Linear quantitative structure-ecotoxicity relationship modeling of a series of symmetrical triazine derivatives based on physicochemical parameters

Linear quantitative structure-ecotoxicity relationship modeling of a series of symmetrical triazine derivatives based on physicochemical parameters

The present study reports the Quantitative Structure-Ecotoxicity Relationship (QSER) analysis of a series of 21 1,3,5-triazine derivatives based on multiple-linear regression (MLR) method. The ecotoxicity data were estimated by using in silico approach and included the following parameters:...

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Bibliographic Details
Main Authors: Kovačević Strahinja, Karadžić-Banjac Milica, Jevrić Lidija, Podunavac-Kuzmanović Sanja
Format: Article
Language:English
Published: Faculty of Technology, Novi Sad 2023-01-01
Series:Acta Periodica Technologica
Subjects:
chemometrics
pesticides
toxicity
qsar
molecular modeling
Online Access:https://doiserbia.nb.rs/img/doi/1450-7188/2023/1450-71882354255K.pdf
Description
Summary:The present study reports the Quantitative Structure-Ecotoxicity Relationship (QSER) analysis of a series of 21 1,3,5-triazine derivatives based on multiple-linear regression (MLR) method. The ecotoxicity data were estimated by using in silico approach and included the following parameters: acute algae toxicity (AAT), acute daphnia toxicity (ADT), Daphnia Magna LC50 48h/EPA (DMepa) and Daphnia Magna LC50 48h/DEMETRA (DMdemetra). The ecotoxicity data were correlated with molecular descriptors selected by using the stepwise selection method. The considered molecular descriptors are lipophilicity descriptors (CrippenLogP, ALogp2), Autocorrelation Descriptor Mass (ATSm1, ATSm2, ATSm3, ATSm4), Autocorrelation Descriptor Charge (ATSc2), minimum E-states for (strong) hydrogen bond acceptors (minHBa), maximum E-states for (strong) hydrogen bond acceptors (maxHBa), second kappa shape index (Kier2), maximum atom-type E-State: “:N:” (maxaaN), sum of path lengths starting from nitrogens (WTPT-5) and McGowan characteristic volume (McGowan_Volume). The modeling resulted in four statistically valid MLR models. The models were validated by the internal and external validation approaches. The external validation confirmed high predictive ability of the established MLRs.
ISSN:1450-7188
2406-095X

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