The Field Based Chemistry Revolution
It has long been known that small molecule drugs are recognized by and bind to proteins on the basis of their 3D electronic and shape properties, yet the drug discovery cycle has traditional described and protected 2D structures. Cresset is using field point descriptions of molecules to close the gap between chemistry and biology, bringing the features that are recognized by proteins to the desktop of our customers. Using fields, structurally diverse yet biologically similar bioisosteres appear identical, enabling the rapid change of chemical series, evaluation of patent landscapes and off-target effects.
Changing the Way You Think and Work with Small Molecules
Above left: 2D structures of structurally diverse bioisosteres both active at PDE3, cAMP (the natural substrate) and SKF93741, a PDE3 inhibitor.
Above right: The field patterns of the compounds reveal that they are biologically identical and share the same activity.
Making Fields Work: The Cresset Advantage
Fields rely on an accurate and detailed description of atoms in molecules. Traditional technology uses simplified descriptions of atoms and molecules that are not accurate enough to provide high quality field patterns. Cresset uses fields invented by Dr Andy Vinter while working at Cambridge University and as a consultant to the pharmaceutical industry and then developed by Cresset. Their method relies on Andy’s XED models of atoms and molecules, which present a more complex, accurate description of the charge around any atom. Using the XED model of atoms the field points around even simple molecules such as acetone (below) become richer and more informative, here showing the lone pairs of electrons that are present on the oxygen atom.
Above left: The incorrect field pattern on acetone generated with a traditional approach.
Above right: The correct acetone field pattern generated using Cresset’s XED model of atoms.
The XED model of atoms and molecules gives improved field patterns but also gives a significantly improved description of intermolecular interactions. For example the interaction of aromatic groups, a common interaction in protein-ligand complexes is correctly predicted by the XED model to prefer an edge to face arrangement (below right) where traditional technology incorrectly suggests that a face to face arrangement would be preferred (below left). The net effect is to more accurately model protein-ligand complexes leading to better understanding of activity or off-target effects.
Above left: Benzene-benzene interactions as predicted using traditional technologies incorrectly suggest face-to-face interactions.
Above right: Benzene-benzene interactions correctly predicted as edge to face using the XED model.
Cresset’s academic collaborators have used the XED model to study and predict specific intermolecular interactions. For example Professor Chris Hunter studied the dimerization of a series of di-aryl amides (below left) using NMR and computational chemistry. He found that the XED model accurately predicted the experimentally observed association constants (below right).