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The design of compounds with desirable properties – The anti-HIV case study
Journal of Computational Chemistry, 44 (2022), 10; 1016-1030. https://doi.org/10.1002/jcc.27061
Novak, Jurica; Pathak, Prateek; Grishina, Maria A.; Potemkin, Vladimir A.

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Novak, J., Pathak, P., Grishina, M. A. i Potemkin, V. A. (2022). The design of compounds with desirable properties – The anti-HIV case study. Journal of Computational Chemistry, 44. (10), 1016-1030. doi: 10.1002/jcc.27061

Novak, Jurica, et al. "The design of compounds with desirable properties – The anti-HIV case study." Journal of Computational Chemistry, vol. 44, br. 10, 2022, str. 1016-1030. https://doi.org/10.1002/jcc.27061

Novak, Jurica, Prateek Pathak, Maria A. Grishina i Vladimir A. Potemkin. "The design of compounds with desirable properties – The anti-HIV case study." Journal of Computational Chemistry 44, br. 10 (2022): 1016-1030. https://doi.org/10.1002/jcc.27061

Novak, J., et al. (2022) 'The design of compounds with desirable properties – The anti-HIV case study', Journal of Computational Chemistry, 44(10), str. 1016-1030. doi: 10.1002/jcc.27061

Novak J, Pathak P, Grishina MA, Potemkin VA. The design of compounds with desirable properties – The anti-HIV case study. Journal of Computational Chemistry [Internet]. 19.12.2022. [pristupljeno 29.11.2024.];44(10):1016-1030. doi: 10.1002/jcc.27061

J. Novak, P. Pathak, M. A. Grishina i V. A. Potemkin, "The design of compounds with desirable properties – The anti-HIV case study", Journal of Computational Chemistry, vol. 44, br. 10, str. 1016-1030, Prosinac 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:193:158194. [Citirano: 29.11.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:193:158194
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Podaci o radu
Naslov (engleski)The design of compounds with desirable properties – The anti-HIV case study
AutorJurica Novak
AutorPrateek Pathak
AutorMaria A. Grishina
AutorVladimir A. Potemkin
Autorova ustanovaSveučilište u Rijeci
(Fakultet biotehnologije i razvoja lijekova)
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Kemija
Teorijska kemija
Sažetak (engleski)
Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeZavršna verzija rukopisa prihvaćena za objavljivanje (postprint)
Naslov časopisaJournal of Computational Chemistry
Brojčani podacivol. 44, br. 10, str. 1016-1030
p-ISSN0192-8651
e-ISSN1096-987X
DOIhttps://doi.org/10.1002/jcc.27061
URN:NBNurn:nbn:hr:193:158194
Datum objave elektroničkog izdanja2022-12-19
URL dokumentahttps://onlinelibrary.wiley.com/doi/10.1002/jcc.27061
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Datum isteka embarga: 2024-03-20
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2023-03-17 13:54:45