QM/NN QSPR models with error estimation: vapor pressure and logP
QSPR models for logP and vapor pressures of organic compounds based on neural net interpretation of descriptors derived from quantum mechanical (semiempirical MO; AM1) calculations are presented. The models are cross-validated by dividing the compound set into several equal portions and training several individual multilayer feedforward neural nets (trained by the back-propagation of errors algorithm), each with a different portion as test set. The results of these nets are combined to give a mean predicted property value and a standard deviation. The performance of two models, for logP and the vapor pressure at room temperature, is analyzed, and the reliability of the predictions is tested.
Beck
B
Computer-Chemie-Centrum, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany.
Breindl
A
Clark
T