Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 May 14;26(5):133.
doi: 10.1208/s12249-025-03134-3.

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

Nithin Vidiyala  1 , Pavani Sunkishala  2 , Prashanth Parupathi  3 , Dinesh Nyavanandi  4
Affiliations
Review

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

Nithin Vidiyala et al. AAPS PharmSciTech. .

Abstract

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

Keywords: artificial intelligence; deep learning; drug development; drug discovery; machine learning.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of interest: The authors declare no conflict of interests.

References

    1. Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A, et al. Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nat Biotechnol. 2019;37:1038–40. https://doi.org/10.1038/s41587-019-0224-x . - DOI - PubMed
    1. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. Highly Accurate Protein Structure Prediction with AlphaFold. Nature. 2021;596:583–9. https://doi.org/10.1038/s41586-021-03819-2 . - DOI - PubMed - PMC
    1. Bereczki Z, Benczik B, Balogh OM, Marton S, Puhl E, Pétervári M, Váczy-Földi M, Tamas Papp Z, Papp T, Makkos A, et al. Mitigating Off-target Effects of Small RNAs: Conventional Approaches, Network Theory and Artificial Intelligence. Br J Pharmacol. 2024;182:340–79. https://doi.org/10.1111/bph.17302 . - DOI - PubMed
    1. Jyoti Verma, G. Unleashing the Power of Artificial Intelligence: Exploring Multidisciplinary Frontiers for Innovation and Impact. In Artificial Intelligence for Intelligent Systems; Inam, U.K., Ed.; Taylor & Francis Group, 2024. ISBN 9781003496410.
    1. Mazumdar H, Khondakar KR, Das S, Halder A, Kaushik A. Artificial Intelligence for Personalized Nanomedicine; from Material Selection to Patient Outcomes. Expert Opin Drug Deliv 2024;85–100. https://doi.org/10.1080/17425247.2024.2440618 .

LinkOut - more resources