Aim and Objective: Cycloalkanes have been largely used in the field of medicine, components of food, pharmaceutical drugs, and they are mainly used to produce fuel.

In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°_f), the standard enthalpies of fusion (∆H°_fus), and the standard Gibbs free energy of formation (∆G°_f)of the cycloalkanes is represented.

Materials and Methods: The Genetic Algorithm (GA) and multiple linear regressions (MLR) were successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular descriptors were obtained with the Dragon program. The Genetic algorithm and backward method were used to reduce and select suitable descriptors.

Results: QSPR models were used to delineate the important descriptors responsible for the properties of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF), Pearson Correlation Coefficient (PCC) and the Durbin–Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The statistical parameters of the training, and test sets for GA–MLR models were calculated.

Conclusion: The results of the present study indicate that the predictive ability of the models was satisfactory and molecular descriptors such as: the Functional group counts, Topological indices, GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising route for developing highly correlated QSPR models for prediction the studied properties.

目的和目的：环烷烃已广泛用于医药，食品成分，药物领域，主要用于生产燃料。

在目前的研究中，分子描述子与热力学性质之间的关系包括标准的形成焓（ΔH° _f），标准的融合焓（ΔH° _fus）和标准的吉布斯自由形成能（ΔG° _f）。代表了）。

材料和方法：遗传算法（GA）和多元线性回归（MLR）已成功用于预测环烷烃的热力学性质。Dragon程序获得了大量的分子描述符。使用遗传算法和后向方法来减少和选择合适的描述符。

结果：QSPR模型用于描述负责所研究的环烷烃性质的重要描述符。通过计算方差通货膨胀系数（VIF），皮尔逊相关系数（PCC）和Durbin-Watson（DW）统计数据，测试了模型中描述符的多重共线性和自相关属性。使用留一法交叉验证（LOOCV）和测试集验证方法讨论了MLR模型的预测能力。计算了训练的统计参数以及GA–MLR模型的测试集。

结论：本研究结果表明，该模型的预测能力令人满意，分子描述符如：官能团数，拓扑指数，GETAWAY描述符，结构指数和分子性质为开发高度相关的QSPR提供了有希望的途径用于预测研究特性的模型。