Protein-Ligand Docking Surrogate Models: SARS-CoV-2 Benchmark for Deep LearningAccelerated Virtual Screening

see https://2019-ncovgroup.github.io/HTDockingDataRepo/<br>This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.<br>We acknowledge other members of the National Virtual Biotechnology Laboratory (NVBL) Medical Therapeutics group. We acknowledge computing time via the COVID19 HPC Consortium.<br>Research was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act.<br>\textit{Scientific and Technical Information Only For All Information.}\\Unless otherwise indicated, this information has been authored by an employee or employees of UChicago Argonne, LLC., operator of Argonne National Laboratory, with the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this information. The public may copy and use this information without charge, provided that this Notice and any statement of authorship are reproduced on all copies.While every effort has been made to produce valid data, by using this data, User acknowledges that neither the Government nor operating contractors of the above national laboratories makes any warranty, express or implied, of either the accuracy or completeness of this information or assumes any liability or responsibility for the use of this information. Additionally, this information is provided solely for research purposes and is not provided for purposes of offering medical advice. Accordingly, the U.S. Government and operating contractors of the above national laboratories are not to be liable to any user for any loss or damage, whether in contract, tort (including negligence), breach of statutory duty, or otherwise, even if foreseeable, arising under or in connection with use of or reliance on the content displayed in this report.