Bioinformatics Advance Access originally published online on June 5, 2007
Bioinformatics 2007 23(16):2038-2045; doi:10.1093/bioinformatics/btm298
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Classification of small molecules by two- and three-dimensional decomposition kernels
Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 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 datasets 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 datasets.
Availability: An implementation of the small molecule classifier is available from http://www.dsi.unifi.it/neural/src/3DDK
Contact: costa{at}dsi.unifi.it
Associate Editor: Anna Tramontano
Received on February 19, 2007; revised on May 14, 2007; accepted on May 28, 2007
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