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Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease

Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease

Matheus P. Freitas, Mariene H. Duarte
Copyright: © 2015 |Pages: 39
ISBN13: 9781466681361|ISBN10: 1466681365|EISBN13: 9781466681378
DOI: 10.4018/978-1-4666-8136-1.ch003
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MLA

Freitas, Matheus P., and Mariene H. Duarte. "Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease." Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, edited by Kunal Roy, IGI Global, 2015, pp. 84-122. https://doi.org/10.4018/978-1-4666-8136-1.ch003

APA

Freitas, M. P. & Duarte, M. H. (2015). Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease. In K. Roy (Ed.), Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment (pp. 84-122). IGI Global. https://doi.org/10.4018/978-1-4666-8136-1.ch003

Chicago

Freitas, Matheus P., and Mariene H. Duarte. "Evolution of Multivariate Image Analysis in QSAR: The Case for a Neglected Disease." In Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, edited by Kunal Roy, 84-122. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8136-1.ch003

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Abstract

Multivariate Image Analysis applied in Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple method to achieve, at least in a variety of examples, QSAR models with predictive abilities comparable to those of sophisticated tridimensional methodologies. MIA-QSAR is based on the correlation between properties (e.g. biological activities) and chemical descriptors, which are pixels of images representing chemical structures in a congeneric series of molecules. The MIA-QSAR approach has been improved since its creation, in 2005, both in terms of data analysis and development of more descriptive information. This chapter reports the MIA-QSAR method, including its augmented version, named aug-MIA-QSAR because of the introduction of new dimensions to better encode atomic properties. In addition, the application to a case study illustrates the main practical differences between traditional and augmented MIA-QSAR. The use of a neglected disease as example represents a challenge in QSAR, which is particularly focused on diseases with higher economical appearance.

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