A novel knowledge based conformation sampling algorithm and applications in drug discovery

Abstract

Computational approaches have become important tools in drug discovery. Computational technologies have been developed for application in all aspects of the drug discovery process including target identification, lead compound discovery, and lead compound optimization. Interactions of drugs with a protein targets depend on the ability to adopt a three-dimensional structure that is complementary. Complete and rapid prediction of conformational space is important for the success of computational drug discovery technologies. A novel knowledge based conformation sampling algorithm was implemented which derives a database of frequently sampled small molecule fragments within the Cambridge Structure Database and the Protein Data Bank. Likely fragment conformations or ‘rotamers’ are used for rapid sampling of molecular conformational space. The ‘rotamer’ approach has allowed integration of the algorithm into computational biology programs like ROSETTA and FOLDIT, the online science game. Computational methods like homology modelling, docking and virtual high throughput screening were applied in the discovery of novel inhibitors of Discoidin Domain Receptor kinase domain which led to the identification of at least two novel scaffolds.