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


BACKGROUND
Datasets available from http://www.igb.uci.edu/servers/servers.html
Swamidass
S Joshua
SJ
Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.
Chen
Jonathan
J
Bruand
Jocelyne
J
Phung
Peter
P
Ralaivola
Liva
L
Baldi
Pierre
P
D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D008297:Male; D051379:Mice; D008958:Models, Molecular; D015233:Models, Statistical; D009153:Mutagens; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D051381:Rats
D000970:Antineoplastic Agents; D009153:Mutagens