Data mining for seeking accurate quantitative relationship between molecular structure and GC retention indices of alkanes by projection pursuit

Authors:
Yiping Du;Yizeng Liang
Affiliations:
College of Chemistry and Chemical Engineering, Institute of Chemometrics and Intelligent Analytical Instruments, Central South University, Changsha 410083, People's Republic of China and Departmen ...;College of Chemistry and Chemical Engineering, Institute of Chemometrics and Intelligent Analytical Instruments, Central South University, Changsha 410083, People's Republic of China
Venue:
Computational Biology and Chemistry
Year:
2003

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Abstract

Primary data mining on alkanes for seeking accurate quantitative relationship between molecular structure and retention indices of gas chromatography is developed in this paper. Based on the results obtained from projection pursuit (PP), a new variable named class distance variable, which essentially describes the branching structure of the alkanes, is proposed. With the help of the new variable, both fitting and prediction accuracy of the regression model can be dramatically improved. The results obtained in this work show that the technique of PP developed in statistics is a quite promising tool for seeking accurate quantitative structure-activity relationship (QSAR) and/or quantitative structure-property relationship (QSPR) researches.