title: Measurement and ANN prediction of pH-dependent solubility of nitrogen-heterocyclic compounds.
abstract: Based on the solubility of 25 nitrogen-heterocyclic compounds (NHCs) measured by saturation shake-flask method, artificial neural network (ANN) was employed to the study of the quantitative relationship between the structure and pH-dependent solubility of NHCs. With genetic algorithm-multivariate linear regression (GA-MLR) approach, five out of the 1497 molecular descriptors computed by Dragon software were selected to describe the molecular structures of NHCs. Using the five selected molecular descriptors as well as pH and the partial charge on the nitrogen atom of NHCs (QN) as inputs of ANN, a quantitative structure-property relationship (QSPR) model without using Henderson-Hasselbalch (HH) equation was successfully developed to predict the aqueous solubility of NHCs in different pH water solutions. The prediction model performed well on the 25 model NHCs with an absolute average relative deviation (AARD) of 5.9%, while HH approach gave an AARD of 36.9% for the same model NHCs. It was found that QN played a very important role in the description of NHCs and, with QN, ANN became a potential tool for the prediction of pH-dependent solubility of NHCs.
authors.affiliation: Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China; Wanhua Chemical (Ningbo) Co. Ltd, Ningbo 315000, China.
authors.forename: Feifei
authors.initials: F
authors.lastname: Sun
authors.affiliation: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310027, China.
authors.forename: Qingni
authors.initials: Q
authors.lastname: Yu
authors.affiliation: Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Jingke
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Lecheng
authors.initials: L
authors.lastname: Lei
authors.affiliation: Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Zhongjian
authors.initials: Z
authors.lastname: Li
authors.affiliation: Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China. Electronic address: xwzhang@zju.edu.cn.
authors.forename: Xingwang
authors.initials: X
authors.lastname: Zhang
mesh_terms: D004785:Environmental Pollutants / Q000737:chemistry*; D006571:Heterocyclic Compounds / Q000737:chemistry*; D006863:Hydrogen-Ion Concentration; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility; D014867:Water / Q000737:chemistry
chemical_list: D004785:Environmental Pollutants; D006571:Heterocyclic Compounds; D014867:Water; D009584:Nitrogen
keywords: ANN; Nitrogen-heterocyclic compounds; QSPR; pH-dependent solubility