Abstract
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.
Highlights
The assessment of molecular similarity is a key step in the drug discovery process that has far relied almost exclusively on computational approaches
We have shown how a simple molecular network can perform a rudimentary assessment of molecular similarity and can successfully classify unknowns
To the best of our knowledge this is the first experimental approach to assess molecular similarity and these results represent the first step towards developing networks that may be able to discriminate and assess similarity of biologically active molecules and drugs and potentially predict bioactivity
Summary
The assessment of molecular similarity is a key step in the drug discovery process that has far relied almost exclusively on computational approaches. Molecular similarity relates to the extent to which molecules have similar structures or properties. Molecular similarity and any quantification of it are both strongly context dependent. Assessing molecular similarity is a key element in the drug discovery process as structural similarity is believed to be correlated to activity with respect to a given target [1,2,3,4]. The most common approaches involve computational methods, including the use of molecular fingerprints [2], simple calculated properties such as solvent accessible surface area, number of hydrogen-bond donor and acceptor groups, etc. Three-dimensional methods, such as CoMFA [11] and CoMSIA [12], map favorable and unfavorable interaction regions around or onto the structure of a molecule, requiring prior knowledge of the appropriate conformations of this molecule
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