Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development

  • Madhukiran Parvathaneni Professor, Biotechnology, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101 https://orcid.org/0000-0003-2747-4882
  • Abduselam K. Awol Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101
  • Monika Kumari Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101
  • Ke Lan Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101
  • Manisha Lingam Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Abstract

Drug discovery has traditionally been a time consuming and expensive endeavor. Additionally, drugs weren’t as effectively designed as those that are being predicted and developed through AI and ML today. Machine learning is a form of artificial intelligence that develops and evolves based on experience (similarly to the human mind), and is more recently being utilized in drug discovery and design. The integration of AI and ML into the drug discovery and development process has allowed for higher target precision, lower toxicity, and better dosage formulations. AI more generally has been introduced to and has been leveraged at, each step of drug development, including target identification and validation, hit identification, as well as hit to lead optimization, and has been key in shortening the previously lengthy drug screening process. AI and ML has also been applied downstream in drug formulation where it has maximized resource utilization and is allowing for web-based 3D printing of drugs. Application of AI in the drug development process has also been extended to the modeling of novel drug-like compounds to predict their ADMET properties. This review will address the stages of drug discovery and development in which the application of AI and ML modeling has altered the traditional development of drugs.


Keywords: Drug discovery, machine learning, artificial intelligence, computational drug development.

Keywords: Drug discovery, machine learning, artificial intelligence, computational drug development

Downloads

Download data is not yet available.

Author Biographies

Madhukiran Parvathaneni, Professor, Biotechnology, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Abduselam K. Awol, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Monika Kumari, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Ke Lan, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Manisha Lingam, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA 17101

References

1. Savage N. Tapping into the drug discovery potential of AI. Biopharm Deal. 2021 May 27; d43747-021-00045-7. https://doi.org/10.1038/d43747-021-00045-7
2. Chan HCS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing Drug Discovery via Artificial Intelligence. Trends in Pharmacological Sciences. 2019 Aug; 40(8):592-604. https://doi.org/10.1016/j.tips.2019.06.004
3. Sinha S, Vohora D. Drug Discovery and Development. In: Pharmaceutical Medicine and Translational Clinical Research [Internet]. Elsevier; 2018. p. 19-32. Available from: https://linkinghub.elsevier.com/retrieve/pii/B978012802103300002X https://doi.org/10.1016/B978-0-12-802103-3.00002-X
4. Jung YL, Yoo HS, Hwang J. Artificial intelligence-based decision support model for new drug development planning. Expert Systems with Applications. 2022 Jul; 198:116825. https://doi.org/10.1016/j.eswa.2022.116825
5. Schauperl M, Denny RA. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges. J Chem Inf Model. 2022 Jul 11; 62(13):3142-56. https://doi.org/10.1021/acs.jcim.2c00026
6. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019 Mar; 24(3):773-80. https://doi.org/10.1016/j.drudis.2018.11.014
7. Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol. 2013 Apr; 9(4):232-40. https://doi.org/10.1038/nchembio.1199
8. Srivastava R. Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities. ACS Omega. 2021 Sep 28;6(38):24891-901. https://doi.org/10.1021/acsomega.1c03736
9. Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. Alzheimer's & Dementia: Translational Research & Clinical Interventions. 2017 Nov; 3(4):651-7. https://doi.org/10.1016/j.trci.2017.10.005
10. Mullard A. What does AlphaFold mean for drug discovery? Nat Rev Drug Discov. 2021 Oct; 20(10):725-7. https://doi.org/10.1038/d41573-021-00161-0
11. Callaway E. What's next for AlphaFold and the AI protein-folding revolution. Nature. 2022 Apr 14; 604(7905):234-8. https://doi.org/10.1038/d41586-022-00997-5
12. Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B. 2022 Sep 1; 126(34):6372-83. https://doi.org/10.1021/acs.jpcb.2c04346
13. Aittokallio T. What are the current challenges for machine learning in drug discovery and repurposing? Expert Opinion on Drug Discovery. 2022 May 4; 17(5):423-5. https://doi.org/10.1080/17460441.2022.2050694
14. Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, et al. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc. 2022 Mar; 17(3):672-97. https://doi.org/10.1038/s41596-021-00659-2
15. Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, et al. A review on machine learning approaches and trends in drug discovery. Computational and Structural Biotechnology Journal. 2021; 19:4538-58. https://doi.org/10.1016/j.csbj.2021.08.011
16. Dreiman GHS, Bictash M, Fish PV, Griffin L, Svensson F. Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding. SLAS Discovery. 2021 Feb; 26(2):257-62. https://doi.org/10.1177/2472555220949495
17. Jia L, Gao H. Machine Learning for In Silico ADMET Prediction. In: Heifetz A, editor. Artificial Intelligence in Drug Design [Internet]. New York, NY: Springer US; 2022 [cited 2022 Dec 5]. p. 447-60. (Methods in Molecular Biology; vol. 2390). Available from: https://link.springer.com/10.1007/978-1-0716-1787-8_20 https://doi.org/10.1007/978-1-0716-1787-8_20
18. Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, et al. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem. 2020 Sep 11; 8:726. https://doi.org/10.3389/fchem.2020.00726
19. Vijayan RSK, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discovery Today. 2022 Apr; 27(4):967-84. https://doi.org/10.1016/j.drudis.2021.11.023
20. Walters WP. Going further than Lipinski's rule in drug design. Expert Opinion on Drug Discovery. 2012 Feb; 7(2):99-107. https://doi.org/10.1517/17460441.2012.648612
21. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021 Aug; 25(3):1315-60. https://doi.org/10.1007/s11030-021-10217-3
22. Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, et al. QSAR without borders. Chem Soc Rev. 2020; 49(11):3525-64. https://doi.org/10.1039/D0CS00098A
23. Verma J, Khedkar V, Coutinho E. 3D-QSAR in Drug Design - A Review. CTMC. 2010 Jan 1; 10(1):95-115. https://doi.org/10.2174/156802610790232260
24. Kwon S, Bae H, Jo J, Yoon S. Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinformatics. 2019 Dec; 20(1):521. https://doi.org/10.1186/s12859-019-3135-4
25. Van Valkenburg W, editor. Biological Correlations-The Hansch Approach [Internet]. WASHINGTON, D. C.: AMERICAN CHEMICAL SOCIETY; 1974 [cited 2022 Dec 5]. (Advances in Chemistry; vol. 114). Available from: https://pubs.acs.org/doi/book/10.1021/ba-1972-0114 https://doi.org/10.1021/ba-1972-0114
26. Cronin MTD, Schultz TW. Pitfalls in QSAR. Journal of Molecular Structure: THEOCHEM. 2003 Mar; 622(1-2):39-51. https://doi.org/10.1016/S0166-1280(02)00616-4
27. Clark R. Prospective Ligand- and Target-Based 3D QSAR: State of the Art 2008. CTMC. 2009 Jun 1; 9(9):791-810. https://doi.org/10.2174/156802609789207118
28. Cruz VL, Martinez S, Ramos J, Martinez-Salazar J. 3D-QSAR as a Tool for Understanding and Improving Single-Site Polymerization Catalysts. A Review. Organometallics. 2014 Jun 23; 33(12):2944-59. https://doi.org/10.1021/om400721v
29. Jagiello K, Grzonkowska M, Swirog M, Ahmed L, Rasulev B, Avramopoulos A, et al. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. J Nanopart Res. 2016 Sep; 18(9):256. https://doi.org/10.1007/s11051-016-3564-1
30. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021 Jan; 26(1):80-93. https://doi.org/10.1016/j.drudis.2020.10.010
31. Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, et al. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIREs Comput Mol Sci [Internet]. 2021; 11(5). Available from: https://onlinelibrary.wiley.com/doi/10.1002/wcms.1516 https://doi.org/10.1002/wcms.1516
32. Sharma SK, Wang X. Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions. IEEE Commun Surv Tutorials. 2020; 22(1):426-71. https://doi.org/10.1109/COMST.2019.2916177
33. Hussain F, Hassan SA, Hussain R, Hossain E. Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges. 2019; Available from: https://arxiv.org/abs/1907.08965
34. Yang H, Alphones A, Xiong Z, Niyato D, Zhao J, Wu K. Artificial-Intelligence-Enabled Intelligent 6G Networks. IEEE Network. 2020 Nov; 34(6):272-80. https://doi.org/10.1109/MNET.011.2000195
35. Murali A, Das NN, Sukumaran SS, Chandrasekaran K, Joseph C, Martin JP. Machine Learning Approaches for Resource Allocation in the Cloud: Critical Reflections. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) [Internet]. Bangalore: IEEE; 2018; 2073-9. Available from: https://ieeexplore.ieee.org/document/8554703/
https://doi.org/10.1109/ICACCI.2018.8554703
36. Toczé K, Nadjm-Tehrani S. A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing. Wireless Communications and Mobile Computing. 2018 Jun 4; 2018:1-23. https://doi.org/10.1155/2018/7476201
37. Nurcahyani I, Lee JW. Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey. Sensors. 2021 Sep 30; 21(19):6542. https://doi.org/10.3390/s21196542
38. Wang J, Jiang C, Zhang H, Ren Y, Chen KC, Hanzo L. Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks. IEEE Commun Surv Tutorials. 2020; 22(3):1472-514. https://doi.org/10.1109/COMST.2020.2965856
39. Musella S, Verna G, Fasano A, Di Micco S. New Perspectives on Machine Learning in Drug Discovery. CMC. 2021 Oct 15; 28(32):6704-28. https://doi.org/10.2174/0929867327666201111144048
40. Damiati SA. Digital Pharmaceutical Sciences. AAPS PharmSciTech. 2020 Aug; 21(6):206. https://doi.org/10.1208/s12249-020-01747-4
41. Elbadawi M, Muñiz Castro B, Gavins FKH, Ong JJ, Gaisford S, Pérez G, et al. M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines. International Journal of Pharmaceutics. 2020 Nov; 590:119837. https://doi.org/10.1016/j.ijpharm.2020.119837
42. Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, et al. Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions. Med Res Rev. 2021 May; 41(3):1427- 73. https://doi.org/10.1002/med.21764
43. Elbadawi M, McCoubrey LE, Gavins FKH, Ong JJ, Goyanes A, Gaisford S, et al. Disrupting 3D printing of medicines with machine learning. Trends in Pharmacological Sciences. 2021 Sep; 42(9):745-57. https://doi.org/10.1016/j.tips.2021.06.002
44. Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int. 2021 Dec; 21(1):270. https://doi.org/10.1186/s12935-021-01981-1
45. McCoubrey LE, Elbadawi M, Orlu M, Gaisford S, Basit AW. Harnessing machine learning for development of microbiome therapeutics. Gut Microbes. 2021 Jan 1; 13(1):1872323. https://doi.org/10.1080/19490976.2021.1872323
46. Sethuraman N. Artificial Intelligence: A New Paradigm for Pharmaceutical Applications in Formulations Development. IJPER. 2020 Dec 22; 54(4):843-6. https://doi.org/10.5530/ijper.54.4.176
47. Andrade CH, Pasqualoto KFM, Ferreira EI, Hopfinger AJ. 4D-QSAR: Perspectives in Drug Design. Molecules. 2010 May 4; 15(5):3281-94. https://doi.org/10.3390/molecules15053281
48. Bak A. Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? IJMS. 2021 May 14; 22(10):5212. https://doi.org/10.3390/ijms22105212
49. Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery. 2021 Sep 2; 16(9):949-59. https://doi.org/10.1080/17460441.2021.1909567
50. Callaway E. AlphaFold's new rival? Meta AI predicts shape of 600 million proteins. Nature. 2022 Nov 10; 611(7935):211-2. https://doi.org/10.1038/d41586-022-03539-1
51. Liu Q, Huang R, Hsieh J, Zhu H, Tiwari M, Liu G, et al. Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021. Clin Pharma and Therapeutics. 2022 Jun 16; cpt.2668. https://doi.org/10.1002/cpt.2668
52. Smith JA, Abhari RE, Hussain Z, Heneghan C, Collins GS, Carr AJ. Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study. BMJ Open. 2020 Oct; 10(10):e039969. https://doi.org/10.1136/bmjopen-2020-039969
53. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. The Lancet Digital Health. 2021 Mar; 3(3):e195-203. https://doi.org/10.1016/S2589-7500(20)30292-2
54. Ball R, Dal Pan G. "Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time? Drug Saf. 2022 May; 45(5):429-38. https://doi.org/10.1007/s40264-022-01157-4
55. Chen Z, Liu X, Hogan W, Shenkman E, Bian J. Applications of artificial intelligence in drug development using real-world data. Drug Discovery Today. 2021 May; 26(5):1256- 64. https://doi.org/10.1016/j.drudis.2020.12.013
Statistics
253 Views | 8 Downloads
How to Cite
1.
Parvathaneni M, Awol AK, Kumari M, Lan K, Lingam M. Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development. JDDT [Internet]. 15Jan.2023 [cited 27Jan.2023];13(1):151-8. Available from: https://jddtonline.info/index.php/jddt/article/view/5867