Prediction of pesticides chromatographic lipophilicity from the computational molecular descriptors.
نویسندگان
چکیده
Quantitative structure-property relationship models were developed for the prediction of pesticides and some PAH compounds lipophilicity based on a wide set of computational molecular descriptors and a set of experimental chromatographic data. The chromatographic lipophilicity of pesticides has been evaluated by high-performance liquid chromatography (HPLC) using different chemically bonded (C18, C8, CN and Phenyl HPLC columns) stationary phases and two different organic modifiers (methanol and acetonitrile, respectively) in the mobile phase composition. Through a systematic study, by using the classic multivariate analysis, several quantitative structure-property/lipophilicity multi-dimensional models were established. Multiple linear regression and genetic algorithm for the variable subset selection were used. The internal and external statistical evaluation procedures revealed some appropriate models for the chromatographic lipophilicity prediction of pesticides. Moreover, the statistical parameters of regression and those obtained by applying t-test for the intercept (a(0)) and for the slope (a(1)) in order to evaluate relationship between experimental and predicted octanol-water partition coefficients in case of the test set compounds, revealed two statistically valid models that can be successfully used in lipophilicity prediction of pesticides.
منابع مشابه
Novel Atom-Type-Based Topological Descriptors for Simultaneous Prediction of Gas Chromatographic Retention Indices of Saturated Alcohols on Different Stationary Phases
In this work, novel atom-type-based topological indices, named AT indices, were presented as descriptors to encode structural information of a molecule at the atomic level. The descriptors were successfully used for simultaneous quantitative structure-retention relationship (QSRR) modeling of saturated alcohols on different stationary phases (SE-30, OV-3, OV-7, OV-11, OV-17 and OV-25). At first...
متن کاملNovel consensus quantitative structure-retention relationship method in prediction of pesticides retention time in nano-LC
In this study, quantitative structure-retention relationship (QSRR) methodology employed for modeling of the retention times of 16 banned pesticides in nano-liquid chromatography (nano-LC) column. Genetic algorithm-multiple linear regression (GA-MLR) method employed for developing global and consensus QSRR models. The best global GA-MLR model was established by adjusting GA parameters. Three de...
متن کاملQSRR prediction of the chromatographic retention behavior of painkiller drugs.
Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromatographic retention time to the chemical structure of a solute. A QSRR study has been carried out on the reversed-phase high-performance liquid chromatography retention times (log tR) of 62 diverse drugs (painkillers) by using molecular descriptors. Multiple linear regression (MLR) is u...
متن کاملPrediction of s-triazine components lipophilicity of total herbicides
A liquid chromatography method has been developed and validated for the determination of mathematical models for prediction of the lipophilicity of s-triazine compounds. Correlation between retention factors, RM 0 , of several s-triazine derivatives and their physico-chemical and structural properties has been studied by TLC on silica gel impregnated with paraffin oil. The research in this pape...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of separation science
دوره 34 3 شماره
صفحات -
تاریخ انتشار 2011