Can we estimate the accuracy of ADME-Tox predictions?

By Igor V. Tetko, Pierre Bruneau, Hans-Werner Mewes, Douglas C. Rohrer, Gennadiy Poda. 2006

239 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

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In Analytical Chemistry

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Citations in the corpus

In silico pharmacology for drug discovery: applications to targets and beyond (2007, background): “Several papers have described the applicability domain of models and methods in considerable detail (Dimitrov et al., 2005; Tetko et al., 2006) to calculate this property.”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, unknown): “For all the computational modeling approaches which we may want to consider they are dependent upon the model applicability domain 112 and are affected by the quality of the underlying data, 113, 114 which may in turn determine the utility and relevance of any model and prediction 115 .”
Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds (2009, methodology): “Otherwise a valid comparison of the performance of log P methods could be problematic.(62) It is worth to notice that for this study we used a simple measure, StdDev of models contributing to the Consensus log P model, to discriminate molecules with low and high confidence in prediction.”
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS (2016, methodology): “The developed consensus model estimates both the applicability domain [59] and the accuracy of the prediction for new compounds based on the CONSENSUSSTD distance to model [44, 59].”
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules (2007, methodology): “ANNs [8, 12, 4, 14, 10] and decision trees [11, 10] (resp.”
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011, methodology): “Currently several DMs are supported: the standard deviation of an ensemble of models (STDEV), the correlation in the space of models (CORREL) [79] and Mahalanobis distance (LEVERAGE).”