D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies
By Conor Parks, Zied Gaieb, Michael Chiu, Huanwang Yang, Chenghua Shao, W. Patrick Walters, Johanna M. Jansen, Georgia B. McGaughey, Richard A. Lewis, S. Bembenek and 5 more. 2019
113 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
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In Computational Theory and Mathematics
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In Materials Chemistry
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In Molecular Biology
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Citations in the corpus
- Protein storytelling through physics (2020, background): “The challenge was to compute the binding affinities ( 95 ).”
- CAMEO - Perspectives on the future of fully automated evaluation of structure prediction methods (2021, unknown): “24 To score these predictions, we will first follow the procedure developed by other ligand benchmarking efforts such as CELPP 25 and the D3R Grand Challenges, 26 – 29 which evaluate ligand poses with a symmetry-corrected RMSD.”
- Computational approaches streamlining drug discovery (2023, methodology): “This synergy is reflected in the latest 3DR Grand Challenge 4 results for ligand IC 50 predictions 59 , in which the top methods that used a combination of both physics-based and ML scoring outperformed those that did not use ML.”
- AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings (2021, methodology): “This method was successfully applied in the D3R Grand Challenge 4 29 , both by us 30,31 and others 32 .”
- Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.2] (2021, methodology): “The Drug Design Data Resource [49] (D3R) and Statistical Assessment of the Modeling of Proteins and Ligands [50] (SAMPL) prospective challenges have demonstrated the utility of focusing the community on common benchmark systems and using common methods to analyze performance [51–60].”
- New prediction categories in CASP15 (2023, methodology): “Previous ligand docking challenges like Teach Discover Treat (TDT), 39 Continuous Evaluation of Ligand Prediction Performance (CELPP) 40 and Drug Discovery Data Resource (D3R) 34 – 37 have used two main types of metrics to assess how well participants can model receptor – ligand complexes.”