SmartGraph: a network pharmacology investigation platform

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Abstract

Motivation

Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals.

Results

The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction.

Availability

https://smartgraph.ncats.io/.

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Most cited references 19

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Network pharmacology.

Andrew Hopkins (2007)

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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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Is Open Access

InChI - the worldwide chemical structure identifier standard

Stephen Heller, Alan McNaught, Stephen Stein … (2013)

Since its public introduction in 2005 the IUPAC InChI chemical structure identifier standard has become the international, worldwide standard for defined chemical structures. This article will describe the extensive use and dissemination of the InChI and InChIKey structure representations by and for the world-wide chemistry community, the chemical information community, and major publishers and disseminators of chemical and related scientific offerings in manuscripts and databases.

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Author and article information

Contributors

Gergely Zahoránszky-Kőhalmi:

ORCID: http://orcid.org/0000-0002-2534-8770

gzahoranszky@gmail.com

Journal

Journal ID (nlm-ta): J Cheminform

Journal ID (iso-abbrev): J Cheminform

Title: Journal of Cheminformatics

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 1758-2946

Publication date (Electronic): 21 January 2020

Publication date PMC-release: 21 January 2020

Publication date Collection: 2020

Volume: 12

Electronic Location Identifier: 5

Affiliations

[1 ]ISNI 0000 0004 3497 6087, GRID grid.429651.d, National Center for Advancing Translational Sciences, ; Rockville, MD USA

[2 ]ISNI 0000 0001 2188 8502, GRID grid.266832.b, Department of Internal Medicine, , University of New Mexico School of Medicine, ; Albuquerque, NM USA

[3 ]ISNI 0000 0001 2188 8502, GRID grid.266832.b, UNM Comprehensive Cancer Center, ; Albuquerque, NM USA

[4 ]ISNI 0000 0000 9919 9582, GRID grid.8761.8, Department of Rheumatology and Inflammation Research, Institute of Medicine, , Sahlgrenska Academy At University of Gothenburg, ; Gothenburg, Sweden

[5 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark

Author information

Gergely Zahoránszky-Kőhalmi http://orcid.org/0000-0002-2534-8770

Article

Publisher ID: 409

DOI: 10.1186/s13321-020-0409-9

PMC ID: 6974502

PubMed ID: 33430980

SO-VID: 10dc3f91-71e5-478e-b4e3-ad20d49236ae

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 July 2019

Date accepted : 7 January 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1U54CA189205-01

Award ID: GM095952

Award Recipient : Tudor I. Oprea

Custom metadata

ScienceOpen disciplines: Chemoinformatics

Keywords: network pharmacology,pathway analysis,target deconvolution,network perturbation,protein–protein interactions (ppis),bioactivity prediction,potent chemical pattern,scaffold,neo4j,network visualization

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ScienceOpen disciplines: Chemoinformatics

Keywords: network pharmacology, pathway analysis, target deconvolution, network perturbation, protein–protein interactions (ppis), bioactivity prediction, potent chemical pattern, scaffold, neo4j, network visualization

SmartGraph: a network pharmacology investigation platform

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Abstract

Motivation

Results

Availability

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Drug Repurposing Research Collection

Most cited references 19

Network pharmacology.

The properties of known drugs. 1. Molecular frameworks.

InChI - the worldwide chemical structure identifier standard

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