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D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

Abstract：

:The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

journal_name

J Comput Aided Mol Des

journal_title

Journal of computer-aided molecular design

authors

Gathiaka S,Liu S,Chiu M,Yang H,Stuckey JA,Kang YN,Delproposto J,Kubish G,Dunbar JB Jr,Carlson HA,Burley SK,Walters WP,Amaro RE,Feher VA,Gilson MK

doi

10.1007/s10822-016-9946-8

subject

Has Abstract

pub_date

2016-09-01 00:00:00

pages

651-668

issue

9

eissn

0920-654X

issn

1573-4951

pii

10.1007/s10822-016-9946-8

journal_volume

30

pub_type

杂志文章

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