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Prediction of Autoignition Temperature for Diverse Organic Compounds using In Silico Methods and Molecular Descriptors

A large dataset of 446 structurally diverse organic compounds has been used to develop mathematical models that relate the structures within this heterogeneous group of compounds to their autoignition temperature (AIT) values. For the development of such Quantitative Structure Property (QSPR) models, the molecular structure of each compound was represented by calculated molecular descriptors which encode their topological, electronic and geometric features. Correlation Feature Selection method combined with Best First evaluator was used to select the most significant descriptors that were used as inputs for the development of several models. Different modeling methodologies such as kNN, SVM and MLR were then applied and the ability of the new models to predict AIT was assessed and compared to available experimental data. The accuracy and robustness of the produced models was based on validation principles as described by the Organisation for Economic Cooperation and Development (OECD). The kNN model was proven to be the most accurate model. Moreover the applicability domain of the models based on similarity measurements has been defined to indicate reliable predictions. For structures that fall within the domain of applicability, the proposed models can be used to predict AIT values based solely on their structure.