Multivariate QSAR: From Classical Descriptors to New Perspectives

Matheus   P.   Freitas

doi:10.2174/157340907782799408

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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Current Computer-Aided Drug Design

Title: Multivariate QSAR: From Classical Descriptors to New Perspectives

Volume: 3 Issue: 4

Author(s): Matheus P. Freitas

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Keywords: Descriptors, ligand approach, MIA-QSAR, multivariate QSAR

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.

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Cite this article as:

Freitas P. Matheus, Multivariate QSAR: From Classical Descriptors to New Perspectives, Current Computer-Aided Drug Design 2007; 3 (4) . https://dx.doi.org/10.2174/157340907782799408

DOI https://dx.doi.org/10.2174/157340907782799408	Print ISSN 1573-4099
Publisher Name Bentham Science Publisher	Online ISSN 1875-6697

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