These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
2 files

D3R Grand Challenge 3: Blind Prediction of Protein-Ligand Poses and Affinity Rankings

submitted on 05.07.2018, 22:58 and posted on 06.07.2018, 16:47 by Zied Gaieb, Conor Parks, Michael Chiu, Huanwang Yang, Chenghua Shao, Patrick Walters, Millard Lambert, Neysa Nevins, Scott D. Bembenek, Stephen K. Burley, Rommie E. Amaro, Michael K. Gilson

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 sub-challenge, in which the protein coordinates from all of the co-crystal 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.


Email Address of Submitting Author


University of California San Diego



ORCID For Submitting Author


Declaration of Conflict of Interest

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.


Read the published paper

in Journal of Computer-Aided Molecular Design