Title
D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.
Abstract
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.
Year
DOI
Venue
2019
10.1007/s10822-018-0180-4
Journal of Computer-Aided Molecular Design
Keywords
Field
DocType
D3R, Drug Design Data Resource, Docking, Scoring, Ligand ranking, Blinded prediction challenge
Protein ligand,VEGF receptors,Ranking,Chemistry,Computational biology,Bioinformatics
Journal
Volume
Issue
ISSN
33
1
0920-654X
Citations 
PageRank 
References 
2
0.37
15
Authors
14