Prediction of 13C NMR chemical shifts by artificial neural network. I. Partial charge model as atomic descriptor
| Autor: | Leonard M. Khalilov, Farit H. Mukminov, Ilya I. Kiryanov, Arthur R. Tulyabaev |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
education.field_of_study
010304 chemical physics Chemistry Process Chemistry and Technology Chemical shift Population Analytical chemistry Linear model Thermodynamics Charge (physics) Carbon-13 NMR 010402 general chemistry 01 natural sciences 0104 chemical sciences Computer Science Applications Analytical Chemistry Partial charge 0103 physical sciences Linear regression education Mulliken population analysis Spectroscopy Software |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 152:62-68 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/j.chemolab.2016.01.010 |
| Popis: | Mulliken population analysis (MPA), Hirshfeld population analysis (HPA), Charge Model 5 (CM5) and Hu Lu Yang charge fitting method (HLY) were considered in order to reveal influence of atomic partial charges on the 13 C NMR chemical shifts. The test set included seven classes of organic molecules. Partial charges of carbon atoms were obtained from quantum-chemical calculations at DFT/HISS level. Linear regressions were constructed as estimators of accuracy of each model. The best approach was shown by multivariate regression with MPA, HPA, and CM5 charges as predictors in a linear model with mean value of R 2 = 0.8917. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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