title: Five-descriptor model to predict the chromatographic sequence of natural compounds.
abstract: Despite the recent introduction of mass detection techniques, ultraviolet detection is still widely applied in the field of the chromatographic analysis of natural medicines. Here, a neural network cascade model consisting of nine small artificial neural network units was innovatively developed to predict the chromatographic sequence of natural compounds by integrating five molecular descriptors as the input. A total of 117 compounds of known structure were collected for model building. The order of appearance of each compound was determined in gradient chromatography. Strong linear correlation was found between the predicted and actual chromatographic position orders (Spearman's rho = 0.883, p < 0.0001). Application of the model to the external validation set of nine natural compounds was shown to dramatically increase the prediction accuracy of the real chromatographic order of multiple compounds. A case study shows that chromatographic sequence prediction based on a neural network cascade facilitated compound identification in the chromatographic fingerprint of Radix Salvia miltiorrhiza. For natural medicines of known compound composition, our method provides a feasible means for identifying the constituents of interest when only ultraviolet detection is available.
authors.affiliation: Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
authors.forename: Shuying
authors.initials: S
authors.lastname: Hou
authors.affiliation: Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
authors.forename: Jinhua
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Pharmacy Administration, Harbin Medical University, Harbin, P. R., China.
authors.forename: Zhangming
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Biology Pharmacy, Heilongjiang Vocational College of Biology Science and Technology, Harbin, P. R., China.
authors.forename: Songling
authors.initials: S
authors.lastname: Yang
authors.affiliation: Department of Nephrology, the Fourth Affiliated Hospital, Harbin Medical University, Harbin, P. R., China.
authors.forename: Jia
authors.initials: J
authors.lastname: Xu
authors.affiliation: Institute of Clinical Pharmacology, the Second Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
authors.forename: Wenliang
authors.initials: W
authors.lastname: Zhu
mesh_terms: D002851:Chromatography, High Pressure Liquid; D004365:Drugs, Chinese Herbal / Q000737:chemistry*; D016571:Neural Networks, Computer; D027561:Salvia miltiorrhiza / Q000737:chemistry*
chemical_list: D004365:Drugs, Chinese Herbal
keywords: Liquid chromatography; Natural compounds; Neural network cascade; Radix Salvia miltiorrhiza; Ultraviolet detection