Improvement of QSAR of Flavonoids by Using Autocorrelation Functions Weighted by Different Atomic Properties

Abstract

Flavonoid derivatives are very important class of bioactive compounds often used in drug design. Several quantitative structure-activity studies for 104 flavonoid derivatives and their inhibition of p56lck Protein Tyrosine Kinase (PTK) were performed using different classes of molecular descriptors. However, published models are not of high accuracies – the best model achieves r = 0.81 (correlation coefficient) and s = 0.43 (standard error of estimate) for the complete data set (104 flavonoids). In our recent study old results were outperformed by using novel sets of molecular descriptors computed by (1) the DRAGON 4.0 program and (2) the CODESSA 2.21 program. Somewhat better model we obtained using four DRAGON descriptors r = 0.85, s = 0.38, and scv = 0.39 (leave-one-out cross-validated standard error of estimate). In this study we present further improvement of models by using molecular descriptors that are based on autocorrelation functions weighted by different atomic properties that were computed by the ADRIANA.code program. Data set was encoded as SMILES and converted to 3D structures (SD files) by the CORINA program (www2.chemie.uni-erlangen.de/software/corina/), and more than 700 descriptors were calculated by the ADRIANA.code program. The selection of the most significant molecular descriptors into Multivariate Linear Regression (MLR) models containing 1-3 descriptors were performed by the CROMRsel program. The best model containing three descriptors had s = 0.29 and scv = 0.31 for 104 molecules. The best model contains 2D autocorrelation descriptor of order 11 weighted by the sigma atom charge, and two 3D autocorrelation functions of orders 9 and 4 weighted by the total charge, and sigma electronegativity, respectively. This is promising result showing that in data sets of molecules that have large common portion of their structures, descriptors based on autocorrelation are most significant ones.