Abstract
A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.
Highlights
In quantitative structure-property relationship (QSPR) modeling, some mathematical and artificial intelligence methods are used to explore the chemical and physical properties of various substances.These methods, including mathematical statistics, machine learning methods, and artificial intelligence methods, can reflect the relationship between the activity and structure of compounds
In order to solve the problem of molecular descriptor selection and model establishment establishment in QSPR research, a novel chaos-enhanced accelerated particle swarm optimization in QSPR research, a novel chaos-enhanced accelerated particle swarm optimization algorithm (CAPSO)
The algorithm was applied in the selection of molecular descriptors and QSPR modeling, descriptors and QSPR modeling, and a prediction model called CAPSO back propagation artificial neural network (BP artificial neural network (ANN)) was obtained
Summary
In quantitative structure-property relationship (QSPR) modeling, some mathematical and artificial intelligence methods are used to explore the chemical and physical properties of various substances. Luan et al [33] developed a model with radial basis function artificial neural network (RBF ANN) and the heuristic method (HM) and obtained the better performance in pKa prediction. These studies showed that ANN has outstanding performance in pKa prediction. Combined with other artificial neural networks, the QSPR model is used to predict the pKa values of various compounds
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