QSPR Modeling of the Reactivity Parameters of Monomers in Radical Copolymerizations

For a series of monomers, QSPR test analysis is performed by optimizing the correlation weights of the local invariants of molecular graphs representing monomer structures in order to construct models of the reactivity parameters of monomers Q and e. This approach may be used as a tool in reactivity predictions for monomers for which no experimental data on Q and e are available.Original Russian Text Copyright © 2004 by A. A. Toropov, V. O. Kudyshkin, N. L. Voropaeva, I. N. Ruban, and S. Sh. Rashidova__________Translated from Zhurnal Strukturnoi Khimii, Vol. 45, No. 6, pp. 994–998, November–December, 2004.     

Autoignition temperature: comprehensive data analysis and predictive models

ABSTRACT

Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure-AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic model could be built. This led to a selection of the dataset containing 875 compounds annotated with AIT values. The thereupon-based SVR model performs reasonably well in cross-validation with the determination coefficient r ² = 0.77 and mean absolute error MAE = 37.8°C. External validation on 20 industrial compounds missing in the training set confirmed its good predictive power (MAE = 28.7°C).     

Quantitative Structure-Toxicity Relationships Using Tops-Mode. 2. Neurotoxicity of a Non-Congeneric Series of Solvents

Abstract

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.