NDLI: A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery

Content Provider	Springer Nature Link
Author	Aliagas, Ignacio Gobbi, Alberto Heffron, Timothy Lee, Man Ling Ortwine, Daniel F. Zak, Mark Khojasteh, S. Cyrus
Copyright Year	2015
Abstract	Using data from the in vitro liver microsomes metabolic stability assay, we have developed QSAR models to predict in vitro human clearance. Models were trained using in house high-throughput assay data reported as the predicted human hepatic clearance by liver microsomes or pCL$_{h}$. Machine learning regression methods were used to generate the models. Model output for a given molecule was reported as its probability of being metabolically stable, thus allowing for synthesis prioritization based on this prediction. Use of probability, instead of the regression value or categories, has been found to be an efficient way for both reporting and assessing predictions. Model performance is evaluated using prospective validation. These models have been integrated into a number of desktop tools, and the models are routinely used to prioritize the synthesis of compounds. We discuss two therapeutic projects at Genentech that exemplify the benefits of a probabilistic approach in applying the models. A three-year retrospective analysis of measured liver microsomes stability data on all registered compounds at Genentech reveals that the use of these models has resulted in an improved metabolic stability profile of synthesized compounds.
Starting Page	327
Ending Page	338
Page Count	12
File Format	PDF
ISSN	0920654X
Journal	Journal of Computer-Aided Molecular Design
Volume Number	29
Issue Number	4
e-ISSN	15734951
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-02-24
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Liver microsomes In vitro clearance Probability QSAR model Physical Chemistry Computer Applications in Chemistry Animal Anatomy / Morphology / Histology
Content Type	Text
Resource Type	Article
Subject	Drug Discovery Physical and Theoretical Chemistry Computer Science Applications

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