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.


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Jamshidi, M., 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., 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', Emerging Technologies During the Era of COVID-19 Pandemic, pp. 9-31. https://doi.org/10.1007/978-3-030-67716-9_2

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Last updated: 06 December 2022
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