Singh, Kunwar P. published the artcileMultispecies QSAR Modeling for Predicting the Aquatic Toxicity of Diverse Organic Chemicals for Regulatory Toxicology, Quality Control of 350-30-1, the publication is Chemical Research in Toxicology (2014), 27(5), 741-753, database is CAplus and MEDLINE.
The research aims to develop multispecies quant. structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chems. in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicol. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) exptl. toxicity data for chems. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chems. in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chems. and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, resp., whereas regression QSAR models (DTB and DTF) rendered high correlation (R2) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chems. for screening and prioritization of chems. for regulation.
Chemical Research in Toxicology published new progress about 350-30-1. 350-30-1 belongs to chlorides-buliding-blocks, auxiliary class Fluoride,Chloride,Nitro Compound,Benzene, name is 3-Chloro-4-fluoronitrobenzene, and the molecular formula is 0, Quality Control of 350-30-1.
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