Autoignition temperature: comprehensive data analysis and predictive models

I.I. Baskin , S. Lozano , M. Durot , G. Marcou (1) , Dragos Horvath (1) , A. Varnek (1)

1 CMC - Chimie de la matière complexe

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 predic-tive power (MAE = 28.7°C). ARTICLE HISTORY

Mots clés: Quantitative structure- property relationship (QSPR); autoignition temperature; support vector regression; fragment descriptors; generative topographic mapping; Quantitative Structure-Property Prediction (QSPR); Quantitative structure- property relationship (QSPR)

Domaines: Chemo-informatique; Génie chimique

I.I. Baskin, S. Lozano, M. Durot, G. Marcou, Dragos Horvath, et al.. Autoignition temperature: comprehensive data analysis and predictive models. SAR and QSAR in Environmental Research, 2020, 31 (8), pp.597-613. ⟨10.1080/1062936X.2020.1785933⟩. ⟨hal-02950552⟩