In new research published in Journal of Systems Chemistry, Sijbren Otto and colleagues have provided the first experimental approach towards molecular networks that can predict bioactivity based on an assessment of molecular similarity.
Molecular similarity is an important concept in drug discovery. Molecules that share certain features such as shape, structure or hydrogen bond donor/acceptor groups may have similar properties that make them common to a particular target. Assessment of molecular similarity has so far relied almost exclusively on computational approaches, but Dr Otto reasoned that a measure of similarity might be obtained by interrogating the molecules in solution experimentally.
In their study, the authors employed a dynamic molecular network containing a variety of potential receptors. The receptors were based on a range of dithiol building blocks that could combine through reversible disulfide links to form a trimeric or tetrameric macrocycle. Each unit also contained a carboxylic acid group to recognise the effector molecule, in this case a range of amines and ammonium ions featuring functional groups that are common in many drugs.
Previous work on dynamic combinatorial libraries has shown that such networks change their composition when an effector is introduced into the system. In this case, inspection of the clustered molecules revealed an ability for the network to discriminate relatively thin amines from more bulky ones.
“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” says Dr Otto. “However, there is still a long road ahead” he adds. “Many more such studies on different dynamic networks are needed and we are currently working towards this vision by using more networks that exhibit increased structural diversity”.