logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction

By Pierre Bruneau, Nathan R. McElroy. 2006

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Lipophilicity in drug discovery (2010, background): “One of the reasons for this discrepancy is that the molecules being predicted are dissimilar to those in the training sets of the models [10] and this has led many organisations to create bespoke models trained on their own data, which are more relevant to the compounds of interest [14].”
A renaissance of neural networks in drug discovery (2016, methodology): “Researchers from AstraZeneca have employed Bayesian-regularized neural networks to model important physicochemical properties of drugs such as aqueous solubility [42] and log D the pH-dependent distribution of drugs between lipophilic and aqueous phases [43].”
Guidelines for the welfare and use of animals in cancer research (2010, background): “Chemical and physical properties In silico predictions or in vitro measurements of logP (Mannhold et al, 2009), logD (Bruneau and McElroy, 2006; Dohta et al, 2007), pKa (Lee et al, 2007) and solubility (Colclough et al, 2008; Du-Cuny et al, 2008) Metabolism Mouse, rat, human liver microsomes,…”
Can we estimate the accuracy of ADME-Tox predictions? (2006, methodology): “A PHYSPROP dataset containing 12,908 experimental logP measurements (training set), and two datasets with experimental logD measurements for 7,498 neutral compounds from AstraZeneca (AZ) and 8,750 neutral compounds from Pfizer (PFE) were used and were as described in previous publications [5,6,22, 3”