Skip to main content
SpringerLink
Log in

Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions

  • Chapter
  • First Online:
Emerging Technologies During the Era of COVID-19 Pandemic

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 348))

Abstract

The world is in a frustrating situation, which is exacerbating due to the time-consuming process of the COVID-19 vaccine design and production. This chapter provides a comprehensive investigation of fundamentals, state-of-the-art and some perspectives to speed up the process of the design, optimization and production of the medicine for COVID-19 based on Deep Learning (DL) methods. The proposed platforms are able to be used as predictors to forecast antigens during the infection disregarding their abundance and immunogenicity with no requirement of growing the pathogen in vitro. First, we briefly survey the latest achievements and fundamentals of some DL methodologies, including Deep Boltzmann Machines (DBM), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Hopfield network and Long Short-Term Memory(LSTM). These techniques help us to reach an integrated approach for drug development by non-conventional antigens. We then propose several DL-based platforms to utilize for future applications regarding the latest publications and medical reports. Considering the evolving date on COVID-19 and its ever-changing nature, we believe this survey can give readers some useful ideas and directions to understand the application of Artificial Intelligence (AI) to accelerate the vaccine design not only for COVID-19 but also for many different diseases or viruses.

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. Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y.: Digital technology and COVID-19. Nat. Med. 26(4), 459–461 (2020)

    Article  Google Scholar 

  2. Ton, AT, Gentile, F., Hsing, M., Ban, F., Cherkasov, A.: Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds. Mol. Inform. (2020)

    Google Scholar 

  3. Jamshidi, M.B., et al.: Artificial Intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Acc. 8, 109581–109595 (2020)

    Google Scholar 

  4. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Google Scholar 

  5. Arpaci, I., Al-Emran, M., Al-Sharafi, M.A., Shaalan, K.: A novel approach for predicting the adoption of smartwatches using machine learning algorithms. In: Recent Advances in Intelligent Systems and Smart Applications, pp. 185–195. Springer (2020)

    Google Scholar 

  6. Arpaci, I.: A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Comput. Hum. Behav. 90, 181–187 (2019)

    Article  Google Scholar 

  7. Robson, B., McBurney, R.: The role of information, bioinformatics and genomics. In: Drug Discovery and Development, pp. 77–94. Elsevier (2013)

    Google Scholar 

  8. Li, J., Robson, B.J.D.: Bioinformatics and computational chemistry in molecular design: recent advances and their applications, vol. 101, pp. 285–307 (2000)

    Google Scholar 

  9. Arodola, O.A., Soliman, M.E.: Quantum mechanics implementation in drug-design workflows: does it really help? Drug Des. Dev. Ther. 11, 2551 (2017)

    Article  Google Scholar 

  10. Pohl, R., Gilman, R., Miller, G.A., Pachucki, K.: Muonic hydrogen and the proton radius puzzle. Annu. Rev. Nucl. Part. Sci. 63, 175–204 (2013)

    Article  Google Scholar 

  11. Zhu, H.: Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 60, 573–589 (2020)

    Article  Google Scholar 

  12. Zhu, H., et al.: t4 report: supporting read-across using biological data. Altex 33(2), 167 (2016)

    Article  Google Scholar 

  13. Rifaioglu, A.S., Atas, H., Martin, M.J., Cetin-Atalay, R., Atalay, V., Doğan, T.: Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief. Bioinform. 20(5), 1878–1912 (2019)

    Article  Google Scholar 

  14. Yin, D., et al.: A novel multi-epitope recombined protein for diagnosis of human brucellosis. BMC Infect. Dis. 16(1), 219 (2016)

    Article  Google Scholar 

  15. Díaz, Ó., Dalton, J.A., Giraldo, J.: Artificial intelligence: a novel approach for drug discovery. Trends Pharmacol. Sci. 40(8), 550–551 (2019)

    Article  Google Scholar 

  16. Zhavoronkov, A.: Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. ACS Publications (2018)

    Google Scholar 

  17. Chen, B., et al.: Predicting HLA class II antigen presentation through integrated deep learning. Nat. Biotechnol. 37(11), 1332–1343 (2019)

    Article  Google Scholar 

  18. Kim, Y., et al.: A computational framework for deep learning-based epitope prediction by using structure and sequence information. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1208–1210. IEEE (2019)

    Google Scholar 

  19. Zhu, R.-F., Gao, R.-L., Robert, S.-H., Gao, J.-P., Yang, S.-G., Zhu, C.: Systematic review of the registered clinical trials of coronavirus diseases 2019 (COVID-19). medRxiv (2020)

    Google Scholar 

  20. Jamshidi, M.B., Jamshidi, M., Rostami, S.: An intelligent approach for nonlinear system identification of a li-ion battery. In: 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 98–103. IEEE (2017)

    Google Scholar 

  21. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T.: The rise of deep learning in drug discovery. Drug Discov. Today 23(6), 1241–1250 (2018)

    Article  Google Scholar 

  22. Ghasemi, F., Mehridehnavi, A., Perez-Garrido, A., Perez-Sanchez, H.: Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discov. Today 23(10), 1784–1790 (2018)

    Article  Google Scholar 

  23. Sunseri, J., King, J.E., Francoeur, P.G., Koes, D.R.: Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput.-Aided Mol. Des. 33(1), 19–34 (2019)

    Google Scholar 

  24. Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3–14 (2016)

    Article  Google Scholar 

  25. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  26. Jing, Y., Bian, Y., Hu, Z., Wang, L., Xie, X.-Q.S.: Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J 20(3), 58 (2018)

    Article  Google Scholar 

  27. Sellwood, M.A., Ahmed, M., Segler, M.H., Brown, N.: Artificial intelligence in drug discovery. Future Science (2018)

    Google Scholar 

  28. Fleming, N.: How artificial intelligence is changing drug discovery. Nature 557(7706), S55–S55 (2018)

    Article  Google Scholar 

  29. Larranaga, P., et al.: Machine learning in bioinformatics. Brief. Bioinform. 7(1), 86–112 (2006)

    Article  MathSciNet  Google Scholar 

  30. Cherkasov, A., et al.: QSAR modeling: where have you been? where are you going to? J. Med. Chem. 57(12), 4977–5010 (2014)

    Article  Google Scholar 

  31. Smalley, E.: AI-powered drug discovery captures pharma interest. Nat. Biotechnol. 35, 604–605 (2017)

    Article  Google Scholar 

  32. Ye, D.: Artificial intelligence and deep learning application in evaluating the descendants of Tubo Mgar Stong Btsan and social development. In: Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), pp. 1869–1876. Springer (2020)

    Google Scholar 

  33. Xu, B., et al.: Epidemiological data from the COVID-19 outbreak, real-time case information. Sci. Data 7(1), 1–6 (2020)

    Article  Google Scholar 

  34. Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet. Infect. Dis. 20(5), 533–534 (2020)

    Article  Google Scholar 

  35. Wang, S., He, M., Gao, Z., He, S., Ji, Q.: Emotion recognition from thermal infrared images using deep Boltzmann machine. Front. Comput. Sci. 8(4), 609–618 (2014)

    Article  MathSciNet  Google Scholar 

  36. Salakhutdinov, R., Hinton, G.: Deep Boltzmann machines. In: Artificial Intelligence and Statistics, pp. 448–455 (2009)

    Google Scholar 

  37. Taherkhani, A., Cosma, G., McGinnity, T.M.: Deep-FS: a feature selection algorithm for deep Boltzmann machines. Neurocomputing 322, 22–37 (2018)

    Article  Google Scholar 

  38. Alberici, D., Barra, A., Contucci, P., Mingione, E.: Annealing and replica-symmetry in deep Boltzmann machines. J. Stat. Phys., 1–13 (2020)

    Google Scholar 

  39. Hess, M., Lenz, S., Blätte, T.J., Bullinger, L., Binder, H.: Partitioned learning of deep Boltzmann machines for SNP data. Bioinformatics 33(20), 3173–3180 (2017)

    Article  Google Scholar 

  40. Zhang, X.-L., Wu, J.: Deep belief networks based voice activity detection. IEEE Trans. Audio Speech Lang. Process. 21(4), 697–710 (2012)

    Article  Google Scholar 

  41. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  42. Maetschke, S.R., Ragan, M.A.J.B.: Characterizing cancer subtypes as attractors of Hopfield networks 30(9), 1273–1279 (2014)

    Google Scholar 

  43. Conforte, A.J., Alves, L., Coelho, F.C., Carels, N., Silva, F.A.B.D.: Modeling basins of attraction for breast cancer using Hopfield networks. Front. Genet. 11, 314 (2020)

    Google Scholar 

  44. Al-Maitah, M.: Analyzing genetic diseases using multimedia processing techniques associative decision tree-based learning and Hopfield dynamic neural networks from medical images. Neural Comput. Appl. 32(3), 791–803 (2020)

    Article  Google Scholar 

  45. Stephenson, N., et al.: Survey of machine learning techniques in drug discovery. Curr. Drug Metab. 20(3), 185–193 (2019)

    Google Scholar 

  46. Sangari, A., Sethares, W.: Paper texture classification via multi-scale restricted Boltzman machines. In: 2014 48th Asilomar Conference on Signals, Systems and Computers, pp. 482–486. IEEE (2014)

    Google Scholar 

  47. Arpaci, I., et al.: Analysis of twitter data using evolutionary clustering during the COVID-19 Pandemic. CMC—Comput. Mater. Continua. 65(1), 193–203 (2020)

    Article  Google Scholar 

  48. Kim, H.-C., Jang, H., Lee, J.-H.: Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network. J. Neurosci. Meth. 330, 108451 (2020)

    Article  Google Scholar 

  49. Vogelstein, J.T., et al.: A community-developed open-source computational ecosystem for big neuro data. Nat. Meth. 15(11), 846–847 (2018)

    Article  Google Scholar 

  50. Beam, A.L., Kohane, I.S.: Big data and machine learning in health care. JAMA 319(13), 1317–1318 (2018)

    Article  Google Scholar 

  51. Arpaci, I., Karataş, K., Baloğlu, M.J.P., Differences, I.: The development and initial tests for the psychometric properties of the COVID-19 phobia scale (C19P-S), p. 110108 (2020)

    Google Scholar 

  52. Cai, C., et al.: Transfer learning for drug discovery. J. Med. Chem. 63(16), 8683–8694 (2020)

    Google Scholar 

  53. Baskin, I.I.: The power of deep learning to ligand-based novel drug discovery. Expert Opin. Drug Discov.,1–10 (2020)

    Google Scholar 

  54. Yildirim, O., Gottwald, M., Schüler, P., Michel, M.C.: Opportunities and challenges for drug development: public–private partnerships, adaptive designs and big data. Front. Pharmacol. 7, 461 (2016)

    Article  Google Scholar 

  55. Walls, A.C., Park, Y.-J., Tortorici, M.A., Wall, A., McGuire, A.T., Veesler, D.: Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell (2020)

    Google Scholar 

  56. Shoichet, B.K.: Virtual screening of chemical libraries. Nature 432(7019), 862–865 (2004)

    Article  Google Scholar 

  57. Zhang, L., Tan, J., Han, D., Zhu, H.: From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22(11), 1680–1685 (2017)

    Article  Google Scholar 

  58. Wang, J., Cao, H., Zhang, J.Z., Qi, Y.: Computational protein design with deep learning neural networks. Sci. Rep. 8(1), 1–9 (2018)

    Google Scholar 

  59. Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. Department of Computer Science, Colorado University at Boulder (1986)

    Google Scholar 

  60. Kim, H.-C., Jang, H., Lee, J.-H.: Test–retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network. J. Neurosci. Meth. 330, 108451 (2020)

    Google Scholar 

  61. Karim, M.R., Cochez, M., Jares, J.B., Uddin, M., Beyan, O., Decker, S.: Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network. In: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 113–123 (2019)

    Google Scholar 

  62. Conover, M., Staples, M., Si, D., Sun, M., Cao, R.: AngularQA: protein model quality assessment with LSTM networks. Comput. Math. Biophys. 7(1), 1–9 (2019)

    Article  Google Scholar 

  63. Ibrahim, I.M., Abdelmalek, D.H., Elshahat, M.E., Elfiky, A.A.: COVID-19 spike-host cell receptor GRP78 binding site prediction. J. Infect. (2020)

    Google Scholar 

  64. Kumarakulasinghe, N.B., et al.: EGFR kinase inhibitors and gastric acid suppressants in EGFR-mutant NSCLC: a retrospective database analysis of potential drug interaction. Oncotarget 7(51), 85542 (2016)

    Article  Google Scholar 

  65. Carreira-Perpignan, M.: HGE on contrastive divergence learning. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, vol. 10 (2005)

    Google Scholar 

  66. Grohskopf, L.A., Sokolow, L.Z., Broder, K.R., Walter, E.B., Fry, A.M., Jernigan, D.B.: Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices—United States, 2018–19 influenza season. MMWR Recommendations and Rep. 67(3), 1 (2018)

    Article  Google Scholar 

  67. Graham, R.L., Donaldson, E.F., Baric, R.S.: A decade after SARS: strategies for controlling emerging coronaviruses. Nat. Rev. Microbiol. 11(12), 836–848 (2013)

    Article  Google Scholar 

  68. Amanat, F., Krammer, F.: SARS-CoV-2 vaccines: status report. Immunity (2020)

    Google Scholar 

  69. Ahmed, S.F., Quadeer, A.A., McKay, M.R.: Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses 12(3), 254 (2020)

    Article  Google Scholar 

  70. Hu, F., Jiang, J., Yin, P.: Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model (2020). arXiv preprint: arXiv:2003.00728

  71. Du, L., He, Y., Zhou, Y., Liu, S., Zheng, B.-J., Jiang, S.: The spike protein of SARS-CoV—a target for vaccine and therapeutic development. Nat. Rev. Microbiol. 7(3), 226–236 (2009)

    Article  Google Scholar 

  72. Roper, R.L., Rehm, K.E.: SARS vaccines: where are we? Expert Rev. Vaccines 8(7), 887–898 (2009)

    Article  Google Scholar 

  73. Shen, C., et al.: Treatment of 5 critically ill patients with COVID-19 with convalescent plasma. JAMA 323(16), 1582–1589 (2020)

    Article  Google Scholar 

  74. Hughes, J.P., Rees, S., Kalindjian, S.B., Philpott, K.L.: Principles of early drug discovery. Br. J. Pharmacol. 162(6), 1239–1249 (2011)

    Article  Google Scholar 

  75. Chung, M., et al.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202–207 (2020)

    Article  Google Scholar 

  76. Yang, Y., Faraggi, E., Zhao, H., Zhou, Y.: Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 27(15), 2076–2082 (2011)

    Article  Google Scholar 

  77. Rappuoli, R.: Reverse vaccinology. Curr. Opin. Microbiol. 3(5), 445–450 (2000)

    Article  Google Scholar 

  78. Hassan, A., Mahmood, A.J.I.A.: Convolutional recurrent deep learning model for sentence classification. IEEE Acc. 6, 13949–13957 (2018)

    Google Scholar 

  79. Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E., Svetnik, V.: Deep neural nets as a method for quantitative structure–activity relationships. J. Chem. Inf. Model. 55(2), 263–274 (2015)

    Article  Google Scholar 

  80. Unterthiner, T., Mayr, A., Klambauer, G., Hochreiter, S.: Toxicity prediction using deep learning (2015). arXiv preprint: arXiv:1503.01445

  81. Weinstein, M.C., Freedberg, K.A., Hyle, E.P., Paltiel, A.D.: Waiting for certainty on Covid-19 antibody tests—at what cost?. N. Engl. J. Med. (2020)

    Google Scholar 

Download references

Acknowledgements

This research has been supported by the Ministry of Education, Youth and Sports of the Czech Republic under the project OP VVV Electrical Engineering Technologies with High-Level of Embedded Intelligence CZ.02.1.01/0.0/0.0/18_069/0009855.

Author information

Authors and Affiliations

  1. Research and Innovation Centre for Electrical Engineering (RICE), University of West Bohemia, 301 00, Pilsen, Czech Republic

    Mohammad Behdad Jamshidi, Jakub Talla, Zdeněk Peroutka & Hamed Hashemi-Dezaki

  2. Department of Power Electronics And Machines (KEV), University of West Bohemia, 301 00, Pilsen, Czech Republic

    Mohammad Behdad Jamshidi, Jakub Talla & Zdeněk Peroutka

  3. School of Engineering, Macquarie University, Sydney, NSW, 2109, Australia

    Ali Lalbakhsh

  4. Department of Electrical Engineering, Islamic Azad University, Kermanshah Branch, 1477893855, Kermanshah, Iran

    Sobhan Roshani & Saeed Roshani

  5. Department of Computer Science and Engineering (KIV), University of West Bohemia, 301 00, Pilsen, Czech Republic

    Vaclav Matousek

  6. Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, 53706, USA

    Mirhamed Mirmozafari

  7. Medical Sciences Research Center, Faculty of Medicine, Tehran Medical Sciences Branch, IAU, 1477893855, Tehran, Iran

    Zahra Malek

  8. School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, EH11 4DY, UK

    Luigi La Spada

  9. Department of Pharmacy, Irma Lerma Rangel College of Pharmacy, Texas A&M University, Kingsville, TX, 78363, USA

    Asal Sabet

  10. Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, 70803, USA

    Mojgan Dehghani

  11. Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Kermanshah, 1477893855, Iran

    Morteza Jamshidi

  12. Electrical and Mechanical Engineering Department, University of Alberta, Edmonton, AB, T6G1H9, Canada

    Mohammad Mahdi Honari

  13. Department of Radiation Oncology, Cancer Institute, Tehran University of Medical Sciences, Tehran, 1416753955, Iran

    Farimah Hadjilooei

  14. Dentistry School, Babol University of Medical Sciences, Babol, 4717647745, Iran

    Alireza Jamshidi

  15. Department of English Language and Literature, Razi University, Kermanshah, 6714414971, Iran

    Pedram Lalbakhsh

  16. Department of Technologies and Measurement (KET), University of West Bohemia, 301 00, Pilsen, Czech Republic

    Sahar Ahmadi

  17. Department of Theoretical Electrical Engineering (KTE), University of West Bohemia, 301 00, Pilsen, Czech Republic

    Saeedeh Lotfi

Authors
  1. Mohammad Behdad Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Lalbakhsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jakub Talla
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zdeněk Peroutka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sobhan Roshani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vaclav Matousek
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Saeed Roshani
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mirhamed Mirmozafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zahra Malek
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Luigi La Spada
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Asal Sabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mojgan Dehghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Morteza Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mohammad Mahdi Honari
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Farimah Hadjilooei
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Alireza Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Pedram Lalbakhsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hamed Hashemi-Dezaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Sahar Ahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Saeedeh Lotfi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Behdad Jamshidi .

Editor information

Editors and Affiliations

  1. Tokat Gaziosmanpasa University, Tokat, Turkey

    Ibrahim Arpaci

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Universiti Malaysia Kelantan, Kelantan, Malaysia

    Mohammed A. Al-Sharafi

  4. Polytechnic of Coimbra, Rua General Santos Costa, Portugal

    Gonçalo Marques

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

Jamshidi, M.B. et al. (2021). Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions. In: Arpaci, I., Al-Emran, M., A. Al-Sharafi, M., Marques, G. (eds) Emerging Technologies During the Era of COVID-19 Pandemic. Studies in Systems, Decision and Control, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67716-9_2

Download citation

Publish with us

Policies and ethics

Search

Navigation