This webinar was presented by Hyesu Jang, Scientific Software Developer II at OpenEye. Hyesu received her Ph.D. degree in Chemistry in 2021 at the University of California, Davis, where she studied quantum chemical studies of electrochemical catalysts and electrostatic potential-based charge model development under Lee-Ping Wang.
About this session
Accurate prediction of binding modes is a core prerequisite in structure-based drug design. High accuracy can be achieved relatively easily in lead optimization, where most molecules of interest have high similarity to a known crystallographic ligand. However, in hit-to-lead, multiple chemotypes are often examined, many of which are dissimilar to ligands with known binding modes, reducing the reliability and accuracy of pose prediction.
We have recently implemented Induced-Fit Posing (IFP) in Orion®, to improve pose prediction accuracy in hit-to-lead. Based on OEDocking, OpenEye’s well-validated and fast molecular docking tool, IFP utilizes short trajectory molecular dynamics simulations after docking to better model protein flexibility during induced-fit.
We demonstrated the feasibility of the IFP protocol by performing retrospective cross-docking studies across diverse protein targets, showing substantially improved accuracy over the standard methods in the OEDocking suite.
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