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Artificial Intelligence in Health AI in ocular drug discovery and development

and analytical capabilities with these novel therapeutic Consent for publication
approaches, researchers can accelerate the development of
personalized medicine strategies for ophthalmic diseases. Not applicable.

Moreover, AI is set to play a crucial role in overcoming Availability of data
the challenges associated with the clinical trial phase of Not applicable.
drug development. By predicting patient responses to
13
potential treatments and identifying the most suitable References
candidates for participation in trials, AI can streamline the 1. Wang N, Zhang Y, Wang W, et al. How can machine learning
process of participant recruitment. This would, in turn, and multiscale modeling benefit ocular drug development?
lead to faster, more cost-effective trials that also enable Adv Drug Deliv Rev. 2023;196:114772.
researchers to fine-tune dosage and treatment protocols
earlier, reducing the probability of unforeseen adverse doi: 10.1016/j.addr.2023.114772
reactions and late-stage trial failures. 13 2. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK.
Artificial intelligence in drug discovery and development.
6. Conclusion Drug Discov Today. 2021;26(1):80-93.
The integration of AI into ophthalmic drug discovery doi: 10.1016/j.drudis.2020.10.010
and development represents a paradigm shift, offering 3. Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic
novel approaches to longstanding challenges in this field. target discovery. Trends Pharmacol Sci. 2023;44(9):561-572.
Using AI in target identification, compound screening, doi: 10.1016/j.tips.2023.06.010
and predictive toxicology, we are on the brink of making
substantial progress in the treatment of eye diseases. 4. Crampon K, Giorkallos A, Deldossi M, Baud S, Steffenel LA.
The successful applications and case studies discussed Machine-learning methods for ligand-protein molecular
underscore AI’s potential to enhance drug delivery docking. Drug Discov Today. 2022;27(1):151-164.
systems, refine disease management, and expedite drug doi: 10.1016/j.drudis.2021.09.007
development. However, addressing challenges and 5. Wang MWH, Goodman JM, Allen TEH. Machine learning
limitations, including data quality, computational resource in predictive toxicology: Recent applications and future
requirements, model interpretability, and regulatory issues, directions for classification models. Chem Res Toxicol.
is paramount to fully realizing its transformative potential. 2021;34(2):217-239.
Nonetheless, the continued evolution of AI technologies, doi: 10.1021/acs.chemrestox.0c00316
coupled with their integration into emerging therapeutic
modalities, is likely to pave the way for advancements that 6. Yang S, Kar S. Application of artificial intelligence and
machine learning in early detection of adverse drug
will redefine our approach to treating ophthalmic diseases, reactions (ADRs) and drug-induced toxicity. Artif Intell
ultimately improving patient outcomes and quality of life. Chem. 2023;1(2):100011.

Acknowledgments doi: 10.1016/j.aichem.2023.100011
None. 7. Hsueh HT, Chou RT, Rai U, et al. Machine learning-driven
multifunctional peptide engineering for sustained ocular
Funding drug delivery. Nat Commun. 2023;14(1):2509.
None. doi: 10.1038/s41467-023-38056-w
8. Remtulla R, Das SK, Levin LA. Predicting absorption-
Conflict of interest distribution properties of neuroprotective phosphine-
The authors declare that they have no competing interests. borane compounds using in silico modeling and machine
learning. Molecules. 2021;26(9):2505.
Author contributions doi: 10.3390/molecules26092505
Conceptualization: Michael Balas 9. Li Z, Wang L, Wu X, et al. Artificial intelligence in
Writing – original draft: Siddharth Gandhi ophthalmology: The path to the real-world clinic. Cell Rep
Writing – review & editing: All authors Med. 2023;4(7):101095.
doi: 10.1016/j.xcrm.2023.101095
Ethics approval and consent to participate
10. Jia Z, Chen J, Xu X, et al. The importance of resource
Not applicable. awareness in artificial intelligence for healthcare. Nat Mach

Volume 1 Issue 3 (2024) 29 doi: 10.36922/aih.3341