Aug-MIA-QSAR based strategy in bioactivity prediction of a series of flavonoid derivatives as HIV-1 inhibitors

Abstract

Multivariate image analysis-quantitative structure-activity relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of compounds based on two-dimensional image analysis. Aug-MIA-QSAR is a modified version of multivariate image analysis, where the atoms in 2D chemical structures were augmented (labelled by assigning specific colours). This study focuses on efficiently constructing such prediction models using a dataset of flavonoid derivatives possessing human immunodeficiency virus - 1 inhibition. The models were constructed by partial least square regression using non-linear iterative partial least square (NIPALS) algorithm and linearized by identifying an optimum number of seven latent variables. A leave-one-out cross validation (LOOCV) helped to verify the actual and predicted data. The two multivariate methods were compared and analysed to identify the most suitable method.

Keywords: ANN; Aug-MIA-QSAR; MIA-QSAR; NIPALS; PLS.