Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors

Computer-aided drug design, an important component of
the early
stages of the drug discovery pipeline, routinely identifies large
numbers of false positive hits that are subsequently confirmed to
be experimentally inactive compounds. We have developed a methodology
to improve true positive prediction rates in structure-based drug
design and have successfully applied the protocol to twenty target
systems and identified the top three performing conformers for each
of the targets. Receptor performance was evaluated based on the area
under the curve of the receiver operating characteristic curve for
two independent sets of known actives. For a subset of five diverse
cancer-related disease targets, we validated our approach through
experimental testing of the top 50 compounds from a blind screening
of a small molecule library containing hundreds of thousands of compounds.
Our methods of receptor and compound selection resulted in the identification
of 22 novel inhibitors in the low μM–nM range, with the
most potent being an EGFR inhibitor with an IC50 value
of 7.96 nM. Additionally, for a subset of five independent target
systems, we demonstrated the utility of Gaussian accelerated molecular
dynamics to thoroughly explore a target system’s potential
energy surface and generate highly predictive receptor conformations.