title: Prediction of aqueous solubility of organic compounds based on a 3D structure representation.
abstract: Two quantitative models for the prediction of aqueous solubility of 1293 organic compounds were developed by a Multilinear Regression (MLR) analysis and a Back-Propagation (BPG) neural network. The molecules were described by a set of 32 values of a Radial Distribution Function (RDF) code representing the 3D structure and eight additional descriptors. The 1293 compounds were divided into a training set of 797 compounds and a test set of 496 compounds based on a Kohonen self-organizing neural network map. The obtained models show a good predictive power: for the test set, a correlation coefficient of 0.96 and a standard deviation of 0.59 were achieved by the back-propagation neural network approach.
authors.affiliation: Computer-Chemie-Centrum and Institut für Organische Chemie, Universität Erlangen-Nürnberg, Nägelsbachstrasse 25, D-91052 Erlangen, Germany.
authors.forename: Aixia
authors.initials: A
authors.lastname: Yan
authors.forename: Johann
authors.initials: J
authors.lastname: Gasteiger