Skip Navigation

Bioinformatics 2005 21(Suppl 1):i359-i368; doi:10.1093/bioinformatics/bti1055
This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Swamidass, S. J.
Right arrow Articles by Baldi, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Swamidass, S. J.
Right arrow Articles by Baldi, P.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity

S. Joshua Swamidass {dagger}, Jonathan Chen {dagger}, Jocelyne Bruand , Peter Phung , Liva Ralaivola and Pierre Baldi *

Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA

*To whom correspondence should be addressed.

Motivation: Small molecules play a fundamental role in organic chemistry and biology. They can be used to probe biological systems and to discover new drugs and other useful compounds. As increasing numbers of large datasets of small molecules become available, it is necessary to develop computational methods that can deal with molecules of variable size and structure and predict their physical, chemical and biological properties.

Results: Here we develop several new classes of kernels for small molecules using their 1D, 2D and 3D representations. In 1D, we consider string kernels based on SMILES strings. In 2D, we introduce several similarity kernels based on conventional or generalized fingerprints. Generalized fingerprints are derived by counting in different ways subpaths contained in the graph of bonds, using depth-first searches. In 3D, we consider similarity measures between histograms of pairwise distances between atom classes. These kernels can be computed efficiently and are applied to problems of classification and prediction of mutagenicity, toxicity and anti-cancer activity on three publicly available datasets. The results derived using cross-validation methods are state-of-the-art. Tradeoffs between various kernels are briefly discussed.

Availability: Datasets available from http://www.igb.uci.edu/servers/servers.html

Contact: pfbaldi{at}ics.uci.edu


Received on January 15, 2005; accepted on March 27, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
J.-L. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra
Genome scale enzyme metabolite and drug target interaction predictions using the signature molecular descriptor
Bioinformatics, January 15, 2008; 24(2): 225 - 233.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.