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D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

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Abstract

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) Grant 1U01GM111528 for the Drug Design Data Resource (D3R). We also thank OpenEye Scientific Software for generously donating the use of their software. We thank Prof. William Jorgensen (Yale) for providing valuable insight into the selected free energy sets. The RCSB PDB is jointly funded by the National Science Foundation (DBI-1832184), the National Institutes of Health (R01GM133198), and the United States Department of Energy (DE-SC0019749).The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the federal funding agencies. MKG has an equity interest in, and is a co-founder and scientific advisor of, VeraChem LLC; REA has equity interest in and is a co-founder and scientific advisor of Actavalon, Inc.; and PW has an equity interest in Relay Pharmaceuticals, Inc. We also thank the reviewers for their helpful suggestions.

Author information

Author notes

  1. Conor D. Parks and Zied Gaieb Shared first authorship.

Authors and Affiliations

  1. Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA

    Conor D. Parks, Zied Gaieb, Michael Chiu, Rommie E. Amaro & Michael K. Gilson

  2. RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA

    Huanwang Yang, Chenghua Shao & Stephen K. Burley

  3. San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA

    Huanwang Yang, Chenghua Shao & Stephen K. Burley

  4. Relay Therapeutics, Cambridge, MA, 20139, USA

    W. Patrick Walters

  5. Novartis Institutes for BioMedical Research, Emeryville, CA, 94608, USA

    Johanna M. Jansen

  6. Vertex Pharmaceuticals Inc, 50 Northern Ave, Boston, MA, 02210, USA

    Georgia McGaughey

  7. Novartis Institutes for BioMedical Research, Novartis Pharma AG, 4002, Basel, Switzerland

    Richard A. Lewis

  8. Denovicon Therapeutics, San Diego, CA, 92130, USA

    Scott D. Bembenek

  9. Janssen Research & Development, San Diego, CA, 92121, USA

    Michael K. Ameriks & Tara Mirzadegan

  10. Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093-0340, USA

    Rommie E. Amaro

  11. Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, 9500 Gilman Drive, MC0751, La Jolla, CA, 92093, USA

    Michael K. Gilson

Authors
  1. Conor D. Parks
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  2. Zied Gaieb
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  3. Michael Chiu
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  4. Huanwang Yang
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  5. Chenghua Shao
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  6. W. Patrick Walters
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  7. Johanna M. Jansen
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  8. Georgia McGaughey
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  9. Richard A. Lewis
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  10. Scott D. Bembenek
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  11. Michael K. Ameriks
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  12. Tara Mirzadegan
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  13. Stephen K. Burley
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  14. Rommie E. Amaro
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  15. Michael K. Gilson
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Corresponding authors

Correspondence to Rommie E. Amaro or Michael K. Gilson.

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Parks, C.D., Gaieb, Z., Chiu, M. et al. D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 34, 99–119 (2020). https://doi.org/10.1007/s10822-020-00289-y

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  • DOI: https://doi.org/10.1007/s10822-020-00289-y

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