Article:mtsg07

From Open Babel
Revision as of 10:44, 1 February 2007 by Ghutchis (Talk | contribs) (Fixed DOI)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
@article{mtsg07, 
  author = {Paolo Mazzatorta, Liên-Anh Tran, Benoît Schilter, and
                 Martin Grigorov},
  title = {Integration of Structure-Activity Relationship and
                 Artificial Intelligence Systems To Improve in Silico
                 Prediction of Ames Test Mutagenicity},  
  journal = {Journal of Chemical Information and Modeling}, 
  year = {2007},
  volume = {47},
  number = {1},
  pages = {34--38},
  abstract = {The Ames mutagenicity test in Salmonella typhimurium is
                 a bacterial short-term in vitro assay aimed at
                 detecting the mutagenicity caused by
                 chemicals. Mutagenicity is considered as an early
                 alert for carcinogenicity. After a number of
                 decades, several (Q)SAR studies on this endpoint
                 yielded enough evidence to make feasible the
                 construction of reliable computational models for
                 prediction of mutagenicity from the molecular
                 structure of chemicals. In this study, we propose a
                 combination of a fragment-based SAR model and an
                 inductive database. The hybrid system was developed
                 using a collection of 4337 chemicals (2401 mutagens
                 and 1936 nonmutagens) and tested using 753
                 independent compounds (437 mutagens and 316
                 nonmutagens). The overall error of this system on
                 the external test set compounds is 15\% (sensitivity
                 = 15\%, specificity = 15\%), which is quantitatively
                 similar to the experimental error of Ames test data
                 (average interlaboratory reproducibility determined
                 by the National Toxicology Program). Moreover, each
                 single prediction is provided with a specific
                 confidence level. The results obtained give
                 confidence that this system can be applied to
                 support early and rapid evaluation of the level of
                 mutagenicity concern.},
  doi = {10.1021/ci050400b} 
}