Genetic algorithm as a variable selection procedure for the simulation of 13C nuclear magnetic resonance spectra of flavonoid derivatives using multiple linear regression.
In order to accurately simulate (13)C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure-property relationship (QSPR) model, relating atom-based calculated descriptors to (13)C NMR chemical shifts (ppm, TMS=0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53ppm, respectively, for the prediction set.
Ghavami
Raoof
R
Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran. rghavami@uok.ac.ir
Najafi
Amir
A
Sajadi
Mohammad
M
Djannaty
Farhad
F
D000465:Algorithms; D002247:Carbon Isotopes; D003198:Computer Simulation / Q000706:statistics & numerical data; D005419:Flavonoids / Q000737:chemistry*; D016014:Linear Models; D009682:Magnetic Resonance Spectroscopy / Q000706:statistics & numerical data*; D008956:Models, Chemical; D008957:Models, Genetic; D015999:Multivariate Analysis; D021281:Quantitative Structure-Activity Relationship
D002247:Carbon Isotopes; D005419:Flavonoids