Volume : 10, Issue : 09, September – 2023

Title:

THE PARADIGM OF ARTIFICIAL INTELLIGENCE (AI) IN DRUG DISCOVERY AND DRUG DEVELOPMENT – A REVIEW

Authors :

Miss. Pemmaka Vara Lalitha, Mr. V. S. Chandrasekaran*, Dr. M. Kishore Babu

Abstract :

Artificial intelligence has decreased the workload of humans and improved the human standard of living. Artificial intelligence widening over the world not only in drug discovery and development of the drug but also in other aspects too. This article is mainly related to a paradigm shift of artificial intelligence (ai) in drug discovery and development to make them more operative and precise. Artificial intelligence has been increasing more in pharmaceutical industries. Conventional drug discovery is a spectacularly time-consuming and overpriced process. At the same time ai tools are modifying every stage of the drug discovery and development process, ai tools alter the speed and economics of the pharmaceutical industry. Artificial intelligence has been altering dramatically in many strands of pharmaceuticals. In this review article, we focus on the use of ai in different regions of pharmaceutical industries, along with drug discovery and development, enhancing pharmaceutical productivity and clinical trials.
Keywords: Paradigm, Artificial Intelligence, Machine Learning, Deep Learning

Cite This Article:

Please cite this article in press Pemmaka Vara Lalitha et al, The Paradigm Of Artificial Intelligence (Ai) In Drug Discovery And Drug Development – A Review, Indo Am. J. P. Sci, 2023; 10 (09).

Number of Downloads : 10

References:

1. Ramesh A. Artificial intelligence in medicine. Annals, The Royal College of Surgeons of England. 2004; 86:334–338. [PMC free article] [PubMed] [Google Scholar]
2. Wirtz B.W. Artificial intelligence and the public sector—Applications and Challenges. International Journal Public Administration. 2019; 42:596–615. [Google Scholar]
3. Mak K.-K., Pichika M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today. 2019;24:773–780. [PubMed] [Google Scholar]
4. Wang Y. A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach. Journal Computer-Aided Molecular Design. 2015;29:349–360. [PubMed] [Google Scholar]
5. Dana D. Deep learning in drug discovery and medicine; Scratching the surface. Molecules. 2018;23:2384. [PMC free article] [PubMed] [Google Scholar]
6. D Paul, G Sanap, S Shenoy, et al. Artificial intelligence in drug discovery and development. Drug Discovery Today, 26 (1) (2021), 80-93, 10.1016/j.drudis.2020.10.010
7. J Rantanen, J. Khinast. The future of pharmaceutical manufacturing sciences. Journal of Pharmaceutical Science. 104 (11) (2015), pp. 3612-3638, 10.1002/jps.24594
8. AH Göller, L Kuhnke, F Montanari, et al. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. 2015.
9. Harrer S. Artificial intelligence for clinical trial design. Trends Pharmacological Sciences. 2019; 40:577–591. [PubMed] [Google Scholar]
10. 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. [Google Scholar]
11. Xiao X. I Drug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. Journal Biomolecular Structure and Dynamics. 2015; 33:2221–2233. [PubMed] [Google Scholar]
12. Feng Q. Padme: a deep learning-based framework for drug–target interaction prediction. arXiv. 2018 arXiv:1807.09741. [Google Scholar]
13. Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018;557 S55–S55. [PubMed] [Google Scholar]
14. Chan H.S. Advancing drug discovery via artificial intelligence. Trends Pharmacological Sciences. 2019;40(8):592–604. [PubMed] [Google Scholar]