The Use of Artificial Intelligence in Drug Repurposing
Description
This poster explores drug repurposing, a strategy for identifying new therapeutic uses for existing medications, and highlights the transformative role of artificial intelligence (AI) in this process. By leveraging vast biomedical datasets, AI accelerates drug repurposing through predictive modeling, natural language processing, chemical similarity analysis, and genomic data evaluation. The advantages of AI, including speed, cost-efficiency, and data-driven insights, are discussed alongside challenges such as data quality, model interpretability, and regulatory concerns. Case studies, including the identification of baricitinib for COVID-19 and metformin for cancer treatment, illustrate AI's practical applications. Overall, AI presents significant opportunities to enhance drug discovery, although key challenges must be addressed to fully realize its potential.
Files
The Use of Artificial Intelligence in Drug Repurposing.pdf
Files
(116.8 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:3655652cb30d83d7dae9d8861b7d5fe1
|
116.8 kB | Preview Download |
Additional details
References
- 1. Kulkarni VS, Alagarsamy V, Solomon VR, Jose PA, Murugesan S. Drug Repurposing: An Effective Tool in Modern Drug Discovery. Russ J Bioorg Chem. 2023;49(2):157-166. doi: 10.1134/S1068162023020139. Epub 2023 Feb 21. PMID: 36852389; PMCID: PMC9945820.
- 2. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–477.
- 3. Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics. 2019 Dec 15;35(24):5191-5198. doi: 10.1093/bioinformatics/btz418. PMID: 31116390; PMCID: PMC6954645.
- 4. Li J, Zheng S, Chen B, et al. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17(1):2–12. doi:10.1093/bib/bbv046.
- 5. Percha B, Altman RB. A global network of biomedical relationships derived from text. Bioinformatics. 2018;34(15):2614–2624. doi:10.1093/bioinformatics/bty169.
- 6. Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462(7270):175–181. doi:10.1038/nature08512
- 7. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7. PMID: 27397505; PMCID: PMC4967469.
- 8. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges, and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58. doi:10.1038/nrd.2018.168
- 9. Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018 Apr 3;319(13):1317-1318. doi: 10.1001/jama.2017.18391. PMID: 29532063
- 10. Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 30:31–30:57. ISSN 1542-7730. URL http://doi.acm.org/10.1145/3236386.3241340
- 11. Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023 Jul 10;15(7):1916. PMID: 37514102; PMCID: PMC10385763.
- 12. Richardson P, Griffin I, Tucker C, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet. 2020;395(10223). doi:10.1016/S0140-6736(20)30304-4
- 13. Issa NT, Stathias V, Schürer S, Dakshanamurthy S. Machine and deep learning approaches for cancer drug repurposing. Semin Cancer Biol. 2021 Jan;68:132-142. doi: 10.1016/j.semcancer.2019.12.011. Epub 2020 Jan 3. PMID: 31904426; PMCID: PMC7723306