Artificial intelligence and machine learning in drug discovery-a review
Abstract
The emergence of artificial intelligence (AI) and machine learning (ML) has revolutionized various sectors, including healthcare and pharmaceutical industries. Among the most transformative applications is their role in drug discovery and development. Traditionally a costly, laborious, and time-consuming process, drug discovery has significantly benefited from the integration of AI and ML technologies, enabling rapid identification of potential drug candidates, target identification, drug repurposing, and prediction of pharmacokinetics and toxicity. This article explores the principles, applications, benefits, challenges, and future perspectives of AI and ML in drug discovery, supported by contemporary references.
Keywords:
AI, drug discovery, machine learning
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References
1. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016;47:20–33.
2. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–260.
3. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250.
4. Zhang Y, Milinovich G, Xu Y. NLP in drug discovery. Brief Bioinform. 2020;21(2):468–478.
5. Wallach I, Dzamba M, Heifets A. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:1510.02855.
6. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An advanced generative adversarial autoencoder model for de novo drug design. Mol Pharm. 2017;14(9):3098–3104.
7. Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17(2):97–113.
8. Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2021;32(1):4–24.
9. Sosa DN, Tiwari A, et al. Drug repurposing through AI: Opportunities and challenges. Trends Pharmacol Sci. 2022;43(1):30–43.
10. Richardson P, Griffin I, Tucker C, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet. 2020;395(10223):e30–e31.
11. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361–370.
12. Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: Toxicity prediction using deep learning. Front Environ Sci. 2016;3:80.
13. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731.
14. Weng WH, et al. Predicting clinical trial enrollment using machine learning and EHR data. NPJ Digit Med. 2020;3:18.
15. Atomwise. https://www.atomwise.com
16. Insilico Medicine. https://www.insilico.com
17. BenevolentAI. https://www.benevolent.com
18. Jumper J, Evans R, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589.
19. Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1(9):727–730.
20. Kalil AC, Patterson TF, et al. Baricitinib plus remdesivir for hospitalized adults with COVID-19. N Engl J Med. 2021;384(9):795–807.
21. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318.
22. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
23. EMA. Reflection paper on the use of AI in medicinal product lifecycle. European Medicines Agency, 2021.
24. Mak K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773–780.
25. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications. ACM Trans IntellSyst Technol. 2019;10(2):1–19.
26. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017;549(7671):195–202.
2. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–260.
3. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250.
4. Zhang Y, Milinovich G, Xu Y. NLP in drug discovery. Brief Bioinform. 2020;21(2):468–478.
5. Wallach I, Dzamba M, Heifets A. AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:1510.02855.
6. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An advanced generative adversarial autoencoder model for de novo drug design. Mol Pharm. 2017;14(9):3098–3104.
7. Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17(2):97–113.
8. Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2021;32(1):4–24.
9. Sosa DN, Tiwari A, et al. Drug repurposing through AI: Opportunities and challenges. Trends Pharmacol Sci. 2022;43(1):30–43.
10. Richardson P, Griffin I, Tucker C, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet. 2020;395(10223):e30–e31.
11. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361–370.
12. Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: Toxicity prediction using deep learning. Front Environ Sci. 2016;3:80.
13. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731.
14. Weng WH, et al. Predicting clinical trial enrollment using machine learning and EHR data. NPJ Digit Med. 2020;3:18.
15. Atomwise. https://www.atomwise.com
16. Insilico Medicine. https://www.insilico.com
17. BenevolentAI. https://www.benevolent.com
18. Jumper J, Evans R, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589.
19. Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1(9):727–730.
20. Kalil AC, Patterson TF, et al. Baricitinib plus remdesivir for hospitalized adults with COVID-19. N Engl J Med. 2021;384(9):795–807.
21. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318.
22. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
23. EMA. Reflection paper on the use of AI in medicinal product lifecycle. European Medicines Agency, 2021.
24. Mak K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773–780.
25. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications. ACM Trans IntellSyst Technol. 2019;10(2):1–19.
26. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017;549(7671):195–202.
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How to Cite
Boddepalli, R., & VC, R. R. (2025). Artificial intelligence and machine learning in drug discovery-a review. Asian Journal of Hospital Pharmacy, 5(3), 18-20. https://doi.org/10.38022/ajhp.v5i3.107
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