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Artificial Intelligence in Health New drug discovery in the AI era

the entire process of finding novel drugs and accelerating Srivastava
pre-clinical and clinical developments by combining
the predictive powers of GenAI with the knowledge and Ethics approval and consent to participate
experience of human researchers. Not applicable.
5. Conclusion Consent for publication

AI, in particular GenAI, is navigating drug hunters in Not applicable.
the development of new therapeutics and emerged as a
proficient alternative tool to the traditional drug discovery Availability of data
approach. The advanced AI/ML/DL transformative Not applicable.
tools are expediting drug discovery by assisting target
identification, computational chemistry, predicting drug– References
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clinical drug development. AI/ML-driven approaches have
been leveraged for designing clinical prototype formulation doi: 10.1007/s40273-021-01065-y
and also revolutionizing clinical trials by better patient 2. Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug
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Furthermore, AI/ML tools are being effectively utilized doi: 10.1016/j.apsb.2022.02.002
for generating clinical trial study protocols, comprehensive 3. Waring MJ, Arrowsmith J, Leach AR et al. An analysis of the
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voluminous data generated during multicentric clinical companies. Nat Rev Drug Discov. 2015;14:475-486.
trials. We may witness AI-generated drugs hitting the doi: 10.1038/nrd4609
market sooner than later.
4. Dueñas ME, Peltier-Heap RE, Leveridge M, Annan RS, Büttner
Acknowledgments FH, Trost M. Advances in high‐throughput mass spectrometry
in drug discovery. EMBO Mol Med. 2023;15(1):e14850.
The views, thoughts, and opinions expressed in this article
are solely of the corresponding author writing in his doi: 10.15252/emmm.202114850
individual capacity only and do not reflect the views of the 5. Komura H, Watanabe R, Mizuguchi K. The trends and
author’s employer, company, or other associated parties. future prospective of in silico models from the viewpoint
The authors acknowledge the support of Apoorva Kadlag of ADME evaluation in drug discovery. Pharmaceutics.
for helping with graphic designing and Anuja Bhardwaj for 2023;15(11):2619.
proofreading the final draft. doi: 10.3390/pharmaceutics15112619

Funding 6. Wang S, Dong G, Shenga C. Structural simplification: An
efficient strategy in lead optimization. Acta Pharm Sin B.
None. 2019;9(5):880-901.
doi: 10.1016/j.apsb.2019.05.004
Conflict of interest
7. Khalil AS, Jaenisch R, Mooney DJ. Engineered tissues and
Satinder Singh and Pratima Srivastava are employees in strategies to overcome challenges in drug development. Adv
DMPK, Aragen Life Sciences Limited, Hyderabad, India. Drug Deliv Rev. 2020;158:116-139.
The other author declares no conflict of interest. The doi: 10.1016/j.addr.2020.09.012
authors declare that they have no competing interests.
8. Wang L, Hu D, Xu J, Hu J, Wang Y. Complex in vitro model:
Author contributions A transformative model in drug development and precision
medicine. Clin Transl Sci. 2024;17(2):e13695.
Conceptualization: Satinder Singh
Data curation: Vyas Shingatgeri, Pratima Srivastava doi: 10.1111/cts.13695
Writing–original draft: Satinder Singh 9. Onakpoya IJ, Heneghan CJ, Aronson JK. Worldwide
Writing–review & editing: Satinder Singh, Pratima withdrawal of medicinal products because of adverse drug

Volume 2 Issue 2 (2025) 38 doi: 10.36922/aih.4423