Prediction of toxicity and octanol – water partition coefficient of organochlorine pesticides using Molecular Descriptors and GA-MLR Method
Article Type:
Research/Original Article (بدون رتبه معتبر)

In this research, Quantitative Structure–Activity Relationship (QSAR) studies have been used to predict activities of organochlorine pesticides. Firstly, the chemical structure of molecules was drawn with the Gauss view 05 program and optimized at Hartree–Fock level of theory and 6-31G* basis sets using Gaussian 09 software. The physiochemical properties namely octanol-water partition coefficient (logP) and toxicity (log LD50) are taken from the scientific web book. The dragon software has been used for the calculation of molecular descriptors. The suitable descriptors were selected with the aid of the genetic algorithm (GA) and backward techniques. At the next step, the relationship between molecular descriptors and the activities was investigated by multiple linear regression (MLR) method. In order to build and test QSAR models, a data set of organochlorine pesticides was randomly separated into 2 groups: training (80%) and test (20%) sets. The models were evaluated with regression parameters: correlation coefficient (R), squared regression coefficient (R2), adjusted correlation coefficient (R2 adj) and root mean squared error (RMSE). For the predictive ability and verification of the models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The external prediction accuracy of the obtained models was examined using the above regression parameters. Results of validations and high statistical quality of models indicate that generated GA-MLR models are reasonable QSAR models. These models help to delineate the important descriptors responsible for predicting their activities.

Iranian Journal of Entomological Research, Volume:11 Issue: 3, 2019
245 to 257  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 990,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 50 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!