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Bioinformatics Advance Access published online on June 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm298
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Classification of Small Molecules by Two- and Three-Dimensional Decomposition Kernels

Alessio Ceroni , Fabrizio Costa and Paolo Frasconi

Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Università sdegli Studi di Firenze, Italy. http://www.dsi.unifi.it/neural/

To whom correspondence should be addressed. Fabrizio Costa, E-mail: p-f{at}dsi.unifi.it


  Abstract

Motivation: Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on two-dimensional (2D) representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit three-dimensional (3D) information.

Results: We introduce new ideas for building kernels on small molecules that can effectively use and combine 2D and 3D information. We tested these kernels in conjunction with support vector machines for binary classification on the 60 NCI cancer screening data sets, as well as on the NCI HIV data set. Our results show that 3D information leveraged by these kernels can consistently improve prediction accuracy in all data sets.

Availability: An implementation of the small molecule classifier is available from http://www.dsi.unifi.it/neural/src/3DDK

Associate Editor: Prof. Anna Tramontano

Received on February 19, 2007; revised on May 14, 2007; accepted on May 28, 2007

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