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

Multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) is a very simple correlative method that uses pixels (binaries) of chemical structures built from 2D viewer programs 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 bioactivities 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.

An atom size/color-dependent QSAR method based on multivariate image analysis has been implemented and showed to be more predictive than traditional MIA-QSAR.Figure optionsDownload as PowerPoint slideHighlights
► The MIA-QSAR was improved by adding atom color and size-dependent descriptors.
► The bioactivities of chemokine receptor (CCR2) inhibitors were modeled using aug-MIA-QSAR.
► 2D shape and substituent sizes are more important than 3D requirements.