title: Prediction of pKa values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors.
abstract: Two quantitative pKa prediction models for aliphatic carboxylic acids and for alcohols were developed by multiple linear-regression (MLR) analysis with empirical atomic descriptors. The acid and alcohol molecules were described by a set of five and four atomic descriptors, respectively. For the pKa model of 1122 aliphatic carboxylic acids, the squared correlation coefficient is 0.813 with a standard error of prediction of 0.423; for the pKa model of 288 alcohols, the squared correlation coefficient is 0.817 with a standard error of prediction of 0.755, respectively. The good predictive abilities of the models obtained were indicated by both cross-validation and by external validation. An atomic descriptor was developed to model the inductive effect of the neighboring atoms for a central atom in a molecule. The ability of the descriptor to measure the inductive effect of substituent groups was demonstrated by a good correlation of this descriptor with Taft sigma* constants in aliphatic carboxylic acids. It provides a new approach to estimate Taft sigma* constants directly from molecular structures. An algorithm using Kohonen neural networks for splitting a data set into a training set and a test set is also presented.
authors.affiliation: Computer-Chemie-Centrum and Institut für Organische Chemie, Universität Erlangen-Nürnberg, Nägelsbachstrasse 25, D-91052, Erlangen, Germany.
authors.forename: Jinhua
authors.initials: J
authors.lastname: Zhang
authors.forename: Thomas
authors.initials: T
authors.lastname: Kleinöder
authors.forename: Johann
authors.initials: J
authors.lastname: Gasteiger
mesh_terms: D000438:Alcohols / Q000737:chemistry*; D000465:Algorithms; D002264:Carboxylic Acids / Q000737:chemistry*; D002625:Chemistry, Organic / Q000379:methods; D006860:Hydrogen Bonding; D006863:Hydrogen-Ion Concentration; D016014:Linear Models; D008956:Models, Chemical; D015233:Models, Statistical; D015394:Molecular Structure; D016571:Neural Networks, Computer
chemical_list: D000438:Alcohols; D002264:Carboxylic Acids