Deep learning techniques and COVID-19 drug discovery: fundamentals, state-of-the-art and future directions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43961959" target="_blank" >RIV/49777513:23220/21:43961959 - isvavai.cz</a>
Result on the web
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980167/" target="_blank" >https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980167/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Deep learning techniques and COVID-19 drug discovery: fundamentals, state-of-the-art and future directions
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Electrical Engineering Technologies with High-Level of Embedded Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Emerging Technologies During the Era of COVID-19 Pandemic
ISBN
978-3-030-67715-2
Number of pages of the result
23
Pages from-to
9-31
Number of pages of the book
385
Publisher name
Springer
Place of publication
Heidelberg
UT code for WoS chapter
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