logD 7.4 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.