Skip to main content
SpringerLink
Log in

Artificial Intelligence for Drug Development

  • Chapter
  • First Online:
Advances in Artificial Intelligence, Computation, and Data Science

Part of the book series: Computational Biology ((COBO,volume 31))

  • 1095 Accesses

Abstract

Drugs are treated as life-saving medicines against life-threatening diseases. However, drug developments pass through very complex and closely monitored phases to ensure the safety and efficacy of the intended purpose. The efforts are to keep highly toxic drugs from reaching even clinical trials. Even after the approval for drug distribution in the market, the drug’s post-marketing safety is analyzed by the number of reported Adverse Events (AEs). It requires the analysis and interpretation of massive data in all three stages namely pre-clinical, clinical and post-marketing stages. In this article, we explore the use of Artificial Intelligence (AI) in interpreting the huge data that is generated in the pre-clinical and clinical trials for safety purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adams CP, Brantner VV (2010) Spending on new drug development 1. Health Econ 19(2):130–141

    Google Scholar 

  2. Xia HA, Jiang Q (2014) Statistical evaluation of drug safety data. Ther Innov Regul Sci 48(1):109–120

    Google Scholar 

  3. Mak K-K, Pichika MR (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today 24(3):773–780

    Google Scholar 

  4. Lamberti MJ, Wilkinson M, Donzanti BA, Wohlhieter GE, Parikh S, Wilkins RG, Getz K (2019) A study on the application and use of artificial intelligence to support drug development. Clin Ther 41(8):1414–1426

    Google Scholar 

  5. Abdul Rashid MBM (2020) Artificial intelligence effecting a paradigm shift in drug development. SLAS Technol: Transl Life Sci Innov 20:3–15

    Google Scholar 

  6. Murphy RF (2011) An active role for machine learning in drug development. Nature Chem Biol 7(6):327–330

    Google Scholar 

  7. Wale N (2011) Machine learning in drug discovery and development. Drug Dev Res 72(1):112–119

    Article  CAS  Google Scholar 

  8. Meng H-Y, Jin W-L, Yan C-K, Yang H (2019) The application of machine learning techniques in clinical drug therapy. Curr Comput Aided Drug Des 15(2):111–119

    Article  CAS  PubMed  Google Scholar 

  9. Gunther EC, Stone DJ, Gerwien RW, Bento P, Heyes MP (2003) Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proc Natl Acad Sci 100(16):9608–9613

    Google Scholar 

  10. Li H, Zhang W, Chen Y, Guo Y, Li G-Z, Zhu X (2017) A novel multi-target regression framework for time-series prediction of drug efficacy. Sci Rep 7(1):1–9

    Google Scholar 

  11. Silva Á, Cortez P, Santos MF, Gomes L, Neves J(2006) Mortality assessment in intensive care units via adverse events using artificial neural networks. Artif Intell Med 36(3):223–234

    Google Scholar 

  12. Hohne BA, Pierce TH (1989) Expert system applications in power systems. Am Chem Soc 408

    Google Scholar 

  13. Hanessian S, Franco J, Gagnon G, Laramee D, Larouche B (1990) Computer-assisted analysis and perception of stereochemical features in organic molecules using the chiron program. J Chem Inf Comput Sci 30(4):413–425

    Article  CAS  Google Scholar 

  14. Hanessian S, Botta M, Larouche B, Boyaroglu A (1992) Computer-assisted perception of similarity using the chiron program: a powerful tool for the analysis and prediction of biogenetic patterns. J Chem Inf Comput Sci 32(6):718–722

    Article  CAS  Google Scholar 

  15. Hanessian S (1993) Reflections on the total synthesis of natural products: art, craft, logic, and the chiron approach. Pure Appl Chem 65(6):1189–1204

    Article  CAS  Google Scholar 

  16. Wipke WT, Rogers D (1984) Artificial intelligence in organic synthesis. SST: starting material selection strategies. An application of superstructure search. J Chem Inf Comput Sci 24(2):71–81

    Google Scholar 

  17. Azario P, Barone R, Chanon M (1988) Microcomputer and organic synthesis. 3. The MARSEIL/SOS expert system, a new graphic approach. An electronic lab note for organic synthesis. J Org Chem 53(4):720–724

    Google Scholar 

  18. Mehta G, Barone R, Azario P, Barberis F, Arbelot M, Chanon M (1992) New computer-based approach for seeking a key step in the synthesis of complex structures. Application to taxane and crinipellin diterpenoid frameworks. Tetrahedron 48(41):8953–8962

    Google Scholar 

  19. Feller D (1996) The role of databases in support of computational chemistry calculations. J Comput Chem 17(13):1571–1586

    Article  CAS  Google Scholar 

  20. Huang Q, Li L-L, Yang S-Y (2011) RASA: a rapid retrosynthesis-based scoring method for the assessment of synthetic accessibility of drug-like molecules J Chem Inf Model 51(10):2768–2777

    Google Scholar 

  21. Salatin TD, Jorgensen WL (1980) Computer-assisted mechanistic evaluation of organic reactions. 1. Overview. J Org Chem 45(11):2043–2051

    Google Scholar 

  22. Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38(16):1291–1307

    Google Scholar 

  23. Korotcov A, Tkachenko V, Russo DP, Ekins S (2017) Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 14(12):4462–4475

    Google Scholar 

  24. Hansch C, Fujita T (1995) Classical and three-dimensional QSAR in agrochemistry. ACS Publications, Washington, DC

    Google Scholar 

  25. Free SM, Wilson JW (1964) A mathematical contribution to structure-activity studies. J Med Chem 7(4):395–399

    Google Scholar 

  26. Luco JM, Ferretti FH (1997) QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. J Chem Inf Comput Sci 37(2):392–401

    Google Scholar 

  27. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58(1):267–288

    Google Scholar 

  28. Ferreira MMC (2016) Multivariate QSAR. In: Encyclopedia of physical organic chemistry. Wiley, Hoboken, pp 1–38

    Google Scholar 

  29. Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10(17):385

    Article  Google Scholar 

  30. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28

    Google Scholar 

  31. Wesley L, Veerapaneni S, Desai R, Mcgee F, Joglekar N, Rao S, Kamal Z (2016) 3d-QSAR and SVM prediction of braf-v600e and HIV integrase inhibitors: a comparative study and characterization of performance with a new expected prediction performance metric. Am J Biochem Biotechnol 12(4):253–262

    Article  CAS  Google Scholar 

  32. Nekoei M, Mohammadhosseini M, Pourbasheer E (2015) QSAR study of vegfr-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach. Med Chem Res 24(7):3037–3046

    Article  CAS  Google Scholar 

  33. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727

    Article  CAS  PubMed  Google Scholar 

  34. Taussig HB (1962) A study of the German outbreak of phocomelia. Obstet Gynecol Surv 17(6):840–844

    Google Scholar 

  35. Lorberbaum T, Nasir M, Keiser MJ, Vilar S, Hripcsak G, Tatonetti NP (2015) Systems pharmacology augments drug safety surveillance. Clin Pharm Therap 97(2):151–158

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Natural Sciences, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia

    Muhammad Waqar Ashraf

Authors
  1. Muhammad Waqar Ashraf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Waqar Ashraf .

Editor information

Editors and Affiliations

  1. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Tuan D. Pham

  2. Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong

    Hong Yan

  3. College of Sciences and Human Studies, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Muhammad W. Ashraf

  4. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

    Folke Sjöberg

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ashraf, M.W. (2021). Artificial Intelligence for Drug Development. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-69951-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69951-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69950-5

  • Online ISBN: 978-3-030-69951-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation