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Atomic-level AI topological indices as efficient descriptors for developing predictive QSPR models for flash points of acyclic alkanes

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Abstract

The atomic-level AI topological indices combined with the Xu topological index were utilized in the quantitative structure–property relationship (QSPR) study of the flash point (FP) temperatures of 92 acyclic alkanes. Modeling of the flash points by the multiple linear regression (MLR) resulted in a three-parameter model consisted of Xu, AI(–CH3) and AI (–CH2–) indices by which the maximum value of the relative percent deviation (RPD) for predicting the FP data was found to be 5.6%. The topological descriptors were subsequently utilized as inputs for an artificial neural network (ANN) to develop a more accurate model. The findings showed that using a 3-5-1 ANN, the RPD values does not exceed 4.0%. The relative importance of the topological indices to the flash points followed the order of AI (–CH3) > Xu > AI (–CH2–) suggesting both atomic groups and molecular bulkiness as the main factors influencing FP values of the studied compounds.

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All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Acknowledgements

The authors are thankful to Armita Safa, M.Sc. student in Bioinformatics at the University of British Columbia for her valuable help in writing the algorithms during the computational stage of the work.

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  1. Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, Iran

    Fatemeh Fazehi & Fariba Safa

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  1. Fatemeh Fazehi
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  2. Fariba Safa
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FF contributed to data analyzing and writing the original draft. FS contributed to supervision, project administration, interpretation, writing–review and editing.

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Correspondence to Fariba Safa.

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Fazehi, F., Safa, F. Atomic-level AI topological indices as efficient descriptors for developing predictive QSPR models for flash points of acyclic alkanes. J Therm Anal Calorim 148, 2129–2138 (2023). https://doi.org/10.1007/s10973-022-11859-7

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