Abstract
This review describes an overview of multivariate QSAR methods, from classical analysis to 3D approaches and new perspectives. Data exploration, multivariate regression and molecular descriptors are some topics also appraised here. Special emphasis is given to a recently developed 2D image-based approach, known as MIA-QSAR, which is an improved method in many aspects, namely computing cost, simplicity and prediction performance. Remarks on the MIA-QSAR technique, numerical examples and comparison with traditional methodologies, in addition to a description of limitations and potentialities of this method, are also discussed.
Keywords: Descriptors, ligand approach, MIA-QSAR, multivariate QSAR
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