logD7.4 modeling using Bayesian regularized neural networks.: Assessment and correction of the errors of prediction

Bayesian Regularized Neural Networks (BRNNs) employing Automatic Relevance Determination (ARD) are used to construct a predictive model for the distribution coefficient logD(7.4) from an in-house data set of 5000 compounds with experimental endpoints. A method for assessing the accuracy of prediction is established based upon a query compound's distance to the training set. logD(7.4) predictions are also dynamically corrected with an associated library of compounds of continuously updated, experimentally measured logD(7.4) values. A comparison of local models and associated libraries comprising separate ionization class subsets of compounds to compounds of a homogeneous ionization class reveals in this case that local models and libraries have no advantage over global models and libraries.