Document Detail


Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts.
MedLine Citation:
PMID:  22947043     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores or biophores. They are the main building blocks/classifying units of the model and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining appropriate applicability domain is required as part of the OECD guidelines for the validation of (Q)SARs. In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts and different alerts of a model usually represent different toxicity mechanisms, cover different structural space and the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction tradeoffs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a model's full range of sensitivity-specificity spectrum and therefore the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.
Authors:
Suman Chakravarti; Roustem Saiakhov; Gilles Klopman
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-4
Journal Detail:
Title:  Journal of chemical information and modeling     Volume:  -     ISSN:  1549-960X     ISO Abbreviation:  J Chem Inf Model     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-5     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101230060     Medline TA:  J Chem Inf Model     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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