The Impact of Artificial Intelligence on Drug Discovery; A Comprehensive Review
DOI:
https://doi.org/10.22270/ajprd.v11i3.1230Abstract
Artificial Intelligence (AI) is a technology that utilizes knowledge and learning to solve complicated problems. Recent advancements in computational power and AI technology have significantly increased its potential to revolutionize the drug development process. The pharmaceutical industry is among the primary beneficiaries of AI's recent applications in various sectors of society. In this review, we will discuss the primary causes of attrition rates in new drug approvals, explore how AI can improve the efficiency of the drug development process, and examine the collaboration between pharmaceutical industry leaders and AI-powered drug discovery firms.
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References
Bishop, C.M. (2013) Model-based machine learning. Philos. Trans. A Math. Phys. Eng. Sci. 371
Lee, J.-G. et al. Deep learning in medical imaging: general overview. Korean J. Radiol. 2017;18:570–584
Gun9car, G. et al. An application of machine learning to haematological diagnosis. Sci. Rep. 2018; 8:411
Koohy, H. 2018 The rise and fall of machine learning methods in biomedical research. F1000 Res. 6
Young, J.D. et al. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC Bioinf. 2017; 18: 381
Chen, H. et al. The rise of deep learning in drug discovery. Drug Discov. Today 2018; 23, 1241–1250
Grys, B.T. et al. (2017) Machine learning and computer vision approaches for phenotypic profiling. J. Cell Biol. 2017; 16:65–71
Mak, K.-K. and Pichika, M.R. (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today 24, 773–780
Vyas, M. et al. (2018) Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J. Pharm. 12, 72–76
Sellwood, M.A. et al. (2018) Artificial intelligence in drug discovery. Fut. Sci. 10, 2025–2028
Chan, H.S. et al. (2019) Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 2019; 40 (8):592–604
Brown, N. (2015) Silico Medicinal Chemistry: Computational Methods to Support Drug Design. Royal Society of Chemistry
Pereira, J.C. et al. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016; 56:2495–2506
Firth, N.C. et al.MOARF, an integrated workflow for multiobjective optimization: implementation, synthesis, and biological evaluation. J. Chem. Inf. Model. 2015; 55:1169–1180
Wess, G.; Urmann, M.; Sickenberger, B. Medicinal Chemistry: Challenges and Opportunities. Angew. Chem. Int. Ed. 2001; 40, 3341–3350. [CrossRef]
Pu, L.; Naderi, M.; Liu, T.; Wu, H.C.; Mukhopadhyay, S.; Brylinski, M. EToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol. Toxicol. 2019; 20:2. [CrossRef]
Chen, R.; Liu, X.; Jin, S.; Lin, J.; Liu, J. Machine learning for drug-target interaction prediction. Molecules 2018; 23:2208. [CrossRef] [PubMed]
Hansen, K.; Biegler, F.; Ramakrishnan, R.; Pronobis, W.; Von Lilienfeld, O.A.; Müller, K.R.; Tkatchenko, A. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 2015; 6:2326–2331. [CrossRef]
Gawehn, E.; Hiss, J.A.; Schneider, G. Deep Learning in Drug Discovery. Mol. Inform. 2016; 35:3–14. [CrossRef]
Lysenko, A.; Sharma, A.; Boroevich, K.A.; Tsunoda, T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci. Alliance 2018; 1: e201800098. [CrossRef]
You, J.; McLeod, R.D.; Hu, P. Predicting drug-target interaction network using deep learning model. Comput. Biol. Chem. 2019; 80:90–101. [CrossRef]
Liu, X.; IJzerman, A.P.; vanWesten, G.J.P. Computational Approaches for De Novo Drug Design: Past, Present, and Future. In Methods in Molecular Biology; Humana Press Inc.: Totowa, NJ, USA, 2021; 21 (90):139–165.
Bannigan, P.; Aldeghi, M.; Bao, Z.; Häse, F.; Aspuru-Guzik, A.; Allen, C. Machine learning directed drug formulationdevelopment.Adv. Drug Deliv. Rev. 2021;175:113806. [CrossRef]
Hansen, K.; Biegler, F.; Ramakrishnan, R.; Pronobis, W.; Von Lilienfeld, O.A.; Müller, K.R.; Tkatchenko, A. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 2015; 6:2326–2331. [CrossRef]
Santín, E.P.; Solana, R.R.; García, M.G.; Suárez, M.D.M.G.; Díaz, G.D.B.; Cabal, M.D.C.; Rojas, J.M.M.; Sánchez, J.I.L. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2021, 11, e1516. [CrossRef]
Jang, H.Y.; Song, J.; Kim, J.H.; Lee, H.; Kim, I.W.; Moon, B.; Oh, J.M. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digit. Med. 2022; 5:100. [CrossRef] [PubMed]
Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Sci. 2018; 4:268–276. [CrossRef]
Nussinov, R.; Zhang, M.; Liu, Y.; Jang, H. AlphaFold, Artificial Intelligence (AI), and Allostery. J. Phys. Chem. B 2022;126:6372–6383. [CrossRef] [PubMed]
Bai, Q.; Liu, S.; Tian, Y.; Xu, T.; Banegas-Luna, A.J.; Pérez-Sánchez, H.; Huang, J.; Liu, H.; Yao, X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2022;12:e1581. [CrossRef]
Yuan, Y. et al.Lig Builder 2: a practical de novo drug design approach. J. Chem. Inf. Model. 2011;51:1083–1091
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
Anderson, A.C. (2012) Structure-based functional design of drugs: from target to lead compound. Methods Mol. Biol. 2012; 823:359–366
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, 199–2091 34
Alanine, A. et al. Lead generation-enhancing the success of drug discovery by investing in the hit to lead process. Comb. Chem. High Throughput Screen. 2003; 6:51–66
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
Mamoshina, P. et al. Applications of deep learning in biomedicine. Mol. Pharm. 2016; 13:1445–1454
Seddon, G. et al. Drug design for ever, from hype to hope. J. Comput. Aided Mol. Des. 2012; 26:137–150
Merk, D. et al.De novo design of bioactive small molecules by artificial intelligence. Mol. Inform. 2018; 37:1700153
Klopman, G. et al. ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. J. Chem. Inf. Comput. Sci. 2004; 44:704–715
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
Nascimento, A.C.A. et al. (2016) A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinf. 17, 46
Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 2017; 17:97–113
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
Hamet, P. and Tremblay, J.Artificial intelligence in medicine. Metabolism 2017; 69:S36–S40
Hughes, J.P. et al. Principles of early drug discovery. Br. J. Pharmacol. 2011; 162:1239–1249
Mohs, R.C. and Greig, N.H.Drug discovery and development: role of basic biological research. Alzheimers Dement. 2017; 3:651–657
Katsila, T. et al. (2016) Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J. 14, 177–184
Emig, D. et al.Drug target prediction and repositioning using an integrated network-based approach. PLoS One 2016; 8: e60618
Duch, W. et al. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des. 2007; 13: 1497–1508
Guo, M. et al. A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharm. Technol. 2002; 6:44–52
Mehta, C.H. et al.Computational modeling for formulation design. Drug Discovery Today 2019; 24:781–788
Zhao, C. et al. Toward intelligent decision support for pharmaceutical product development. J. Pharm. Innovation 2006; 1:23–35
Rantanen, J. and Khinast, J.The future of pharmaceutical manufacturing sciences. J. Pharm. Sci. 2015; 104:3612–3638
Ketterhagen, W.R. et al. Process modeling in the pharmaceutical industry using the discrete element method. J. Pharm. Sci. 2009; 98:442–470
Chen, W. et al. Mathematical model-based accelerated development of extended-release metformin hydrochloride tablet formulation. AAPS PharmSciTech 20165; 17:1007–1013
Hassanzadeh, P. et al.The significance of artificial intelligence in drug delivery system design. Adv. Drug Delivery Rev. 2019; 151:169–190
Luo, M. et al.Micro-/nanorobots at work in active drug delivery. Adv. Funct. Mater. 2018; 28:1706100
Fu, J. and Yan, H.Controlled drug release by a nanorobot. Nat. Biotechnol. 2012; 30:407–408
Calzolari, D. et al. (2008) Search algorithms as a framework for the optimization of drug combinations. PLoSComput. Biol. 4, e1000249
Wilson, B. and KM, G.Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment. Future Med. 2020; 15:433–435
Tsigelny, I.F. Artificial intelligence in drug combination therapy. Brief. Bioinform. 2019; 20:1434–1448
Ho, D. et al. Artificial intelligence in nanomedicine. Nanoscale Horiz. 2019; 4:365–377
Sacha, G.M. and Varona, P.Artificial intelligence in nanotechnology. Nanotechnology 2013; 24:452002
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Copyright (c) 2023 Muktai N.Rudrurkar *, Vishweshwar M. Dharashive, Dr. Sameer Shafi, Anis J. Kazi, Mohini A. Gurav, Sneha R. Patil, Punam V. Ritthe
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