INTRODUCTION Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. METHODS A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. RESULTS The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One-Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2, Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. CONCLUSION The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

中文翻译：

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用于预测烯烃沸点和汽化焓的定量结构-性能关系研究。

引言 定量结构-性质关系 (QSPR) 模型已被广泛开发以推导分子的化学结构与其已知性质之间的相关性。在这项研究中，QSPR 模型已用于 91 种烯烃，以开发一个稳健的模型，用于预测标准条件 (ΔH°vap/kJ.mol-1) 和常温沸点 (T˚bp/ K) 烯烃。方法 随机选择 81 种结构多样的烯烃训练集，用于构建 QSPR 模型。这些模型使用后向多元线性回归 (MLR) 分析进行了优化。使用遗传算法和多元线性回归分析（GA-MLR）从Dragon软件中选择合适的描述符。结果测试了QSPR模型中贡献的描述符的多重共线性特性，并使用了几种方法来测试预测模型的能力，例如留一法（LOO）交叉验证（Q2 LOO），五折交叉验证技术、外部验证参数（Q2F1、Q2F2、Q2F3）、一致性相关系数（CCC）和预测参数 R2 m。结论模型的预测能力令人满意，三个块中的五个描述符，即连通性、边缘邻接指数和基于二维矩阵的描述符可用于预测烯烃的上述性质。外部验证参数（Q2F1、Q2F2、Q2F3）、一致性相关系数（CCC）和预测参数 R2 m。结论模型的预测能力令人满意，三个块中的五个描述符，即连通性、边缘邻接指数和基于二维矩阵的描述符可用于预测烯烃的上述性质。外部验证参数（Q2F1、Q2F2、Q2F3）、一致性相关系数（CCC）和预测参数 R2 m。结论模型的预测能力令人满意，三个块中的五个描述符，即连通性、边缘邻接指数和基于二维矩阵的描述符可用于预测烯烃的上述性质。