Systems chemistry: using thermodynamically controlled networks to assess molecular similarity

Background: The assessment of mol. similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an exptl. method for similarity assessment based on dynamic combinatorial chem. Results: In order to assess mol. similarity directly in soln., a dynamic mol. network was used in a two-step process. First, a clustering anal. was employed to det. the network's innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic mol. network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related mols. After training, it was also able to classify unknown mols. based on the presence or absence of an ethylamine group. Conclusions: This is the first step in the development of mol. networks capable of predicting bioactivity based on an assessment of mol. similarity. [on SciFinder(R)