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Year 2024, Volume: 49 Issue: 3, 583 - 602, 23.10.2024
https://doi.org/10.55262/fabadeczacilik.1411661

Abstract

References

  • Zhu, W., Liu, X., Li, Q., Gao, F., Liu, T., Chen, X., ... & Zhavoronkov, A. (2023). Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models. Bioorganic & Medicinal Chemistry, 91, 117414. doi: 10.1016/j.bmc.2023.117414

The Rise of Artificial Intelligence in Pharma: Shaping the Future of Drug Discovery

Year 2024, Volume: 49 Issue: 3, 583 - 602, 23.10.2024
https://doi.org/10.55262/fabadeczacilik.1411661

Abstract

Drug discovery as an important scientific area that serves human health, requires continuous advancement for improved quality of life and survival rates. However, drug discovery is a long and expensive process. The studies aimed at dealing with these problems have enabled to combination of AI with drug development stages. For every step of the R&D process, AI plays a vital role in facilitating and accelerating the work. Firstly, AI methods (deep learning and convolutional neural networks) help predict the 3D structure of an unresolved protein making it easier for the rational design of compounds to target a specific protein among other potential outcomes. After estimation of the protein structure of interest, it is also possible to determine the protein-ligand interactions by utilizing AI technologies like random forest. The other stage, namely finding the hit compounds is also possible through AI-assisted QSAR models such as deep neural networks. Besides, there are many AI methods (k-nearest neighbor and support vector machines) for ADMET prediction to optimize lead compounds. Finally, AI techniques also aid in choosing the most suitable synthesis plan. In the light of the latest advances, AI has become the focus of the pharmaceutical industry. However, despite the potential benefits of AI in drug discovery, several challenges must be considered including the availability of suitable data and bioethical issues. This article provides a comprehensive review of the benefits and applications of AI in various stages of drug discovery. In addition, the open-source act model and bioethical issues are also discussed.

Ethical Statement

The authors declare that there are no conflict of interest.

References

  • Zhu, W., Liu, X., Li, Q., Gao, F., Liu, T., Chen, X., ... & Zhavoronkov, A. (2023). Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models. Bioorganic & Medicinal Chemistry, 91, 117414. doi: 10.1016/j.bmc.2023.117414
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Details

Primary Language English
Subjects Pharmaceutical Chemistry
Journal Section Reviews
Authors

Ayça Dedeoglu Erdogan 0000-0001-5936-5328

Armanç Mat This is me 0009-0001-8379-3987

Enise Ece Gürdal 0000-0003-1064-8639

Meric Koksal Akkoc 0000-0001-7662-9364

Publication Date October 23, 2024
Submission Date December 29, 2023
Acceptance Date September 24, 2024
Published in Issue Year 2024 Volume: 49 Issue: 3

Cite

APA Dedeoglu Erdogan, A., Mat, A., Gürdal, E. E., Koksal Akkoc, M. (2024). The Rise of Artificial Intelligence in Pharma: Shaping the Future of Drug Discovery. Fabad Journal of Pharmaceutical Sciences, 49(3), 583-602. https://doi.org/10.55262/fabadeczacilik.1411661