligence-pharma/
6. Berg, K. M. (2023, November 23). Using Artificial Intelligence for Drug Discovery. Retrieved from http://www.medcitynews.
7. Cattell J, C. S. (2013, April). How big data can revolutionize pharmaceutical R and D. Retrieved from https://www.mckinsey.com/industries/life-sciences/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d
8. Kit-Kay Mak, M. R. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today [2019].
9. M.et al, V. Artificial intelligence:the beginning of a new era in pharmacy. Scopus, [2018]72-76.
10. Matthew A Sellwood1, M. A. Artificial intelligence in drug discovery. Future Med. Chem, [2018]
11. Artificial intelligence in drug. (2021). Retrieved from https://www.bing.com/images/search?view=detailv2&insightstoken=bcid_qCoBHe9jOBwG1g*ccid_KgEd72M4*cp_DB17948D748D1E5BEBC343CA9E75D2E6&form=WCVSID&iss=SBIUPLOADGET&darkschemeovr=1&selectedindex=4&id=614416574&ccid=KgEd72M4&exph=270&expw=836&vt=2&simid=60798
12. Zhu, H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annual Review of Pharmacology and Toxicology , [2020] 573-589.
13. Ciallella, H. a. Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chem. Res. Toxicol., [2019] 536–547.
14. Role of artificial intelligence (AI) in drug discovery.[2021]Retrieved from https://www.bing.com/images/search?view=detailv2&insightstoken=bcid_qHAnI9-kLRwGdw*ccid_cCcj36Qt*cp_3B33FD597D5DBFC04F8203D108B11C9B&form=WCVSID&iss=SBIUPLOADGET&darkschemeovr=1&selectedindex=0&id=-313176489&ccid=cCcj36Qt&exph=351&expw=740&vt=2&simid=6080
15. Automatic Chemical Design Using a Data-Driven Continuous. ACS Central Sci., [2018] 268–276.
16. Valavanidis, P. A. Artificial Intelligence (AI) Applications.Τhe most important technology we ever develop and we must ensure it . Research Gate.[2023].
17. Debleena Paul, G. S. Artificial intelligence in drug. Drug Discovery Today.[2021]
18. Aydın, Ö., & Karaarslan, E. J. OpenAI ChatGPT generated literature review: Digital [2022].
19. Jiao, W., Wang, W., Huang, J.-t., Wang, X., & Tu, Z. J. Is ChatGPT a good translator? A preliminary study[2023].
20. Gangadevi, S., Badavath, V. N., Thakur, A., Yin, N., De Jonghe, S., Acevedo, O., . . . Neyts, J. J. Kobophenol A inhibits binding of host ACE2 receptor with spike RBD domain of SARS-CoV-2, a lead compound for blocking COVID-19. [2021]12 (7), 1793-1802.
21. Madhyastha, H., Madhyastha, R., Thakur, A., Kentaro, S., Dev, A., Singh, S., . . . Biointerfaces, S. B. c-Phycocyanin primed silver nano conjugates: Studies on red blood cell stress resilience mechanism. [2020] 194, 111211.
22. Sharma, G., Song, L. F., & Merz, K. M. Modeling, Effect of an Inhibitor on the ACE2-Receptor-Binding Domain of SARS-CoV-2.[2022]
23. Thakur, A., Mandal, S. C., Banerjee, S. J., & Studies, M. Compounds of natural origin and acupuncture for the treatment of diseases caused by estrogen deficiency. [2016] 109-117.
24. Hay, M. e. Clinical development success rates for investigational drugs. Nat.Biotechnology, [2014] 40-51.
25. Harrer, S. e. Artificial intelligence for clinical trial design. . Trends Pharmacological science, [2019]577-551.
26. Kamal H., L. V. Front Neurol. [2018]
27. Tariq, A. G.Evaluating the Potential of Artificial Intelligence in Orthopedic Surgery for Value-based Healthcare. . International Journal of Multidisciplinary Sciences and Arts, [2023] 27-36.
28. Mak, K.-K. a. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, [2019]773–780.
29. Asselah, T. D. COVID-19:Discovery, diagnostics and drug development. J. Hepatol, [2021] 168–184.
30. Azodi, C. B. Opening the black box: Interpretable machine learning for geneticists. Trends Genet, [2020] 442–455.
31. Feng R., B. M. (2018). 358-362.
32. Bajorath J, K. S. The Future Is Now: Artificial Intelligence in Drug Discovery. Journal of Medicinal Chemistry,[2019] 5249-5249. .
33. Meziane, F. e. Intelligent systems in manufacturing: current developments and future prospects. [2000] 218-238.
34. Steiner, S. e.Organic synthesis in a modular robotic system driven by a chemical programming language.[2019]
35. Faure, A. e. Process control and scale-up of pharmaceutical wet granulation processes: a review. Eur. J. Pharm. Biopharm., [2001] 269-277.
36. Landin, M. Artificial intelligence tools for scaling up of high shear wet granulation process. J. Pharm. Sci. , [2017] 273–277.
37. Das, M. a. ANN in pharmaceutical product and process development. In Artificial Neural Network for Drug Design, Delivery and Disposition. Elsevier, [2016] 277–293.
38. kraft, D. (n.d.). System and methods for the production of personalized drug products.
39. Gams, M. e. Integrating artificial and human intelligence into tablet. AAPS PharmSciTech , [2014] 1447–1453.
40. Aksu, B. e. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharm. Dev. Technol. , [2013] 236–245.
41. Dragoi, E. a. On The use of artificial neutral networks to monitor a pharmaceutical freez-drying process. Drying Technology, [2013]72-81.
42. Available online. (2022, December 6)
43. Mag.k.k, P. M. Artifial Inteligence in drug development: Present Status and future Prospects. Drug discovery Today, [2019]773-80.