Application of genetic algorithms for pixel selection in multivariate image analysis for a QSAR study of trypanocidal activity for quinone compounds and design new quinone compounds

• Development of genetic algorithm for pixel selection.
• Multivariate image analysis for QSAR study.
• Modification of structure and their activity predicted.

Quantitative structure-activity relationship (QSAR) analysis has been directed to a series of 31 quinone compounds with trypanocidal activity that was performed by chemometrics methods. The trypanocidal activity of the quinones is related to their redox potential (Epcl). Bidimensional images were used to calculate some pixels. Multivariate image analysis was applied to QSAR modeling of the redox potential of quinones derivatives by means of multivariate calibration such as principal component regression (PCR) and partial least squares (PLS). In this paper we investigate the effect of pixel selection by application of genetic algorithms (GAs) for PLS model. GAs is very useful in the variable selection in modeling and calibration because of the strong effect of the relationship between presence/absence of variables in a calibration model and the prediction ability of the model itself. The subset of pixels, which resulted in the low prediction error, was selected by genetic algorithm. The resulted model showed high prediction ability with RMSEP of 0.0694, 0.0358 and 0.0059 for PCR, PLS and GA-PLS models, respectively. Furthermore, the proposed QSAR model with GA-PLS was used for modification of structure and their activity predicted.