Quantitative structure–activity relationship analysis of human neutrophil elastase inhibitors using shuffling classification and regression trees and adaptive neuro-fuzzy inference systems

Abstract

The purpose of this study was to develop quantitative structure-activity relationship models for N-benzoylindazole derivatives as inhibitors of human neutrophil elastase. These models were developed with the aid of classification and regression trees (CART) and an adaptive neuro-fuzzy inference system (ANFIS) combined with a shuffling cross-validation technique using interpretable descriptors. More than one hundred meaningful descriptors, representing various structural characteristics for all 51 N-benzoylindazole derivatives in the data set, were calculated and used as the original variables for shuffling CART modelling. Five descriptors of average Wiener index, Kier benzene-likeliness index, subpolarity parameter, average shape profile index of order 2 and folding degree index selected by the shuffling CART technique have been used as inputs of the ANFIS for prediction of inhibition behaviour of N-benzoylindazole derivatives. The results of the developed shuffling CART-ANFIS model compared to other techniques, such as genetic algorithm (GA)-partial least square (PLS)-ANFIS and stepwise multiple linear regression (MLR)-ANFIS, are promising and descriptive. The satisfactory results r2p = 0.845, Q2(LOO) = 0.861, r2(L25%O) = 0.829, RMSE(LOO) = 0.305 and RMSE(L25%O) = 0.336) demonstrate that shuffling CART-ANFIS models present the relationship between human neutrophil elastase inhibitor activity and molecular descriptors, and they yield predictions in excellent agreement with the experimental values.

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

“…24 These interactions include directional force, induction force, dispersion force, and hydrogen bond and can be related to the topological structures as well as the geometric and electronic features of the analyte. In our MLR models descriptors, the highest positive weights were the AlogP, the average valence connectivity index chi-1 (AvX1), 25 the Kier benzene-likeliness index (KBLI), 26 whereas the highest negative weights were the HDB (number of hydrogen-bond donors) index (see the Supporting Information, MLR models). Quite unsurprisingly, given the reversed-phase separation mode, the AlogP in each model was shown to represent an important contribution to the final equation.…”