碳环硝基芳香化合物熔点的QSPR和DFT研究

B. Elidrissi, A. Ousaa, M. Ghamali, Samir CHTITA, M. A. Ajana, M. Bouachrine, T. Lakhlifi
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引用次数: 4

摘要

利用ACD/ ChemSketch和Gaussian 03W程序分别计算电子描述符和拓扑描述符,对60种碳环硝基芳香族化合物的熔点进行了定量构效关系(QSPR)研究。在B3LYP/6-31G(d)理论水平上,利用杂化密度泛函理论(DFT)对60个化合物进行了结构优化。在这两种方法中,50种化合物被指定为训练集,其余的作为测试集。通过主成分分析(PCA)、后代多元线性回归(MLR)和人工神经网络(ANN)对这些化合物进行分析。通过留多交叉验证和测试集外部验证来评估模型的稳健性。本研究表明,PCA和MLR也可以用于预测熔点和其他一些物理化学性质,但与ANN给出的结果(R=0.997)相比,我们意识到后者的预测更有效,比其他模型要好得多。
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QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds
A quantitative structure-property relationship (QSPR) study was performed to predict the melting points of 60 carbocyclic nitroaromatic compounds using the electronic and topologic descriptors computed respectively, with ACD/ ChemSketch and Gaussian 03W programs. The structures of all 60 compounds were optimized using the hybrid density functional theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 50 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the principal components analysis (PCA) method, a descendant multiple linear regression (MLR) analyses and an artificial neural network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through test set. This study shows that the PCA and MLR have served also to predict melting point and some other physicochemical properties, but when compared with the results given by the ANN (R=0.997), we realized that the predictions fulfilled by this latter were more effective and much better than other models.
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