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

Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK
J Comput Aided Mol Des 34 99-119 (2020)
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Cited: 44 times
EuropePMC logo PMID: 31974851

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.

Reviews citing this publication (9)

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Articles citing this publication (35)

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  15. Shape-Restrained Modeling of Protein-Small-Molecule Complexes with High Ambiguity Driven DOCKing. Koukos PI, Réau M, Bonvin AMJJ. J Chem Inf Model 61 4807-4818 (2021)
  16. Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods. Hayes RL, Vilseck JZ, Brooks CL. J Chem Theory Comput 18 2114-2123 (2022)
  17. An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models. Parks C, Gaieb Z, Amaro RE. Front Mol Biosci 7 93 (2020)
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