SARS-CoV2 billion-compound docking

Rogers, David M.; Agarwal, Rupesh; Vermaas, Josh V.; Smith, Micholas Dean; Tatikonda, Rajitha Rajeshwar; Cooper, Connor; Sedova, Ada; Boehm, Swen; Baker, Matthew; Glaser, Jens; Smith, Jeremy C.

doi:10.1038/s41597-023-01984-9

Title: SARS-CoV2 billion-compound docking

Journal Article · 28 March 2023 · Scientific Data

DOI: https://doi.org/10.1038/s41597-023-01984-9 · OSTI ID:1963757

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^[3]; Smith, Micholas Dean ^[2]; Tatikonda, Rajitha Rajeshwar ^[2]; Cooper, Connor ^[1];

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^[1]; Baker, Matthew ^[1];

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Michigan State Univ., East Lansing, MI (United States)

This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1963757

Alternate ID(s):: OSTI ID: 1991742

Journal Information:: Scientific Data, Vol. 10, Issue 1; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Title: SARS-CoV2 billion-compound docking

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References (49)

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