Naslov
A QSAR study of quinazolinone derivatives antifungal activity against Sclerotinia sclerotiorum

Autori
Karnaš, Maja ; Šubarić, Domagoj ; Agić, Dejan ; Komar, Mario ; Molnar, Maja ; Vrandečić, Karolina ; Ćosić, Jasenka ; Rastija, Vesna

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
23rd European Symposium on Quantitative Structure- Activity Relationship, Integrative Data-Intensive Approaches to Drug Design, Book of Abstracts / - , 2022, 138-138

Skup
23rd European Symposium on Quantitative Structure- Activity Relationship, Integrative Data-Intensive Approaches to Drug Design

Mjesto i datum
Heidelberg, Njemačka, 26.09.2022. - 30.09.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
quinazolinone derivatives ; QSAR ; antifungal activity

Sažetak
Sclerotinia sclerotiorum (Lib.) de Bary is a necrotrophic plant pathogen responsible for the reduction of the crop quality and yield. One of the most efficient ways to prevent and control diseases caused by S. sclerotiorum is the use of chemical fungicides. However, their use led to the accumulation of residual fungicides in the soil, water, and agricultural products, as well as the appearance of fungicide-resistant forms of this phytopathogen. Therefore, there is a need for new, environmentally friendly compounds that would prove effective against S. sclerotiorum. This study aimed to derive a Quantitative Structure- Activity Relationship (QSAR) for the antifungal activity of 18 quinazolinone derivatives. Antifungal test against S. sclerotiorum was carried out according to the method of Siber et al. Antifungal activities of tested compounds, expressed as % inhibition, were converted in the form of the logarithm values (log % inhibition). The optimized 3D strictures of quinazolinone derivatives were used for the descriptor calculation. Considering the limited number of compounds, splitting into test and training set was not performed. The multiple linear regression QSAR models were obtained with a genetic algorithm (GA) using QSARINS 2.2.4. (University of Insubria, Varese, Italy). The best QSAR model was generated with three DRAGON descriptors: Mv, Mor09e and R6e. The model satisfied the fitting and internal validation criteria, confirming its stability and robustness: R2 = 0.91 ; R2adj = 0.89 ; F = 45.17 ; ΔK = 0.12 ; CCCtr = 0.95 ; Q2loo = 0.85 ; R2-Q2loo = 0.06 ; CCCcv = 0.92 ; R2Yscr = 0.18 ; Q2Yscr = -0.43. Molecular descriptors in obtained QSAR model revealed that quinazolinones of higher molecular weight (Mv), higher contribution of 3D distribution of electronegativity at 8 Å (Mor09e), and enhanced interactions of electronegative atoms at a topological distance 6 (R6e), have lower antifungal effects.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Interdisciplinarne prirodne znanosti

POVEZANOST RADA