logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.

Pierre Bruneau; Nathan R McElroy

doi:10.1021/ci0504014

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logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.

Pierre Bruneau·Nathan R McElroy

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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.

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Pierre Bruneau, Nathan R McElroy. logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.. https://doi.org/10.1021/ci0504014

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