Optimizing the Pharmacophore
Using a ligand-based approach, Peakdale Molecular (Chapel-en-le-Frith, U.K.) and De Novo Pharmaceuticals (Cambridge, U.K.) collaborated to develop targeted GPCR Peakexplorer libraries. These were derived from a variety of family-based extended pharmacophores for subfamilies of the GPCR class.
At the start of the project a sophisticated algorithm sampled the chemical space for each class of aminergic GPCR targets in turn, starting with the dopamine target, for example.
Using standard diversity analysis and clustering tools, the companies were able to identify ten discrete ligands to represent the different types of compounds that were known to be active against dopamine targets.
These ligands were then used to rapidly build up a set of over 3,000 pharmacophore models, which could be tested against a database of druglike compounds seeded with dopamine ligands.
The resulting pharmacophores were found to be feature-rich, identifying a significant number of known ligands from other druglike compounds. This gave Peakdale's chemists the confidence to use these models to aid in the design of new compounds targeted against aminergic GPCRs.
The pharmacophore models are also used by medicinal chemists to help understand how ligands bind to the proteins. In order to design new compounds chemists will look at small fragments of the compounds to see if they can be replaced by something else that still meets the requirements of binding.
Substituting the fragments allows the generation of novel scaffolds and extends the chemical space. This process can be facilitated by computers which can rapidly scan thousands of available fragments and can highlight those that can produce good binding.
De Novo's SkelGen (advanced in silico structure-generating platform) was used to design novel molecules. SkelGen operates with a database of more than 1,700 molecular fragments, each of which is coded with information about the kind of other fragment it can join to and what the resulting bond would be. This allows the program to select the fragments randomly and build them into druglike molecules.
Thousands of chemically feasible, virtual molecules were generated using SkelGen. These molecules were further filtered for suitable properties and ranked. This output was then optimized by Peakdale's medicinal chemists to define chemotypes suitable for generating new compounds for the GPCR-targeted libraries.