Quantitative structure-mobility relationship study of a diverse set of organic acids using classification and regression trees and adaptive neuro-fuzzy inference systems.
A quantitative structure-mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities (mu(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.
Department of Chemistry, Sharif University of Technology, Tehran, Iran. jalali@sharif.edu
Mehdi
M
Jalali-Heravi
Parviz
P
Shahbazikhah
D000465:Algorithms; D002264:Carboxylic Acids / Q000302:isolation & purification*; D004586:Electrophoresis; D017143:Fuzzy Logic; D008956:Models, Chemical; D010636:Phenols / Q000302:isolation & purification; D013451:Sulfonic Acids / Q000302:isolation & purification
D002264:Carboxylic Acids; D010636:Phenols; D013451:Sulfonic Acids