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Quantitative structure–mobility relationship study of a diverse set of organic acids using classification and regression trees and adaptive neuro‐fuzzy inference systems
Author(s) -
JalaliHeravi Mehdi,
Shahbazikhah Parviz
Publication year - 2008
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200700136
Subject(s) - pattern recognition (psychology) , set (abstract data type) , biological system , regression , artificial intelligence , calibration , selection (genetic algorithm) , computer science , adaptive neuro fuzzy inference system , fuzzy logic , mathematics , data mining , statistics , fuzzy control system , biology , programming language
A quantitative structure–mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities ( μ 0 ) of a diverse dataset consisting of 115 carboxylic and sulfonic acids were investigated. A set of 1195 zero‐ to three‐dimensional descriptors representing various structural characteristics was calculated for each molecule in the dataset. Classification and regression trees were successfully used as a descriptor selection method. Four descriptors were selected and used as inputs for adaptive neuro‐fuzzy inference system. The root mean square errors for the calibration and prediction sets are 1.61 and 2.27, respectively, compared with 3.60 and 3.93, obtained from a previous mechanistic model.