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
. 2024 Oct 14;16(10):1328.
doi: 10.3390/pharmaceutics16101328.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Dolores R Serrano  1   2 , Francis C Luciano  1 , Brayan J Anaya  1 , Baris Ongoren  1 , Aytug Kara  1 , Gracia Molina  1 , Bianca I Ramirez  1 , Sergio A Sánchez-Guirales  1 , Jesus A Simon  1 , Greta Tomietto  1 , Chrysi Rapti  1 , Helga K Ruiz  1 , Satyavati Rawat  3 , Dinesh Kumar  3 , Aikaterini Lalatsa  4   5
Affiliations
Review

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Dolores R Serrano et al. Pharmaceutics. .

Abstract

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Keywords: AI; artificial intelligence; drug delivery; drug development; lead optimization; personalized medicines; target identification.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic representation of the basic steps in machine learning classifier workflows.
Figure 2
Figure 2
Schematic representation of the main stages during the drug discovery and drug development process. The star represents those stages where AI plays a key role in pharmaceutical processes.
Figure 3
Figure 3
Schematic representation of Recursion Operating System algorithm.
Figure 4
Figure 4
AI predictive modeling in personalized medicines, drug formulation, drug–excipient compatibility, drug solubility, bioavailability, nanomedicines, and microfluidics.
Figure 5
Figure 5
Examples of AI applications in the pharmaceutical industry. Image created using OpenArt.
See this image and copyright information in PMC

References

    1. Dara S., Dhamercherla S., Jadav S.S., Babu C.M., Ahsan M.J. Machine Learning in Drug Discovery: A Review. Artif. Intell. Rev. 2022;55:1947–1999. doi: 10.1007/s10462-021-10058-4. - DOI - PMC - PubMed
    1. Carpenter K.A., Huang X. Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review. Curr. Pharm. Des. 2018;24:3347–3358. doi: 10.2174/1381612824666180607124038. - DOI - PMC - PubMed
    1. Carracedo-Reboredo P., Linares-Blanco J., Rodriguez-Fernandez N., Cedron F., Novoa F.J., Carballal A., Maojo V., Pazos A., Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J. 2021;19:4538–4558. doi: 10.1016/j.csbj.2021.08.011. - DOI - PMC - PubMed
    1. Bai F., Li S., Li H. AI enhances drug discovery and development. Natl. Sci. Rev. 2024;11:nwad303. doi: 10.1093/nsr/nwad303. - DOI - PMC - PubMed
    1. Hill A., True J.M., Jones C.H. Transforming drug development with synthetic biology and AI. Trends Biotechnol. 2024;42:1072–1075. doi: 10.1016/j.tibtech.2024.01.008. - DOI - PubMed

LinkOut - more resources