Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index

作者：Mihaleva V V; Verhoeven H A; de Vos R C H; Hall R D; van Ham R C H J^*

来源：Bioinformatics, 2009, 25(6): 787-794.

DOI：10.1093/bioinformatics/btp056

Motivation: Matching both the retention index (RI) and the mass spectrum of an unknown compound against a mass spectral reference library provides strong evidence for a correct identification of that compound. Data on retention indices are, however, available for only a small fraction of the compounds in such libraries. We propose a quantitative structure-RI model that enables the ranking and filtering of putative identifications of compounds for which the predicted RI falls outside a predefined
Results: We constructed multiple linear regression and support vector regression (SVR) models using a set of descriptors obtained with a genetic algorithm as variable selection method. The SVR model is a significant improvement over previous models built for structurally diverse compounds as it covers a large range (360-4100) of RI values and gives better prediction of isomer compounds. The hit list reduction varied from 41% to 60% and depended on the size of the original hit list. Large hit lists were reduced to a greater extend compared with small hit lists.

• 出版日期2009-3-15