O. Obrezanova and M. D. Segall, J. Chem. Inf. Model., 2010, 50 (6), pp 1053–1061.
DOI: 10.1021/ci900406x

Abstract

In this article, we extend the application of the Gaussian processes technique to classification quantitative structure−activity relationship modeling problems. We explore two approaches, an intrinsic Gaussian processes classification technique and a probit treatment of the Gaussian processes regression method. Here, we describe the basic concepts of the methods and apply these techniques to building category models of absorption, distribution, metabolism, excretion, toxicity and target activity data. We also compare the performance of Gaussian processes for classification to other known computational methods, namely decision trees, random forest, support vector machines, and probit partial least squares. The results indicate that, while no method consistently generates the best model, the Gaussian processes classifier often produces more predictive models than those of the random forest or support vector machines and was rarely significantly outperformed.

You can view the final published version on the journal webpage via the button below. Alternatively, download an e-print of this article on Gaussian processes via the ACS “Articles on Request” service using the following link:

http://pubs.acs.org/articlesonrequest/AOR-e9ivq4kdydbtNGCKt9Nw

Please note: To access the Articles on Request link, please log in to the Publications website using your ACS ID. If you do not have an ACS ID, you will need to Register for one for free by clicking on ‘Register’ near the top right corner of the website.