ZINC 15 – Ligand Discovery for Everyone

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Abstract

Many questions about the biological activity and availability of small molecules remain inaccessible to investigators who could most benefit from their answers. To narrow the gap between chemoinformatics and biology, we have developed a suite of ligand annotation, purchasability, target, and biology association tools, incorporated into ZINC and meant for investigators who are not computer specialists. The new version contains over 120 million purchasable “drug-like” compounds – effectively all organic molecules that are for sale – a quarter of which are available for immediate delivery. ZINC connects purchasable compounds to high-value ones such as metabolites, drugs, natural products, and annotated compounds from the literature. Compounds may be accessed by the genes for which they are annotated as well as the major and minor target classes to which those genes belong. It offers new analysis tools that are easy for nonspecialists yet with few limitations for experts. ZINC retains its original 3D roots – all molecules are available in biologically relevant, ready-to-dock formats. ZINC is freely available at http://zinc15.docking.org.

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HMDB: a knowledgebase for the human metabolome

David Wishart, Craig Knox, An Guo … (2009)

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

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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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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13.

Jean-Louis Reymond, Lorenz Blum (2009)

GDB-13 enumerates small organic molecules containing up to 13 atoms of C, N, O, S, and Cl following simple chemical stability and synthetic feasibility rules. With 977,468,314 structures, GDB-13 is the largest publicly available small organic molecule database to date.

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Author and article information

Journal

Journal ID (nlm-ta): J Chem Inf Model

Journal ID (iso-abbrev): J Chem Inf Model

Journal ID (publisher-id): ci

Journal ID (coden): jcisd8

Title: Journal of Chemical Information and Modeling

Publisher: American Chemical Society

ISSN (Print): 1549-9596

ISSN (Electronic): 1549-960X

Publication date (Electronic): 19 October 2015

Publication date (Print): 23 November 2015

Volume: 55

Issue: 11

Pages: 2324-2337

Affiliations

[1]Department of Pharmaceutical Chemistry, University of California, San Francisco , Byers Hall, 1700 4th Street, San Francisco, California 94158-2330, United States

Author notes

[* ]Phone 415-514-4127. E-mail: jji@ 123456cgl.ucsf.edu .

Article

DOI: 10.1021/acs.jcim.5b00559

PMC ID: 4658288

PubMed ID: 26479676

SO-VID: b463e9de-7d8f-4506-a3d4-9ee7ad065860

License:

This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.