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Applicability domain for in silico models to achieve accuracy of experimental measurements.
J. Chemometr. 24, 202-208 (2010)
The accuracy of in silico models can be inhomogeneous: models can show excellent performance on some chemical subspaces but have low accuracy on others. We show that applicability domain (AD) approaches can differentiate reliable and non-reliable predictions and identify those with experimental accuracy for both regression and classification models. For reliably predicted molecules, the predicted values can be used instead of experimental measurements. This can halve time and costs of experimental measurements. The developed classification models for AMES mutagenicity test and CYP450 inhibition, which are important drug discovery properties, are publicly available at the online chemical database and modeling environment (OCHEM) site http://qspr.eu.
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Publication type Article: Journal article
Document type Scientific Article
Keywords applicability domain; accuracy of predictions; physico-chemical properties; toxicity prediction