Author(s): Praveen Tahilani, Hemant Swami, Gaurav Goyanar, Shivani Tiwari


DOI: 10.52711/2349-2988.2022.00030   

Address: Praveen Tahilani1, Hemant Swami2, Gaurav Goyanar2, Shivani Tiwari1
1Sagar Institute of Research and Technology - Pharmacy, Bhopal, MP.
2School of Pharmaceutical Science, SAGE University, Indore, M.P.
*Corresponding Author

Published In:   Volume - 14,      Issue - 3,     Year - 2022

As a growing sector, the Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry contributes in the drug discovery process, giving emphasis on how new technologies have improved effectiveness. As in the current scenario artificial intelligence including machine learning may be considered the future for a wide range of disciplines and industries specially the pharmaceutical industry. As we know today pharmaceutical industries producing a single approved drug cost the company millions with many years of rigorous testing prior to its approval, reducing costs and time is of high interest. The involvement of Artificial Intelligence will be useful to the pharmaceutical industry and also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics.

Cite this article:
Praveen Tahilani, Hemant Swami, Gaurav Goyanar, Shivani Tiwari. The Era of Artificial Intelligence in Pharmaceutical Industries - A Review. Research Journal of Science and Technology. 2022; 14(3):183-7. doi: 10.52711/2349-2988.2022.00030

Praveen Tahilani, Hemant Swami, Gaurav Goyanar, Shivani Tiwari. The Era of Artificial Intelligence in Pharmaceutical Industries - A Review. Research Journal of Science and Technology. 2022; 14(3):183-7. doi: 10.52711/2349-2988.2022.00030   Available on:

1.    Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24(3):773-80.
2.    Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151:169-90.
3.    Russel S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. AI Mag. 2015;36(4):105-14.
4.    Duch W, Setiono R, Zurada JM. Computational intelligence methods for rulebased data understanding. Proc IEEE. 2004;92(5):771-805.
5.    Dasta JF. Application of artificial intelligence to pharmacy and medicine. Hosp Pharm. 1992;27(4):319-22.
6.    Jiang F, Jiang Y, Zhi H. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43.
7.    Gobburu JV, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis. J Pharm Sci. 1996;85(5):505-10.
8.    Sakiyama Y. The use of machine learning and nonlinear statistical tools for ADME prediction. Expert Opin Drug Metab Toxicol. 2009;5(2):149-69.
9.    Ramesh A. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004; 86:334–338.
10.    Miles J., Walker A. The potential application of artificial intelligence in transport. IEE Proc.-Intell. Transport Syst. 2006;153:183–198
11.    Yang Y., Siau K. MWAIS; 2018. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age.
12.    Wirtz B.W. Artificial intelligence and the public sector—applications and challenges. Int. J. Public Adm. 2019;42:596–615.
13.    Smith R.G., Farquhar A. The road ahead for knowledge management: an AI perspective. AI Mag. 2000;21 17–17.
14.    Lamberti M.J. A study on the application and use of artificial intelligence to support drug development. Clin. Ther. 2019;41:1414–1426.
15.    Beneke F., Mackenrodt M.-O. Artificial intelligence and collusion. IIC Int. Rev. Intellectual Property Competition Law. 2019;50:109–134.
16.    Steels L., Brooks R. Routledge; 2018. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents.
17.    Bielecki A., Bielecki A. Foundations of artificial neural networks. In: Kacprzyk Janusz., editor. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing; 2019. pp. 15–28. Polish academy of sciences, Warsaw, Poland.
18.    Kalyane D. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade Rakesh K., editor. The Future of Pharmaceutical Product Development and Research. Elsevier; 2020. pp. 73–107.
19.    Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. Ai Magazine. 2015 Dec 31;36(4):105-14.
20.    Lakshmi Teja T, Keerthi P, Debarshi Datta NB. Recent trends in the usage of robotics in pharmacy.
21.    Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar; 13(3):45-60
22.    22.Brady M. Artificial intelligence and robotics. In Robotics and Artificial Intelligence 1984 (pp. 47-63). Springer, Berlin, Heidelberg.
23.    Guo M. A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharm. Technol. 2002;6:44–52.
24.    Mehta C.H. Computational modeling for formulation design. Drug Discovery Today. 2019;24:781–788.
25.    Zhao C. Toward intelligent decision support for pharmaceutical product development. J. Pharm. Innovation. 2006;1:23–35.
26.    Rantanen J., Khinast J. The future of pharmaceutical manufacturing sciences. J. Pharm. Sci. 2015;104:3612–3638.
27.    Ketterhagen W.R. Process modeling in the pharmaceutical industry using the discrete element method. J. Pharm. Sci. 2009;98:442–470.
28.    Chen W. Mathematical model-based accelerated development of extended-release metformin hydrochloride tablet formulation. AAPS PharmSciTech. 2016;17:1007–1013.
29.    Meziane F. Intelligent systems in manufacturing: current developments and future prospects. Integr. Manuf. Syst. 2000;11:218–238.
30.    Steiner S. Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 2019;363:eaav2211.
31.    Faure A. Process control and scale-up of pharmaceutical wet granulation processes: a review. Eur. J. Pharm. Biopharm. 2001;52:269–277.
32.    Landin M. Artificial intelligence tools for scaling up of high shear wet granulation process. J. Pharm. Sci. 2017;106:273–277.
33.    Das M.K., Chakraborty T. ANN in pharmaceutical product and process development. In: Puri Munish., editor. Artificial Neural Network for Drug Design, Delivery and Disposition. Elsevier; 2016. pp. 277–293.
34.    Gams M. Integrating artificial and human intelligence into tablet production process. AAPS PharmSciTech. 2014;15:1447–1453. 35. Kraft, D.L. System and methods for the production of personalized drug products. US20120041778A1.
35.    Aksu B. 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;18:236–245.
36.    96. Goh W.Y. Application of a recurrent neural network to prediction of drug dissolution profiles. Neural Comput. Appl. 2002;10:311–317.
37.    Drăgoi E.N. On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. Drying Technol. 2013;31:72–81. 38. Reklaitis R. PharmaHub; 2008. Towards Intelligent Decision Support for Pharmaceutical Product Development.
38.    Wang X. 2009 International Conference on Computational Intelligence and Software Engineering. IEEE; 2009. Intelligent quality management using knowledge discovery in databases; pp. 1–4.
39.    Hay M. Clinical development success rates for investigational drugs. Nat. Biotechnol. 2014;32:40–51.

Recomonded Articles:

Author(s): Ganesh Shinde, Godage R. K, Dr R. S. Jadhav, Barhate Manoj, Bhagwat Aniket

DOI: 10.5958/2349-2988.2020.00005.4         Access: Open Access Read More

Author(s): Shahanshah Gulpham

DOI:         Access: Open Access Read More

Author(s): M. Vijaya Sekhar Reddy, K. Sasi, K. Ashalatha, M. Madhuri

DOI: 10.5958/2349-2988.2017.00056.0         Access: Open Access Read More

Author(s): Shashikant R Pattan, Nachiket S Dighe, H V Shinde, Deepak S Musmade, Mangesh B Hole, Vinayak M Gaware

DOI:         Access: Open Access Read More

Author(s): Rutuja S Nalkar, Suhas S Siddheshwar, Mahesh H Kolhe

DOI: 10.52711/2349-2988.2021.00036         Access: Open Access Read More

Author(s): Leena Sahu, Amit Roy, Trilochan Satapathy

DOI:         Access: Open Access Read More

Author(s): A.K. Meena, Brijendra Singh, Uttam Niranjan, A.K. Yadav, A.K. Nagaria, Kiran, A. Gaurav, Vertika Gautam, R.Singh

DOI:         Access: Open Access Read More

Author(s): Premjit S Nannaware, Suhas S. Siddheshwar, M.H. Kolhe

DOI: 10.52711/2349-2988.2021.00019         Access: Open Access Read More

Author(s): Sachin Kumar, Pratishtha Gupta

DOI: 10.5958/2349-2988.2016.00008.5         Access: Open Access Read More

Author(s): S. Sharma, Vikas Kumar Jain, Himanshu Shekhar Kar

DOI:         Access: Open Access Read More

Author(s): Vigy Elizebth Cherian

DOI: 10.5958/2349-2988.2019.00011.1         Access: Open Access Read More

Author(s): Mohini Shelke, Shraddha Parjane, S. D Mankar, S. S. Siddheshwar

DOI: 10.52711/2349-2988.2021.00023         Access: Open Access Read More

Author(s): Anuja Uddhav Deokar, Dr Suhas Siddheshwar, Sudarshan. B. Kakad

DOI: 10.5958/2349-2988.2020.00004.2         Access: Open Access Read More

Author(s): Debjit Bhowmik, K.P. Sampath Kumar, Lokesh Deb

DOI: 10.5958/2349-2988.2016.00012.7         Access: Closed Access Read More

Author(s): RS Jadhav, PN Kendre, MH Kolhe, S N Lateef, SM Shelke, RK Godge

DOI:         Access: Open Access Read More

Research Journal of Science and Technology (RJST) is an international, peer-reviewed journal, devoted to science and technology...... Read more >>>

RNI: Not Available                     
DOI: 10.5958/2349-2988 

Recent Articles