Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR

Abstract

Artificial neural networks (ANNs) have been successfully trained to model and predict the acidity constants (pK _a) of 128 various phenols with diverse chemical structures using a quantitative structure-activity relationship. An ANN with 6-14-1 architecture was generated using six molecular descriptors that appear in the multi-parameter linear regression (MLR) model. The polarizability term (π _I), most positive charge of acidic hydrogen atom (q ⁺), molecular weight (MW), most negative charge of the phenolic oxygen atom (q ⁻), the hydrogen-bond accepting ability (ɛ _B) and partial-charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pK _a. It was found that a properly selected and trained neural network with 106 phenols could represent the dependence of the acidity constant on molecular descriptors fairly well. For evaluation of the predictive power of the ANN, an optimized network was used to predict the pK _as of 22 compounds in the prediction set, which were not used in the optimization procedure. A squared correlation coefficient (R ²) and root mean square error (RMSE) of 0.8950 and 0.5621 for the prediction set by the MLR model should be compared with the values of 0.99996 and 0.0114 by the ANN model. These improvements are due to the fact that the pK _a of phenols shows non-linear correlations with the molecular descriptors. Figure Plot of the calculated values of pK _a from the ANN model versus the experimental values of it for training, validation and prediction sets.