Skip to main content

Research Repository

Advanced Search

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

Jamshidi, Mohammad Behdad; Lalbakhsh, Ali; Talla, Jakub; Peroutka, Zdeněk; Roshani, Sobhan; Matousek, Vaclav; Roshani, Saeed; Mirmozafari, Mirhamed; Malek, Zahra; La Spada, Luigi; Sabet, Asal; Dehghani, Mojgan; Jamshidi, Morteza; Honari, Mohammad Mahdi; Hadjilooei, Farimah; Jamshidi, Alireza; Lalbakhsh, Pedram; Hashemi-Dezaki, Hamed; Ahmadi, Sahar; Lotfi, Saeedeh


Mohammad Behdad Jamshidi

Ali Lalbakhsh

Jakub Talla

Zdeněk Peroutka

Sobhan Roshani

Vaclav Matousek

Saeed Roshani

Mirhamed Mirmozafari

Zahra Malek

Asal Sabet

Mojgan Dehghani

Morteza Jamshidi

Mohammad Mahdi Honari

Farimah Hadjilooei

Alireza Jamshidi

Pedram Lalbakhsh

Hamed Hashemi-Dezaki

Sahar Ahmadi

Saeedeh Lotfi


Ibrahim Arpaci

Mostafa Al-Emran

Mohammed A. Al-Sharafi

Gonçalo Marques


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.


Jamshidi, M. B., Lalbakhsh, A., Talla, J., Peroutka, Z., Roshani, S., Matousek, V., Roshani, S., Mirmozafari, M., Malek, Z., La Spada, L., Sabet, A., Dehghani, M., Jamshidi, M., Honari, M. M., Hadjilooei, F., Jamshidi, A., Lalbakhsh, P., Hashemi-Dezaki, H., Ahmadi, S., & Lotfi, S. (2021). Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions. In I. Arpaci, M. Al-Emran, M. A. Al-Sharafi, & G. Marques (Eds.), Emerging Technologies During the Era of COVID-19 Pandemic (9-31). Springer.

Online Publication Date Mar 21, 2021
Publication Date 2021
Deposit Date Nov 30, 2022
Publisher Springer
Pages 9-31
Series Title Studies in Systems, Decision and Control
Series Number 348
Series ISSN 2198-4190
Book Title Emerging Technologies During the Era of COVID-19 Pandemic
ISBN 978-3-030-67715-2
Keywords Artificial intelligence, Artificial neural network, Bioinformatics, COVID-19, Deep learning, Drug discovery
Public URL

You might also like

Downloadable Citations