A Study on The Impact of Artificial Intelligence in Pharmaceuticals

Year : 2025 | Volume : 12 | Issue : 01 | Page : 24 32
    By

    Vedant V. Patil,

  • Roshan.m.Chaudhari,

  • S.P. Pawar,

  1. Student, Department of Pharmaceutics, Poojya Sane Guruji Vidya Prasarak Mandal, Maharashtra, India
  2. Assistant Professor, Department of Pharmaceutics, Poojya Sane Guruji Vidya Prasarak Mandal, Shahada, Maharashtra, India
  3. Principal, Department of Pharmaceutics, Poojya Sane Guruji Vidya Prasarak Mandal, Shahada,, Maharashtra, India

Abstract

The main goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem-solving, and decision-making. AI is becoming more and more significant in several pharmacy domains, including polypharmacology, hospital pharmacy, drug discovery, and drug delivery formulation development. Various types of artificial neural networks (ANNs), including deep neural networks (DNNs) and recurrent neural networks (RNNs), are utilized in the development of drug delivery formulations and in drug discovery. The technology’s promise in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR) has been supported by several drug discovery implementations that have been studied thus far. In terms of desired/optimal properties, de novo design also promotes the creation of significantly more innovative medical substances. This review paper discusses the application of AI in pharmacy, specifically in areas, such as polypharmacology, the development of drug delivery formulations, drug discovery, and hospital pharmacy. A branch of computer science called artificial intelligence makes it possible for machines to function efficiently. Because of its capacity to manage complex data processing tasks, which has enhanced workflow efficiency, its use in pharmaceutical technology has expanded. Reducing operating expenses while enhancing safety, precision, and efficiency. It might help us conserve time and resources while enhancing our comprehension of the relationships among different formulations and process parameters. Research in artificial intelligence (AI) has surged, and it has been shown that AI technology can analyze and understand data across several key areas in pharmacy, including hospital pharmacy, formulation design, and drug discovery.

Keywords: :- Artificial intelligence, Drug discovery, Drug delivery research, Hospital pharmacy ,Telepsychology, MEDi Robot.

[This article belongs to Research & Reviews: A Journal of Drug Design & Discovery ]

How to cite this article:
Vedant V. Patil, Roshan.m.Chaudhari, S.P. Pawar. A Study on The Impact of Artificial Intelligence in Pharmaceuticals. Research & Reviews: A Journal of Drug Design & Discovery. 2025; 12(01):24-32.
How to cite this URL:
Vedant V. Patil, Roshan.m.Chaudhari, S.P. Pawar. A Study on The Impact of Artificial Intelligence in Pharmaceuticals. Research & Reviews: A Journal of Drug Design & Discovery. 2025; 12(01):24-32. Available from: https://journals.stmjournals.com/rrjoddd/article=2025/view=194780


References

  1. Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019; 24(3):773-80 https://doi.org/10.1016/j.drudis.2018.11.014 .
  2. Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151-152:169-90. https://doi.org/10.1016/j.addr.2019.05.001
  3. Russel S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. AI Mag. 2015;36(4):105-14 DOI: https://doi.org/10.1609/aimag.v36i4.2577 .
  4. Duch W, Setiono R, Zurada JM. Computational intelligence methods for rulebased data understanding. Proc IEEE. 2004;92(5):771-805 DOI: 10.1109/JPROC.2004.826605 .
  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 https://doi.org/10.1021/js950433d .
  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 https://doi.org/10.1517/17425250902753261 .
  9. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717-27 https://doi.org/10.1016/S0731-7085(99)00272-1 .
  10. Zhang ZH, Wang Y, Wu WF, Zhao X, Sun XC, Wang HQ. Development of glipizide push-pull osmotic pump controlled release tablets by using expert system and artificial neural network. Yao Xue Xue Bao. 2012;47(12):1687-95
  11. 11.Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure–activity               relationships. J Chem Inf Model. 2015;55(2):263-74.
  1. Mayr A, Klambauer G, Unterthiner T, Hochreither S. Deep Tox: Toxicity prediction using Deep Learning. Front Environ Sci. 2016;3:80 https://doi.org/10.3389/fenvs.2015.00080 .
  2. 13. Merk D, Friedrich L, Grisoni F, Schneider G. De novo design of bioactive small molecules by artificial intelligence.                 Mol Inform. 2018;37(1-2):1-4 https://doi.org/10.1002/minf.201700153
  3. 14. Bishop CM. Model-based machine learning. Philos Trans A Math Phys Eng Sci. 2013;371(1984):20120222           https://doi.org/10.1098/rsta.2012.0222 .
  1. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016; 47:20-33. DOI:10.1016/j.jhealeco.2016.01.012
  2. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019; 20(2):273-86. https://doi.org/10.1093/biostatistics/kxx069
  3. Russell SJ, Norvig P, Davis E. Artificial intelligence: A modern approach. London: Prentice Hall; Pearson,2016.
  4. Dill KA, MacCallum JL. The protein-folding problem, 50 years on. Science. 2012; 338(6110):1042-6. DOI:10.1126/science.1219021
  5. Manikiran SS, Prasanthi NL. Artificial Intelligence: Milestones and Role in Pharma and Healthcare Sector. Pharma Times. 2019;51(1):9-15.
  6. Cherkasov A, Hilpert K, Jenssen H, Fjell CD, Waldbrook M, Mullaly SC, et al. Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic resistant superbugs. ACS Chem Biol. 2009;4(1):65-74 https://doi.org/10.1021/cb800240j .
  7. 21.Arend Hintze. Understanding the four types of AI. [cited 2022 13 June]; Available from:       https://theconversation.com/understanding-the-fourtypes-of-ai-from-reactive-robots-to-self-aware-beings 67616.
  1. Silver D, Schrittwieser J, Simonyan K. Mastering the game of Go without human knowledge. Nature 2017; 550:354–359.
  2. Mulholland M, A comparison of classification in artificial intelligence. Induction versus a self-organising neural networks Chemometrics and Intelligent Laboratory Systems, 1995;30(1): 117-128 https://doi.org/10.1016/0169-7439(95)00050-X .
  3. Shakya S, Analysis of artificial intelligence based image classification techniques, Journal of Innovative Image Processing (JIIP), 2020; 2(01):44-54 https://doi.org/10.36548/jiip .
  4. Arend Hintze, Understanding the four types of AI. [cited 2022. 13 June] Available from: https://theconversation.com/understanding-the-four-types-ofai-from-reactive-robots-to-self-aware-beings-67616.
  5. Markoff J. On ‘Jeopardy’ Watson Win is All but Trivial, New York City, The New York Times,2017 https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html
  6. Troulis M, Everett P, Seldin E, Kikinis R, Kaban L, Development of a three-dimensional treatment planning system based on computed tomographic data, nternational journal of oral and maxillofacial surgery 2002; 31(4):349–357 https://doi.org/10.1054/ijom.2002.0278 .
  7. Arimura H, Soufi M, Kamezawa H, Ninomiya K, Yamada M, Radiomics with artificial intelligence for precision medicine in radiation therapy, J. Radiat. Res, 2019; 60(1): 150–157 https://doi.org/10.1093/jrr/rry077 .
  8. Schmidt-Erfurth U, Sadeghipour A, Gerendas B.S, Waldstein S.M, Bogunovi H, Artificial intelligence in retina, Progress in retinal and eye research. 2018 Nov 1;67:1-29. https://doi.org/10.1016/j.preteyeres.2018.07.004
  9. Zhang ZH, Wang Y, Wu WF, Zhao X, Sun XC, Wang HQ, Development of glipizide push-pull osmotic pump controlled release tablets by using expert system and artificial neural network. 2012;47(12):1687-1695
  10. Feng R, Badgeley M, Mocco J, Oermann EK. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg. 2018 Apr;10(4):358-362. doi: 10.1136/neurintsurg-2017-013355.
  11. Davatzikos C. Machine learning in neuroimaging: Progress and challenges. Neuroimage. 2019 Aug 15;197:652-6. https://doi.org/10.1016/j.neuroimage.2018.10.003
  12. NagaRavi Kiran T, Suresh Kumar N, Lakshmi GVN, Naseema S, Bhargav SB, Mohiddien SM; Artificial Intelligence in Pharmacy; Der Pharmacia Lettre, 2021, 13(5):06-14. https://www.scholarsresearchlibrary.com/abstract/artificial-intelligence-in-pharmacy-69924.html
  13. Sharma, Tamanna, et al. Artificial intelligence in advanced pharmacy. International Journal of Science and Research Archive, 2021, 2.(1): 047-54

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 23/11/2024
Accepted 16/01/2025
Published 20/01/2025


Login


My IP

PlumX Metrics