Talk abstracts
Talk on Tuesday 09:45-10:00am submitted by Eric Hantz
Actives-Based Receptor Selection Strongly Increases Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Unique Potent Cancer Inhibitors
Eric R Hantz (Biophysics Graduate Program), Steffen Lindert (Chemistry and Biochemistry Department, Ohio State University)
Abstract:
The drug discovery pipeline is plagued with inefficiency and false positive hits that has led to the pursuit of many experimentally inactive compounds. We have developed a methodology, based on the knowledge of known actives, to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty unique target systems and identified a small ensemble of 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 the ChemBridge EXPRESS-Pick Collection using Glide SP. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the μM-nM range, with the most potent inhibitor having an IC50 value of 7.96 nM. Additionally for a subset of five independent target systems, we demonstrate the utility of Gaussian accelerated Molecular Dynamics (GaMD) to thoroughly explore a target system’s potential energy surface and generate highly predictive receptor conformations.
Keywords: Virtual Screening , GaMD, SBDD