Mattioni, Brian E. published the artcilePredicting the Genotoxicity of Secondary and Aromatic Amines Using Data Subsetting To Generate a Model Ensemble, Category: thiazole, the publication is Journal of Chemical Information and Computer Sciences (2003), 43(3), 949-963, database is CAplus and MEDLINE.
Binary quant. structure-activity relationship (QSAR) models are developed to classify a data set of 334 aromatic and secondary amine compounds as genotoxic or nongenotoxic based on information calculated solely from chem. structure. Genotoxic endpoints for each compound were determined using the SOS Chromotest in both the presence and absence of an S9 rat liver homogenate. Compounds were considered genotoxic if assay results indicated a pos. genotoxicity hit for either the S9 inactivated or S9 activated assay. Each compound in the data set was encoded through the calculation of numerical descriptors that describe various aspects of chem. structure (e.g. topol., geometric, electronic, polar surface area). Furthermore, five addnl. descriptors that focused on the secondary and aromatic nitrogen atoms in each mol. were calculated specifically for this study. Descriptor subsets were examined using a genetic algorithm search engine interfaced with a k-Nearest Neighbor fitness evaluator to find the most information-rich subsets, which ultimately served as the final predictive models. Models were chosen for their ability to minimize the total number of misclassifications, with special attention given to those models that possessed fewer occurrences of pos. toxicity hits being misclassified as nontoxic (false negatives). In addition, a subsetting procedure was used to form an ensemble of models using different combinations of compounds in the training and prediction sets. This was done to ensure that consistent results could be obtained regardless of training set composition The procedure also allowed for each compound to be externally validated three times by different training set data with the resultant predictions being used in a “majority rules” voting scheme to produce a consensus prediction for each member of the data set. The individual models produced an average training set classification rate of 71.6% and an average prediction set classification rate of 67.7%. However, the model ensemble was able to correctly classify the genotoxicity of 72.2% of all prediction set compounds
Journal of Chemical Information and Computer Sciences published new progress about 92-36-4. 92-36-4 belongs to thiazole, auxiliary class Organic Pigment, name is 2-(4-Aminophenyl)-6-methylbenzothiazole, and the molecular formula is C14H12N2S, Category: thiazole.
Referemce:
https://pubchem.ncbi.nlm.nih.gov/compound/thiazole,
Thiazole | chemical compound | Britannica