کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8376872 | 1543162 | 2017 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات محاسباتی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine](/preview/png/8376872.png)
چکیده انگلیسی
A quantitative structure property relationship (QSPR) study based on enhanced replacement method (ERM) and support vector machine (SVM) was used to correlate molecular structures to their bovine serum albumin water partition coefficients (KBSA/W). A wide variety of natural organic compounds and drugs were selected as a dataset and suitable sets of molecular descriptors were calculated using Dragon package. ERM was used as variable selection method. The nonlinear-support vector machine models were applied to correlate the ERM-selected molecular descriptors with the experimental values of KBSA/W. Results obtained demonstrate the reliability and good predictability of support vector machine model to predict KBSA/W of organic compounds and drugs. Satisfactory results demonstrate that the ERM approach is a very powerful method for variable selection and the predictive ability of the SVM model is superior to those acquired by ERM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Toxicology - Volume 4, November 2017, Pages 1-10
Journal: Computational Toxicology - Volume 4, November 2017, Pages 1-10
نویسندگان
Zahra Dashtbozorgi, Hassan Golmohammadi, Sajad Khooshechin,