prikaz prve stranice dokumenta The design of compounds with desirable properties – The anti-HIV case study
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Scientific paper - Original scientific paper
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. & 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, no. 10, 2022, pp. 1016-1030. https://doi.org/10.1002/jcc.27061

Novak, Jurica, Prateek Pathak, Maria A. Grishina and Vladimir A. Potemkin. "The design of compounds with desirable properties – The anti-HIV case study." Journal of Computational Chemistry 44, no. 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), pp. 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]. 2022 December 19 [cited 2024 November 19];44(10):1016-1030. doi: 10.1002/jcc.27061

J. Novak, P. Pathak, M. A. Grishina and V. A. Potemkin, "The design of compounds with desirable properties – The anti-HIV case study", Journal of Computational Chemistry, vol. 44, no. 10, pp. 1016-1030, December 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:193:158194. [Accessed: 19 November 2024]

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Metadata
Title (english)The design of compounds with desirable properties – The anti-HIV case study
AuthorJurica Novak
AuthorPrateek Pathak
AuthorMaria A. Grishina
AuthorVladimir A. Potemkin
Author's institutionUniversity of Rijeka
(Faculty of Biotechnology and Drug Development)
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Chemistry
Theoretical Chemistry
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionAccepted version
Journal titleJournal of Computational Chemistry
Numberingvol. 44, no. 10, pp. 1016-1030
p-ISSN0192-8651
e-ISSN1096-987X
DOIhttps://doi.org/10.1002/jcc.27061
URN:NBNurn:nbn:hr:193:158194
Online first2022-12-19
Document URLhttps://onlinelibrary.wiley.com/doi/10.1002/jcc.27061
Type of resourceText
Access conditionsOpen access
Embargo expiration date: 2024-03-20
Terms of useLicence In copyright icon
Created on2023-03-17 13:54:45