PREDICTING ESTROGEN ACTIVITIES OF BISPHENOL A AND ITS ANALOGS USING QUANTUM CHEMISTRY CALCULATIONS AND ARTIFICIAL NEURAL NETWORKS
| Autor: | Le Kim Long, Doan Van Phuc, Nguyen Hoang Trang, Nguyen Van Trang, Vu Van Dat, Nguyen Thi Thu Ha |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENII KHIMIYA KHIMICHESKAYA TEKHNOLOGIYA. 62:31-37 |
| ISSN: | 2500-3070 0579-2991 |
| DOI: | 10.6060/ivkkt.20196205.5933 |
| Popis: | This article presents the results of the quantitative structure – activity relationship (QSAR) study of bisphenol A (BPA) and its analogs using quantum chemistry calculations and method of artificial neural networks (ANN). Molecular structural analysis is performed using Density Functional Theory (DFT) at the B3LYP/6-31+G(d) level. The quantum calculations focus on finding the optimized molecular structures, vibrational frequencies, the molecular orbital energies with reasonable accuracy. The study of electron density distribution was carried out in the framework of the natural bond orbital (NBO) methods. The obtained parameters and known observable estrogen activities are used as input data for constructing the QSAR model, using the artificial neural network method. Based on the artificial neural network method the quantum parameters having the strongest impact on the estrogen activity of the compounds were revealed. The internal and external validation methods have been performed to test the performance and the stability of the model. The statistical parameters obtained of the QSAR model were: R2 = 0.99; Q2LOO = 0.98; R2Predict = 0.98. According to the obtained results, our proposed model, constructing by method of artificial neural network using the parameters of quantum chemistry is adequate and may be useful to predict of estrogen activities for unexplored derivatives and BPA analogs with moderate reliability. |
| Databáze: | OpenAIRE |
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