A conceptual deep learning framework for COVID-19 drug discovery

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A conceptual deep learning framework for COVID-19 drug discovery

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A conceptual deep learning framework for COVID-19 drug discovery

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Elektrotechnické technologie s vysokým podílem vestavěné inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (IEEE UEMCON)

ISBN

978-1-66540-690-1

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

0030-0034

Název nakladatele

IEEE

Místo vydání

Piscaway

Místo konání akce

virtual, New York, USA

Datum konání akce

1. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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