Introducing new dimensions in MIA-QSAR: a case for chemokine receptor inhibitors

Abstract

Multivariate image analysis applied to quantitative structureeactivity relationships (MIA-QSAR) is a very simple correlative method that uses pixels (binaries) of chemical structures built from 2D viewer pro- grams as descriptors; structural changes correspond to different pixel coordinates, which explain the variance in the bioactivities block. The MIA-QSAR method has shown to be predictive and capable of encoding some chemical information, but introduction of more descriptive information, such as atom size and colors to differentiate atom types, would improve predictability and interpretability. The bio- activities of a series of chemokine receptor (CCR2) inhibitors have been modeled using both conventional and atom color/size-dependent MIA-QSAR (namely aug-MIA-QSAR); the latter showed to be better. Moreover, the results were comparable to those obtained by 3D methodologies, indicating that 2D shape and substituent size are more significant descriptors than the conformational profiles required by field fit techniques.