title: Quantitative structure-retention relationships for mycotoxins and fungal metabolites in LC-MS/MS.
abstract: Quantitative structure-retention relationship (QSRR) models were used to predict the retention time (t(R)) of mycotoxins and fungal metabolites. Heuristic method and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 5-21-1 RBFNN architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a square of correlation coefficient (R(2)) of 0.8709 and root mean square error of 1.2892 for the test set. This article provided a useful tool for predicting the t(R) of other mycotoxins when experiment data are unknown.
authors.affiliation: Department of Chemistry, Lanzhou University, Lanzhou, Gansu, PR China. jich73@126.com
authors.forename: Caihong
authors.initials: C
authors.lastname: Ji
authors.forename: Yanwei
authors.initials: Y
authors.lastname: Li
authors.forename: Li
authors.initials: L
authors.lastname: Su
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Zhang
authors.forename: Xingguo
authors.initials: X
authors.lastname: Chen
mesh_terms: D005658:Fungi / Q000737:chemistry* / Q000378:metabolism; D008962:Models, Theoretical; D009183:Mycotoxins / Q000032:analysis* / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship
chemical_list: D009183:Mycotoxins