Automated docking methods have not yet fulfilled their potential to dramatically reduce drug discovery time and costs through virtual screening of synthetically available chemical space or improve drug safety through polypharmacology prediction.  Further, docking scoring methodologies remain limited in their ability to rank compounds, even congeneric series, accurately.   The Drug Design Data Resource (D3R, drugdesigndata.org) is an NIH funded initiative to present previously unpublished datasets to the computer-aided drug discovery community as blinded challenges for benchmarking docking methods and workflows.  Lessons learned from our first blinded Grand Challenge 2015 will be presented and discussed.  Additionally, we will introduce our Continuous Evaluation of Ligand - Pose Predictions (CELPP), a weekly community challenge for automated docking servers that makes use of pre-released International Chemical Identifier strings and protein polymer sequences by the World Wide Protein Data Bank.

Drug design data resource: Leveraging blinded datasets for improved docking methodologies and workflows

251st National Meeting (2016)

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