D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings

Zied Gaieb, Conor D. Parks, Michael Chiu, Huanwang Yang, Chenghua Shao, W. Patrick Walters, Millard H. Lambert, Neysa Nevins, Scott D. Bembenek, Michael K. Ameriks, Tara Mirzadegan, Stephen K. Burley, Rommie E. Amaro, Michael K. Gilson

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

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.

Original languageEnglish (US)
JournalJournal of Computer-Aided Molecular Design
Volume33
Issue number1
DOIs
StatePublished - Jan 15 2019

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry

Keywords

  • Blinded prediction challenge
  • D3R
  • Docking
  • Drug Design Data Resource
  • Ligand ranking
  • Scoring

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