D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings
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The Drug Design Data Resource aims to test and advance the state of the art in protein–ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017–2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.
KeywordsD3R Drug Design Data Resource Docking Scoring Ligand ranking Blinded prediction challenge
This work was supported by National Institutes of Health (NIH) Grant No. 1U01GM111528 for the Drug Design Data Resource (D3R). We also thank OpenEye Scientific Software for generously donating the use of their software. RCSB Protein Data Bank is supported by NSF, NCI, NIGMS, and DOE (Grant No. NSF DBI-1338415). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. 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.
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