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Title: SARS-CoV2 Docking Dataset

Abstract

Description: Small-molecule conformations and docking scores for 1.4 billion molecules docked against 6 protein targets from SARS-CoV2: MPro 5R84, MPro 6WQF, NSP15 6WLC, PLPro 7JIR, Spike 6M0J, and a hand-optimized model of the RNA-dependent RNA polymerase. Docking was carried out using the Autodock-GPU program performing 20 independent structure minimizations per dock - saving 3 results per molecule. Scores reported include the Autodock free energy estimate as well as RF3 and VS-DUD-E v2 machine-learned rescoring models. Protein structure files and maps in the format input to Autodock-GPU are included. Literature Ref: Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19, J. Chem. Inf. Model. 2020, 60(12): 5832–5852.

Authors:
; ; ; ; ; ; ; ; ; ;
Publication Date:
DOE Contract Number:  
DE-AC05-00OR22725
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE; ORNL Laboratory Directed Research and Development (LDRD)
Collaborations:
50159092,50159094,50445429
Subject:
36 MATERIALS SCIENCE; 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES
Keywords:
COVID-19, SARS CoV-2, docking, small-molecules
OSTI Identifier:
1783186
DOI:
https://doi.org/10.13139/OLCF/1783186

Citation Formats

Rogers, David M., Glaser, Jens, Agarwal, Rupesh, Vermaas, Josh, Smith, Micholas, Parks, Jerry, Cooper, Connor, Sedova, Ada, Boehm, Swen, Baker, Matthew, and Smith, Jeremy. SARS-CoV2 Docking Dataset. United States: N. p., 2021. Web. doi:10.13139/OLCF/1783186.
Rogers, David M., Glaser, Jens, Agarwal, Rupesh, Vermaas, Josh, Smith, Micholas, Parks, Jerry, Cooper, Connor, Sedova, Ada, Boehm, Swen, Baker, Matthew, & Smith, Jeremy. SARS-CoV2 Docking Dataset. United States. doi:https://doi.org/10.13139/OLCF/1783186
Rogers, David M., Glaser, Jens, Agarwal, Rupesh, Vermaas, Josh, Smith, Micholas, Parks, Jerry, Cooper, Connor, Sedova, Ada, Boehm, Swen, Baker, Matthew, and Smith, Jeremy. 2021. "SARS-CoV2 Docking Dataset". United States. doi:https://doi.org/10.13139/OLCF/1783186. https://www.osti.gov/servlets/purl/1783186. Pub date:Thu May 27 00:00:00 EDT 2021
@article{osti_1783186,
title = {SARS-CoV2 Docking Dataset},
author = {Rogers, David M. and Glaser, Jens and Agarwal, Rupesh and Vermaas, Josh and Smith, Micholas and Parks, Jerry and Cooper, Connor and Sedova, Ada and Boehm, Swen and Baker, Matthew and Smith, Jeremy},
abstractNote = {Description: Small-molecule conformations and docking scores for 1.4 billion molecules docked against 6 protein targets from SARS-CoV2: MPro 5R84, MPro 6WQF, NSP15 6WLC, PLPro 7JIR, Spike 6M0J, and a hand-optimized model of the RNA-dependent RNA polymerase. Docking was carried out using the Autodock-GPU program performing 20 independent structure minimizations per dock - saving 3 results per molecule. Scores reported include the Autodock free energy estimate as well as RF3 and VS-DUD-E v2 machine-learned rescoring models. Protein structure files and maps in the format input to Autodock-GPU are included. Literature Ref: Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19, J. Chem. Inf. Model. 2020, 60(12): 5832–5852.},
doi = {10.13139/OLCF/1783186},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {5}
}