New structure-based models using mixture descriptors to predict the flammabiliy temperature of binary organic mixtures

Subject Areas : Modeling

Zohreh Faramarzi ¹ , Fatemeh Abbasitabar ² , Vahid Zare-Shahabadi ³

1 - Institute of Mining, Oil and Energy, Mahs.C., Islamic Azad University, Mahshahr, Iran.
2 - Department of Chemistry, Marv. C., Islamic Azad University, Marvdasht, Iran.
3 - Institute of Mining, Oil and Energy, Mahs.C., Islamic Azad University, Mahshahr, Iran.

Received: 2025-07-08 Accepted : 2025-08-14 Published : 2025-09-14

Keywords: QSPR, Flammability point, Memorized-Ant Colony optimization, Mixture descriptors, Multiple linear regression.,

Abstract :

Due to the importance of binary mixtures and their wide application in industry, it is important to estimate their flammability temperature. Therefore, in this study, quantitative structure-property relationship(QSPR) models were developed to predict the flammability point of binary organic mixtures. The biggest challenge in QSPR studies of mixtures is the calculation of a numerical descriptor to describe the property of a mixture. In this study, a set of twenty-two formulas was used to calculate mixture descriptors from molecular descriptors of pure compounds. Memorized Ant Colony(Memorized-ACO) Algorithm along with multivariate linear regression(MLR) was used to select the best subset of descriptors that have a significant contribution to the flammability property. The model based on sqr−fmol−sum descriptor was selected as the best model. R² _training and R²_test of this model were 0.93 and 0.94, respectively. The mean absolute error(MAE) for the training and test datasets was 3.75 and 2.97, respectively. A group model was also obtained through a simple averaging strategy using the models generated by the best descriptors. This improved the statistical quality of the final QSPR model.

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