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Predicting Toxicity of Aromatic Pollutants Using QSAR Modeling


Rasulev, Bakhtiyor; Kušić, Hrvoje; Leszczynska, Danuta; Leszczynski, Jerzy; Koprivanac, Natalija
Predicting Toxicity of Aromatic Pollutants Using QSAR Modeling // Book of Abstracts of 2nd International Symposium on Environmental Management SEM2007 / Koprivanac, Natalija ; Kušić, Hrvoje (ur.).
Zagreb: Fakultet kemijskog inženjerstva i tehnologije, 2007. (poster, sažetak, znanstveni)


Naslov
Predicting Toxicity of Aromatic Pollutants Using QSAR Modeling

Autori
Rasulev, Bakhtiyor ; Kušić, Hrvoje ; Leszczynska, Danuta ; Leszczynski, Jerzy ; Koprivanac, Natalija

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

Izvornik
Book of Abstracts of 2nd International Symposium on Environmental Management SEM2007 / Koprivanac, Natalija ; Kušić, Hrvoje - Zagreb : Fakultet kemijskog inženjerstva i tehnologije, 2007

ISBN
978-953-6470-33-4

Skup
2nd International Symposium on Environmental Management SEM2007

Mjesto i datum
Zagreb, Hrvatska, 12-14.09.2007.

Vrsta sudjelovanja
Poster

Vrsta recenzije
Neobjavljeni rad

Ključne riječi
QSAR; toxicity; aromatic pollutants

Sažetak
A large amount of overall organic chemical compounds produced and used annually pertain to aromatic compounds, highly toxic to living organisms in aquatic systems and soil, but to humans too, and moreover, many of them are reported as carcinogenic and mutagenic. One of the most successful approaches for predicting their toxic effect could be found in the application of QSAR/QSPR (quantitative structure-activity/property relationship) modeling. This powerful technique quantitatively relates variations in biological activity, i.e. toxicity, to changes in molecular structure and properties. Hence, the goal of the study was to predict toxicity in vivo of aromatic compounds structured by single benzene ring and including presence and absence of different substitute groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc, by using QSAR/QSPR tool. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. Evaluation of models was performed by calculating and comparing their model performances (R2, s, F, Q2) after splitting set of organic compounds to training and test sets. As the most predictive model is shown the 3-variable model having also a good ratio of the number of descriptors and their predictive ability. The main contribution to the toxicity showed descriptors belonging to 2D autocorrelation and atom-centered fragments descriptors, respectively. The GA-MLRA approach showed good results in this study, which allows to built simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals

Izvorni jezik
Engleski

Znanstvena područja
Kemijsko inženjerstvo



POVEZANOST RADA


Projekt / tema
125-1253092-1981 - Obrada otpadnih voda naprednim oksidacijskim tehnologijama (Sanja Papić, )

Ustanove
Fakultet kemijskog inženjerstva i tehnologije, Zagreb