Directory of Useful Decoys,
Enhanced (DUD-E): Better
Ligands and Decoys for Better Benchmarking

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Abstract

A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target’s ligands by their Bemis–Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool ( http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.

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Most cited references 44

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The properties of known drugs. 1. Molecular frameworks.

G Bemis, M. Murcko (1996)

In order to better understand the common features present in drug molecules, we use shape description methods to analyze a database of commercially available drugs and prepare a list of common drug shapes. A useful way of organizing this structural data is to group the atoms of each drug molecule into ring, linker, framework, and side chain atoms. On the basis of the two-dimensional molecular structures (without regard to atom type, hybridization, and bond order), there are 1179 different frameworks among the 5120 compounds analyzed. However, the shapes of half of the drugs in the database are described by the 32 most frequently occurring frameworks. This suggests that the diversity of shapes in the set of known drugs is extremely low. In our second method of analysis, in which atom type, hybridization, and bond order are considered, more diversity is seen; there are 2506 different frameworks among the 5120 compounds in the database, and the most frequently occurring 42 frameworks account for only one-fourth of the drugs. We discuss the possible interpretations of these findings and the way they may be used to guide future drug discovery research.

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Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

Paul Hawkins, A. Skillman, Gregory Warren … (2010)

Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.

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Benchmarking sets for molecular docking.

Niu Huang, Brian K. Shoichet, John Irwin (2006)

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.

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Author and article information

Journal

Journal ID (nlm-ta): J Med Chem

Journal ID (iso-abbrev): J. Med. Chem

Journal ID (publisher-id): jm

Journal ID (coden): jmcmar

Title: Journal of Medicinal Chemistry

Publisher: American Chemical Society

ISSN (Print): 0022-2623

ISSN (Electronic): 1520-4804

Publication date (Electronic): 20 June 2012

Publication date (Print): 26 July 2012

Volume: 55

Issue: 14

Pages: 6582-6594

Affiliations

[† ]Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94158-2330, United States

Author notes

[* ]For J.J.I.: phone, (415) 514-4127; E-mail, jji@ 123456cgl.ucsf.edu . For B.K.S.: phone, (415) 514-4126; E-mail, shoichet@ 123456cgl.ucsf.edu . Address: John J. Irwin or Brian K. Shoichet, Department of Pharmaceutical Chemistry, University of California San Francisco, 1700 Fourth Street, Box 2550, San Francisco, CA 94158-2330.

Article

DOI: 10.1021/jm300687e

PMC ID: 3405771

PubMed ID: 22716043

SO-VID: 76509a22-2ed1-4120-8d4d-ec62cc96d655

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This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org.

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Date received : 16 May 2012

Funding

National Institutes of Health, United States

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ScienceOpen disciplines: Pharmaceutical chemistry

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ScienceOpen disciplines: Pharmaceutical chemistry

Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking

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Abstract

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Most cited references 44

The properties of known drugs. 1. Molecular frameworks.

Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

Benchmarking sets for molecular docking.

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