Critical Temperatures Prediction of Organic Compounds (Aliphatic Alkanes) Using QSPR Approach

B. Souyei, N. Kertiou, A. Hadj Seyd, Y. Labbi, A. Khechekhouche

Abstract


The quantitative structure-property relationship QSPR method using Multiple Linear Regression MLR and Partial Least Squares PLS methodologies was performed for 160 organic compounds (hydrocarbons, branched alkanes, branched and unbranched alkenes, and alkynes). The MLR and PLS methods were employed to explore the correlation   between the molecular descriptors which are the structural representation while the critical temperature Tc is the property representation. Using Dragon descriptors, this study was aimed at developing a predictive and robust QSPR models for predicting Tc. According to the squared correlation coefficients (R2 =0.942 and 0.941), standard error (s =0.88 and 0.797) and the leave-one-out cross-validation correlation coefficients (Q2Loo = 0.834 and 0.932), for the MLR and PLS methods respectively, the results demonstrated almost identical qualities and good predictive ability for both the MLR and PLS models.

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