Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors

In this work, the inhibitory activity of pyridine N-oxide derivatives against human severe acute respiratory syndrome (SARS) is predicted in terms of quantitative structure–activity relationship (QSAR) models. These models were developed with the aid of multivariate adaptive regression spline (MARS) and adaptive neuro-fuzzy inference system (ANFIS) combined with shuffling cross-validation technique. A shuffling MARS algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict SARS inhibitory activities of pyridine N-oxide derivatives. A data set of 119 drug-like compounds was coded with over hundred calculated meaningful molecular descriptors. The best descriptors describing the inhibition mechanism were solvation connectivity index, length to breadth ratio, relative negative charge, harmonic oscillator of aromatic index, average molecular weight and total path count. These parameters are among topological, electronic, geometric, constitutional and aromaticity descriptors. The statistical parameters of R(2) and root mean square error (RMSE) are 0.884 and 0.359, respectively. The accuracy and robustness of shuffling MARS–ANFIS model in predicting inhibition behavior of pyridine N-oxide derivatives (pIC(50)) was illustrated using leave-one-out and leave-multiple-out cross-validation techniques and also by Y-randomization. Comparison of the results of the proposed model with those of GA-PLS-ANFIS shows that the shuffling MARS–ANFIS model is superior and can be considered as a tool for predicting the inhibitory behavior of SARS drug-like molecules.