Artificial intelligence is an area of computer science that deals with the ability to solve problems using symbolic programming. Artificial intelligence can help solve health-care issues with large-scale applications. Expert system development is a significant and effective application of artificial intelligence. Artificial intelligence (AI) is a technology-based system that uses a variety of advanced tools and networks to simulate human intelligence. AI makes use of systems and software that can read and learn from data and to make independent judgments in order to achieve certain goals. Its applications in the pharmaceutical area are constantly being expanded, as discussed in this chapter. Recently, healthcare sector is facing some complex challenges, such as the increased cost of drugs and therapies, and society needs specific significant changes in this area. Personalized medications with the necessary dose, release parameters, and other required aspects can be manufactured according to individual patient need with the use of AI in pharmaceutical product manufacturing. Using the latest AI-based technologies will not only reduce the time it takes for products to reach the market, but it will also improve product quality and overall safety of the manufacturing process, as well as provide better resource utilization and cost-effectiveness, emphasize the importance of automation. This chapter emphasizes the importance of artificial intelligence (AI) in the pharmaceutical sector, including drug research and development, medication repurposing, enhancing pharmaceutical productivity, and clinical trials And its current and future applications in drug discovery development.
Cite this article:
Shaikh Habeeba. Use of Artificial Intelligence in Drug Discovery and its Application in Drug Development. Asian Journal of Research in Chemistry. 2023; 16(1):83-0. doi: 10.52711/0974-4150.2023.00014
Shaikh Habeeba. Use of Artificial Intelligence in Drug Discovery and its Application in Drug Development. Asian Journal of Research in Chemistry. 2023; 16(1):83-0. doi: 10.52711/0974-4150.2023.00014 Available on: https://ajrconline.org/AbstractView.aspx?PID=2023-16-1-14
1. Ramesh A. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004; 86:334–338.
2. Miles J., Walker A. The potential application of artificial intelligence in transport. IEE Proc.-Intell. Transport Syst. 2006; 153:183–198
3. Yang Y., Siau K. MWAIS; 2018. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age.
4. Wirtz B.W. Artificial intelligence and the public sector—applications and challenges. Int. J. Public Adm. 2019; 42:596–615.
5. Smith R.G., Farquhar A. The road ahead for knowledge management: an AI perspective. AI Mag. 2000; 21 17–17.
6. Lamberti M.J. A study on the application and use of artificial intelligence to support drug development. Clin. Ther. 2019; 41:1414–1426.
7. Bishop, C.M. (2013) Model-based machine learning. Philos. Trans. A Math. Phys. Eng. Sci. 371
8. Lee, J.-G. et al. (2017) Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570–584
9. Guncar, 9 G. et al. (2018) An application of machine learning to haematological diagnosis. Sci. Rep. 8, 411
10. Koohy, H. (2017) The rise and fall of machine learning methods in biomedical research. F1000 Res. 6 http://dx.doi.org/10.12688/f1000research.13016.2
11. Young, J.D. et al. (2017) Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC Bioinf. 18, 381
12. Chen, H. et al. (2018) The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250
13. Grys, B.T. et al. (2017) Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 216, 65–71
14. Labovitz, D.L. et al. (2017) Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 48, 1416–1419
15. Mak K.-K., Pichika M.R. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019; 24:773–780.
16. Vyas M. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J. Pharm. 2018; 12:72–76.
17. Mak K.-K., Pichika M.R. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019; 24:773–780.
18. Sellwood M.A. Artificial intelligence in drug discovery. Fut. Sci. 2018; 10:2025–2028.
19. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 2020; 60:573–589
20. Ciallella H.L., Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol. 2019; 32:536–547.
21. Chan H.S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 2019; 40(8):592–604
22. Brown N. Royal Society of Chemistry; 2015. Silico Medicinal Chemistry: Computational Methods to Support Drug Design
23. Pereira J.C. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016; 56:2495–2506.
24. Firth N.C. MOARF, an integrated workflow for multiobjective optimization: implementation, synthesis, and biological evaluation. J. Chem. Inf. Model. 2015; 55:1169–1180.
25. Zhang L. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discovery Today. 2017; 22:1680–1685.
26. Jain N. In silico de novo design of novel NNRTIs: a bio-molecular modelling approach. RSC Adv. 2015; 5:14814–14827.
27. Wang Y. A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach. J. Comput.-Aided Mol. Des. 2015; 29:349–360
28. King R.D. Comparison of artificial intelligence methods for modeling pharmaceutical QSARS. Appl. Artif. Intell. 1995; 9:213–233.
29. Álvarez-Machancoses Ó, Fernández-Martínez J.L. Using artificial intelligence methods to speed up drug discovery. Expert Opin. Drug Discovery. 2019; 14:769–777.
30. Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018; 557 S55–S55.
31. Álvarez-Machancoses Ó, Fernández-Martínez J.L. Using artificial intelligence methods to speed up drug discovery. Expert Opin. Drug Discovery. 2019; 14:769–777.
32. Dana D. Deep learning in drug discovery and medicine; scratching the surface. Molecules. 2018; 23:2384.
33. Zang Q. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J. Chem. Inf. Model. 2017; 57:36–49.
34. Yang X. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 2019; 119:10520–10594.
35. Hessler G., Baringhaus K.-H. Artificial intelligence in drug design. Molecules. 2018; 23:2520.
36. Chai S. A grand product design model for crystallization solvent design. Comput. Chem. Eng. 2020; 135:106764.
37. Thafar M. Comparison study of computational prediction tools for drug–target binding affinities. Frontiers Chem. 2019; 7:1–19.
38. Öztürk H. DeepDTA: deep drug–target binding affinity prediction. Bioinformatics. 2018; 34:i821–i829.
39. Lounkine E. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012; 486:361–367
40. Mayr A. DeepTox: toxicity prediction using deep learning. Frontiers Environ. Sci. 2016; 3:80.
41. Zhao YH, Abraham MH, Ibrahim A et al. Predicting penetration across the blood–brain barrier from simple descriptors and fragmentation schemes. J. Chem. Inf. Model. 47(1), 170–175 (2007)
42. Suenderhauf C, Hammann F, Huwyler J. Computational prediction of blood–brain barrier permeability using decision tree induction. Molecules 17(9), 10429–10445 (2012).
43. Guo M. A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharm. Technol. 2002; 6:44–52
44. Mehta C.H. Computational modeling for formulation design. Drug Discovery Today. 2019; 24:781–788.
45. Zhao C. Toward intelligent decision support for pharmaceutical product development. J. Pharm. Innovation. 2006; 1:23–35
46. Rantanen J., Khinast J. The future of pharmaceutical manufacturing sciences. J. Pharm. Sci. 2015; 104:3612–3638.
47. Hay M. Clinical development success rates for investigational drugs. Nat. Biotechnol. 2014; 32:40–51
48. Harrer S. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 2019; 40:577–591.
49. Fogel D.B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 2018; 11:156–164
50. Segler, M.H.S. et al. (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. 4, 120–131
51. Huang, Z. et al. (2017) Data mining for biomedicine and healthcare. J. Healthc. Eng. 2017 http://dx.doi.org/10.1155/2017/7107629 Article ID 7107629, 2 pages
52. Zhang, Y. et al. (2017) Computer-aided clinical trial recruitment based on domainspecific language translation: a case study of retinopathy of prematurity. J. Healthc. Eng. 2017, 7862672
53. Mamoshina, P. et al. (2016) Applications of deep learning in biomedicine. Mol. Pharm. 13, 1445–1454
54. Seddon, G. et al. (2012) Drug design for ever, from hype to hope. J. Comput. Aided Mol. Des. 26, 137–150
55. Merk, D. et al. (2018) De novo design of bioactive small molecules by artificial intelligence. Mol. Inform. 37, 1700153
56. Klopman, G. et al. (2004) ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. J. Chem. Inf. Comput. Sci. 44, 704–715
57. Menden, M.P. et al. (2013) Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS One 8, e61318
58. Nascimento, A.C.A. et al. (2016) A multiple kernel learning algorithm for drugtarget interaction prediction. BMC Bioinf. 17, 46
59. Schneider, G. (2017) Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113
60. Matthews, H. et al. (2016) Omics-informed drug and biomarker discovery: opportunities, challenges and future perspectives. Proteomes 4 http://dx.doi.org/ 10.3390/proteomes4030028
61. Hamet, P. and Tremblay, J. (2017) Artificial intelligence in medicine. Metabolism 69, S36–S40
62. Hughes, J.P. et al. (2011) Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249
63. Mohs, R.C. and Greig, N.H. (2017) Drug discovery and development: role of basic biological research. Alzheimers Dement. 3, 651–657
64. Katsila, T. et al. (2016) Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J. 14, 177–184
65. Wang, Q. et al. (2017) A novel framework for the identification of drug target proteins: combining stacked auto-encoders with a biased support vector machine. PLoS One 12, e0176486
66. Ferrero, E. et al. (2017) In silico prediction of novel therapeutic targets using genedisease association data. J. Transl. Med. 15, 182
67. Bakkar, N. et al. (2018) Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol. 135, 227–247
68. Reymond, J.-L. et al. (2010) Chemical space as a source for new drugs. Med. Chem. Commun. 1, 30–38
69. Okafo, G. et al. (2018) Adapting drug discovery to artificial intelligence. Drug Target Rev. 2018, 50–52
70. Segler, M.H.S. et al. (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610
71. Mayr, A. et al. (2016) DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 3, 80
72. Gayvert, K.M. et al. (2016) A data-driven approach to predicting successes and failures of clinical trials. Cell. Chem. Biol. 23, 1294–1301
73. Rodrigues, T. et al. (2018) Machine intelligence decrypts b-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem. Sci. 9, 6899–6903
74. Luechtefeld, T. et al. (2018) Machine learning of toxicological big data enables readacross structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol. Sci. 165, 198–212
75. Bain, E.E. et al. (2017) Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a Phase 2 clinical trial in subjects with schizophrenia. JMIR Mhealth Uhealth 5, e18
76. Perez-Gracia, J.L. et al. (2017) Strategies to design clinical studies to identify predictive biomarkers in cancer research. Cancer Treat. Rev. 53, 79–97
77. Deliberato, R.O. et al. (2017) Clinical note creation, binning, and artificial intelligence. JMIR Med. Inf. 5, e24
78. Corsello, S.M. et al. (2017) The Drug Repurposing Hub: a next-generation drug library and information resource. Nat. Med. 23, 405–408
79. 59 Hernandez, J.J. et al. (2017) Giving drugs a second chance: overcoming regulatory and financial hurdles in repurposing approved drugs as cancer therapeutics. Front. Oncol. 7, 273
80. Lozano-Diez, A. et al. (2017) An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition. PLoS One 12, e0182580
81. Aliper, A. et al. (2016) Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13, 2524–2530
82. Galbusera, F. et al. (2018) Exploring the potential of generative adversarial networks for synthesizing radiological images of the spine to be used in in silico trials. Front. Bioeng. Biotechnol. 6, 53