Abstract

In the present study, quantitative structure-property relationship (QSPR) models were used for modeling and predicting retention index (RI) values of chemical composition in Polygonum minus essential oil. In gas chromatography, RIs are used to convert retention times into system-independent constants. The dataset of 40 molecules was divided into training and external validation sets. A set of molecular descriptors were calculated from the optimized structures of the molecules using Gaussian 09 and Dragon software. The genetic algorithm (GA), backward stepwise multiple linear regression (BW-MLR), and back-propagation artificial neural networks (BPANN) were used to obtain suitable QSPR models. The predictive power of the QSPR model was discussed using a coefficient of determination (R²), average absolute deviation (AAD), mean squared error (MSE), and leave-one-out cross-validation (Q²cv). According to our findings, it was concluded that the QSPR model with three descriptors (ATS5m, BEHm4, and G1) established by the GA-BPANN method could be efficiently used for predicting RI of chemical components in P. minus essential oil and may be helpful for modeling and designing of new molecules of the essential oils.

摘要

在本研究中，定量结构-性质关系 (QSPR) 模型用于建模和预测何首乌减去精油中化学成分的保留指数 (RI) 值。在气相色谱中，RI 用于将保留时间转换为与系统无关的常数。40 个分子的数据集分为训练集和外部验证集。使用 Gaussian 09 和 Dragon 软件根据分子的优化结构计算一组分子描述符。遗传算法 (GA)、后向逐步多元线性回归 (BW-MLR) 和反向传播人工神经网络 (BPANN) 用于获得合适的 QSPR 模型。使用决定系数 (R ²)、平均绝对偏差 (AAD)、均方误差 (MSE) 和留一法交叉验证 (Q ² cv)。根据我们的研究结果，结论是通过 GA-BPANN 方法建立的具有三个描述符（ATS5m、BEHm4 和 G1）的 QSPR 模型可以有效地用于预测P. 减去精油中化学成分的 RI，并且可能会有所帮助用于精油新分子的建模和设计。