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Summary
In this study, the researchers look to solve classification quantitative structure−activity relationship (QSAR) modelling problems using Gaussian processes.
They cover two different approaches, describing how these methods work and applying them to build category models of target activity data and absorption, distribution, metabolism, excretion, toxicity (ADMET) properties. They compare this Gaussian processes method to other computational modelling methods, including decision trees, random forest, support vector machines, and probit partial least squares. Whilst none of the methods were consistently best, Gaussian processes often produced better models than random forest or support vector machines and was rarely significantly outperformed.
Citation details
O. Obrezanova and M. D. Segall, J. Chem. Inf. Model., 2010, 50 (6), pp 1053–1061.
DOI: 10.1021/ci900406x
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http://pubs.acs.org/articlesonrequest/AOR-e9ivq4kdydbtNGCKt9Nw
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