Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness
- 158 Downloads
The chemical space covered by compounds involved in metabolic reactions was compared with that of a random dataset of purchasable compounds by chemoinformatics techniques. The comparison was based on 3D structure, 2D structure, or descriptors of global properties, by means of self-organizing maps, random forests, and classification trees. The overlap between metabolites and non-metabolites was observed to be the least in the space defined by the global descriptors, the most discriminatory features being the number of OH groups, presence of aromatic systems, and molecular weight. Discrimination between the two datasets was achieved with accuracy up to 97%. Models were built to produce a metabolite-likeness parameter. A relationship between metabolite-likeness and ready biodegradability was observed.
KeywordsChemical diversity Chemoinformatics Computer chemistry Metabolism Neural networks
- CPG NN
Counterpropagation neural network
Japanese Ministry of International Trade and Industry
Radial distribution function
Unable to display preview. Download preview PDF.
S. Gupta acknowledges Fundação para a Ciência e Tecnologia (Lisbon, Portugal) for the postdoctoral grant SFRH/BPD/14475/2003 co-funded by the POCI 2010 EU program. Molecular Networks GmbH (Erlangen, Germany) is acknowledged for access to PETRA and CORINA software packages. The authors thank Dr Robert Boethling for assistance with the MITI data.
- 1.Kulkarni SA, Zhu J, Blechinger S (2005) In silico techniques for the study and prediction of xenobiotic metabolism: a review. Xenobiotica 35:955–973, and references thereinGoogle Scholar
- 6.Nobeli I, Ponstingl H, Krissinel EB, Thornton JM (2003) A structure-based anatomy of E. coli metabolome. J Mol Biol 334:697–719Google Scholar
- 10.Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Comput Sci 45:177–182Google Scholar
- 17.Cherkasov A (2006) Can ‹bacterial-metabolite-likeness’ model improve odds of ‹in silico’ antibiotic discovery? J Chem Inf Model 46:1214–1222Google Scholar
- 18.Kohonen T (1989) Self-organization and associative memory, 3rd edn. Springer, Berlin Heidelberg New YorkGoogle Scholar
- 20.Breiman L, Friedman JH, Olshen RA, Stone CJ (2000) Classification and regression trees. Chapman & Hall/CRC, Boca RatonGoogle Scholar
- 22.Organisation for Economic Co-operation and Development. http://www.oecd.org
- 24.National Institute of Technology and Evaluation, Japan. http://www.safe.nite.go.jp/english/db.html
- 25.CORINA software is available from Molecular Networks GmbH (Erlangen, Germany). http://www.mol-net.de
- 27.The JATOON applets are available at http://www.dq.fct.unl.pt/staff/jas/jatoon
- 29.R Development Core Team (2004) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.orgGoogle Scholar
- 30.Fortran original by Breiman L, Cutler A, R port by Liaw A, Wiener M (2004)Google Scholar
- 31.Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design, 2nd edn. Wiley-VCH, WeinheimGoogle Scholar