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Open Access Highly Accessed Research article

Systems chemistry: using thermodynamically controlled networks to assess molecular similarity

Vittorio Saggiomo1, Yana R Hristova2, R Frederick Ludlow23 and Sijbren Otto1*

  • * Corresponding author: Sijbren Otto s.otto@rug.nl

  • † Equal contributors

Author Affiliations

1 Centre for Systems Chemistry, Stratingh Institute, University of Groningen, Nijenborgh 4, 9747 AG, Groningen, The Netherlands

2 Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom

3 Present address: Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, CB4 0QA, United Kingdom

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Journal of Systems Chemistry 2013, 4:2  doi:10.1186/1759-2208-4-2

Published: 12 February 2013

Abstract

Background

The assessment of molecular similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an experimental method for similarity assessment based on dynamic combinatorial chemistry.

Results

In order to assess molecular similarity directly in solution, a dynamic molecular network was used in a two-step process. First, a clustering analysis was employed to determine the network’s innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic molecular network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related molecules. After training, it was also able to classify unknown molecules based on the presence or absence of an ethylamine group.

Conclusions

This is the first step in the development of molecular networks capable of predicting bioactivity based on an assessment of molecular similarity.

Keywords:
Dynamic combinatorial chemistry; Systems chemistry; Molecular networks; Data mining; Clustering analysis

Graphical abstract