A Review on Artificial Intellegence in Drug Discovery & Pharmaceutical Industry
DOI:
https://doi.org/10.22270/ajprd.v11i3.1252Keywords:
Artificial intelligence, Health care, Drug Discovery, IndustryAbstract
Introduction: The use of artificial intelligence (AI) in drug discovery and the pharma industry has been rapidly expanding in recent years. AI algorithms can analyze vast amounts of data, identify patterns, and make predictions that can accelerate drug discovery and improve patient outcomes.
Methods: AI is being used in various stages of the drug discovery process, from target identification and lead optimization to clinical trials and post-market surveillance. Machine learning algorithms, neural networks, and natural language processing are among the AI techniques used in drug discovery.
Results: AI-based drug discovery has already shown promising results, with several drugs in clinical trials or approved for use that were discovered using AI. AI is also being used to improve clinical trial design and patient selection, as well as to monitor adverse drug events and optimize drug dosing.
Conclusion: AI has the potential to transform the drug discovery and pharma industry, making drug development faster, more efficient, and more effective. However, there are still challenges that need to be addressed, such as the need for high-quality data and the potential for bias in AI algorithms. Overall, the use of AI in drug discovery and the pharma industry is an exciting and rapidly evolving field that has the potential to improve patient outcomes and revolutionize healthcare.
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References
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46. Chen, X., et al. (2018). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 54, 1-4.
47. Ching, T., et al.Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 2018; 15(141):20170387.
48. Iqbal, U., et al. Artificial intelligence in healthcare: Past, present and future. American Journal of Drug Discovery and Development, 2019; 9(1):1-13.
49. Marquardt, D., et al. Ethical considerations of using artificial intelligence in health care. AMA Journal of Ethics, 2020; 22(5):E418-E424.
50. Obermeyer, Z., et al. Predicting the future—Big data, machine learning, and clinical medicine. New England Journal of Medicine, 2016; 375(13):1216-1219.
51. Rajkomar, A., et al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine,2018; 1(1):18.
52. Wang, Y., et al. Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology,2018; 54:1-4.
53. Yang, G. Z., Nelson, B. J., Murphy, R. R., &Choset, H. Robotics and automation in surgery: Introduction to the IEEE Transactions on Medical Robotics and Bionics special issue. IEEE Transactions on Medical Robotics and Bionics, 2019; 1(1):1-4.
54. Riaz, A., & Chaudhry, A. Future prospects of artificial intelligence in surgery. Journal of the Pakistan Medical Association, 2019; 69 (Suppl 3):S36-S39.
55. Zhang, L., Tan, J., Han, S., & Li, X.. Artificial intelligence in surgical education: A systematic review. Annals of Translational Medicine, 2020; 8(17):1107.
56. Alizadeh, M., &Eslami, V. The future prospects of artificial intelligence in radiotherapy. Journal of Biomedical Physics and Engineering, 2021; 11(2):125-132.
57. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., & Williams, G. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 2019; 18(6):463-477.
58. Kaur, P., & Kumar, M. Role of artificial intelligence in drug discovery. Expert Opinion on Drug Discovery, 2019; 14(12):1245-1256.
2. Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958; 65:386–408.
3. Zadeh LA. Fuzzy sets. Inf Control 1965; 8:338–53
4. Cellan-Jones R. Stephen Hawking Warns Artificial Intelligence could End Mankind. Available from: http:// www.bbc.com/news/technology-30290540. [Last accessed on 2017 Jun 24].
5. D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G.,& Dieleman, S. Mastering the game of Go with deep neural networks and tree search. nature, 2016; 529(7587):484-489
6. McHugh R, Rascon J. Meet MEDi, the Robot Taking Pain Out of Kids’ Hospital Visits. Available from: http:// www.nbcnews.com/news/us-news/meet-medi-robottaking-pain-out-kids-hospital-visits-n363191. [Last accessed on 2017 Jun 24].McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5:115–33.8. Rosenblatt F. The Perceptron: a probabilistic model for information storage and organisation in the brain. Psychol Rev 1958; 65:386–408.
7. Trynacit K. MEDi Robot to Comfort Patients in Stollery Children’s Hospital. Available from: http://www.cbc. ca/news/canada/edmonton/medi-robot-to-comfortpatients-in-stollery-children-s-hospital-1.3919867
8. Eye for Pharma. Artificial Intelligence - A Brave New World for Pharma. Available from: http://www.social. eyeforpharma.com/clinical/artificial-intelligence-bravenew-world-pharma. [Last accessed on 2017 Jun 24].
9. McCurry J. Erica, ‘most intelligent’ Android, Leads Japan’s Robot Revolution. Available from: http:// www.thehindu.com/todays-paper/tp-national/ Erica-%E2%80%98most-intelligent%E2%80%99- android-leads-Japan%E2%80%99s-robot-revolution/ article13974805.ece [Last accessed on 2017 Jun 24].
10. Aethon. TUG robots. Available from: http://www.aethon. Com/tug/tug healthcare/. [Last accessed on 2017 Jun 24].
11. Zhu, T. et al. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J. Med. Chem. 2013; 56:6560–6572
12. Hall, D.R. et al. Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. J. Chem. Inf. Model.2012; 52:199–209 Zadeh LA. Fuzzy sets. Inf Control 1965; 8:338–53
13. Johnston PA, et al. High-throughput screening for target identification and drug discovery. Bioorganic & Medicinal Chemistry, 2014; 22(5):1371-1377.
14. Lipinski CA. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technologies, 2004; 1(4):337-341.
15. Sams-Dodd F. Target-based drug discovery: Is something wrong? Drug Discovery Today, 2005; 10(2):139-147.
16. Russell B. Clinical trial design for drug development. Journal of Pharmacy and Pharmacology, 2006; 58(1): 3-8.
17. Getz KA, et al. Assessing the impact of the regulatory environment on drug development and approval. Clinical Pharmacology & Therapeutics, 2016; 100(4): 451-454.
18. Swinney DC, Anthony J. How were new medicines discovered? Nature Reviews Drug Discovery, 2011; 10(7): 507-519.
19. Yang H, et al. In silico drug design: Repurposing techniques and methodology. Current Topics in Medicinal Chemistry, 2018; 18(18):1575-1586.
20. Barrangou R, et al. CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology, 2014; 32(4): 347-355.
21. Patti GJ, Yanes O, Siuzdak G. Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 2012; 13(4):263-269.
22. Caffrey M. A comprehensive review of the lipid cubic phase or in meso method for crystallizing membrane and soluble proteins and complexes. Acta Crystallographica Section F, 2015; 71(1): 3-18.
23. Dnyaneshwar K, Gaurav S, Debleena P, et al.. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. the future of pharmaceutical product development and research. Elsevier; 2020; 73–107. doi:10.1016/B978-0-12-814455-8.00003-7.
24. Zhu, H. Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology 2020; 60:573–589
25. Ciallella, H.L. and Zhu, H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chemical Research in Toxicology 2019; 32, 536–547.
26. Mak KK. Artificial intelligence in drug development: present status and future prospects. Kuala Lumpur: Elsevier; 2019 Mar 3.
27. Álvarez-Machancoses, Ó. and Fernández-Martínez, J.L. Using artificial intelligence methods to speed up drug discovery. Expert Opinion Drug Discovery 2019; 14:769–777
28. Fleming, N. How artificial intelligence is changing drug discovery. Nature 2018; 557:S55–S55
29. Dana, D. et al.Deep learning in drug discovery and medicine; scratching the surface. Molecules 2018; 23, 2384
30. Wan, F. and Zeng, J. Deep learning with feature embedding for compound–protein interaction prediction. bioRxiv 2016; 086033
31. AlQuraishi, M. End-to-end differentiable learning of protein structure. Cell Systems 2019; 8:292–301
32. 57 Hutson, M. (2019) AI protein-folding algorithms solve structures faster than ever. Nature XX, YYY–ZZZ
33. Avdagic, Z. et al. (2009) Artificial intelligence in prediction of secondary protein structure using CB513 database. Summit on Translational Bioinformatics 2009, 1
34. Bielecki A., Bielecki A. Foundations of artificial neural networks. In: KacprzykJanusz., editor. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing; 2019;15–28. Polish academy of sciences, Warsaw, Poland.
35. Steels L., Brooks R. Routledge; 2018. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents.
36. Bielecki A., Bielecki A. Foundations of artificial neural networks. In: KacprzykJanusz., editor. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing; 2019; 15–28. Polish academy of sciences, Warsaw, Poland.
37. Kalyane D. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade Rakesh K., editor. The Future of Pharmaceutical Product Development and Research. Elsevier; 2020; 73–107.
38. Da Silva I.N. Springer; 2017. Artificial Neural Networks:
39. Medsker L., Jain L.C. CRC Press; 1999. Recurrent Neural Networks: Design and Applications.
40. Hänggi M., Moschytz G.S. Springer Science & Business Media; 2000. Cellular Neural Networks: Analysis, Design and Optimization.
41. Vyas M. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J. Pharm. 2018; 12:72–76.
42. Hassanzadeh, P. et al. The significance of artificial intelligence in drug delivery system design. Advanced Drug Delivery Reviews 2019; 151:169–190
43. 118 Luo, M. et al. (2018) Micro‐/nanorobots at work in active drug delivery. Advanced Functional Materials 28, 1706100
44. 119 Fu, J. and Yan, H. Controlled drug release by a nanorobot. Nature Biotechnology 2012; 30:407–408
45. Research and Markets. Research and Markets; 2019. Global Growth Insight - Role of AI in the Pharmaceutical Industry 2018-2022: Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry.
46. Chen, X., et al. (2018). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 54, 1-4.
47. Ching, T., et al.Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 2018; 15(141):20170387.
48. Iqbal, U., et al. Artificial intelligence in healthcare: Past, present and future. American Journal of Drug Discovery and Development, 2019; 9(1):1-13.
49. Marquardt, D., et al. Ethical considerations of using artificial intelligence in health care. AMA Journal of Ethics, 2020; 22(5):E418-E424.
50. Obermeyer, Z., et al. Predicting the future—Big data, machine learning, and clinical medicine. New England Journal of Medicine, 2016; 375(13):1216-1219.
51. Rajkomar, A., et al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine,2018; 1(1):18.
52. Wang, Y., et al. Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology,2018; 54:1-4.
53. Yang, G. Z., Nelson, B. J., Murphy, R. R., &Choset, H. Robotics and automation in surgery: Introduction to the IEEE Transactions on Medical Robotics and Bionics special issue. IEEE Transactions on Medical Robotics and Bionics, 2019; 1(1):1-4.
54. Riaz, A., & Chaudhry, A. Future prospects of artificial intelligence in surgery. Journal of the Pakistan Medical Association, 2019; 69 (Suppl 3):S36-S39.
55. Zhang, L., Tan, J., Han, S., & Li, X.. Artificial intelligence in surgical education: A systematic review. Annals of Translational Medicine, 2020; 8(17):1107.
56. Alizadeh, M., &Eslami, V. The future prospects of artificial intelligence in radiotherapy. Journal of Biomedical Physics and Engineering, 2021; 11(2):125-132.
57. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., & Williams, G. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 2019; 18(6):463-477.
58. Kaur, P., & Kumar, M. Role of artificial intelligence in drug discovery. Expert Opinion on Drug Discovery, 2019; 14(12):1245-1256.
Published
2023-06-15
How to Cite
Laddha, C., Shelke, A., Vaidya, Y., Sheikh, A., & Biyani, K. (2023). A Review on Artificial Intellegence in Drug Discovery & Pharmaceutical Industry. Asian Journal of Pharmaceutical Research and Development, 11(3), 45–51. https://doi.org/10.22270/ajprd.v11i3.1252
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Section
Review Articles
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