A conceptual deep learning framework for COVID-19 drug discovery

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43963863" target="_blank" >RIV/49777513:23220/21:43963863 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9666715" target="_blank" >https://ieeexplore.ieee.org/document/9666715</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/UEMCON53757.2021.9666715" target="_blank" >10.1109/UEMCON53757.2021.9666715</a>

Result language
angličtina
Original language name
A conceptual deep learning framework for COVID-19 drug discovery
Original language description
The analytical and experimental methods used for the development of drugs have some disadvantages in the aspect of the needed time for preparation of the desired parenthetical products and the efficiency of them, which not only can the risk for failure increase, particularly when pathogens are impossible to be cultivated under laboratory conditions, but these approaches can also lead to achieving arrays of antigens that are not able to provide sufficient immunity to combat the targeted disease. On the other hand, artificial intelligence (AI) and its new branches, including deep learning (DL) and machine learning (ML) techniques can be deployed for drug development purposes in order to alleviate the difficulties associated with conventional methods. Moreover, intelligent methods will provide researchers with the opportunity to use some userfriendly and efficient services to conquer such problems. In this respect, a conceptual DL framework has been studied in order to demonstrate the capability and applicability of these methods. Accordingly, a framework has been proposed to show how COVID-19 drug development can benefit from the potentials of AI and DL.
Czech name
—
Czech description
—

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)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)
ISBN
978-1-66540-690-1
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
0030-0034
Publisher name
IEEE
Place of publication
Piscaway
Event location
virtual, New York, USA
Event date
Dec 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)