title: logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.
abstract: Bayesian Regularized Neural Networks (BRNNs) employing Automatic Relevance Determination (ARD) are used to construct a predictive model for the distribution coefficient logD7.4 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. logD7.4 predictions are also dynamically corrected with an associated library of compounds of continuously updated, experimentally measured logD7.4 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.
authors.affiliation: AstraZeneca, Parc Industriel Pompelle, BP 1050, 51689 Reims Cedex 2, France. pierre.bruneau@astrazeneca.com
authors.forename: Pierre
authors.initials: P
authors.lastname: Bruneau
authors.forename: Nathan R
authors.initials: NR
authors.lastname: McElroy
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016571:Neural Networks, Computer