Computational neural network analysis of the affinity of 2-pyridyl-3,5-diaryl pyrroles analogs for the human glucagon receptor using density functional theory

Abstract

In our continuing efforts to provide a predictive quantitative structure activity relationship using different algorithms, radial basis function neural networks (RBFNN) have been successfully combined with principal component analysis (PCA) and trained to predict the biological activity (pIC50) of 2-pyridyl-3,5-diaryl pyrrole derivatives as human glucagon receptor antagonists. A set of quantum descriptors, including energy of HOMO, energy of LUMO, softness, hardness, etc. descriptors, were calculated using DFT-B3LYP method, with the basis set of 6-311G. An ANN with 1-15-1 architecture was generated using eight principal components. A principal component regression (PCR) model was also developed for comparison. It was found that a properly selected and trained RBFNN with a suitable training set could represent the dependence of the biological activity on the principal components that were calculated using quantum descriptors fairly well. For evaluation of the predictive ability of the developed PCA-based RBFNN model, an optimized network was applied to predict the pIC50s of compounds in the test set, which were not used in the modeling phase of the procedure. A squared correlation coefficient (R2) and root mean square error of 0.161 and 0.874 for the test set by the PCR model should be compared with the values of 0.999 and 0.0154 by the principal component based RBFNN model. These improvements are due to the fact that the pIC50s of 2-pyridyl-3,5-diaryl pyrrole derivatives show non-linear correlations with the principal component extracted from the quantum descriptors.