A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

Research output: Contribution to journalJournal articlepeer-review

Standard

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. / Zahoránszky-Kőhalmi, Gergely; Siramshetty, Vishal B; Kumar, Praveen; Gurumurthy, Manideep; Grillo, Busola; Mathew, Biju; Metaxatos, Dimitrios; Backus, Mark; Mierzwa, Tim; Simon, Reid; Grishagin, Ivan; Brovold, Laura; Mathé, Ewy A; Hall, Matthew D; Michael, Samuel G; Godfrey, Alexander G; Mestres, Jordi; Jensen, Lars J.; Oprea, Tudor I.

In: Journal of Chemical Information and Modeling, Vol. 62, No. 3, 2022, p. 718-729.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Zahoránszky-Kőhalmi, G, Siramshetty, VB, Kumar, P, Gurumurthy, M, Grillo, B, Mathew, B, Metaxatos, D, Backus, M, Mierzwa, T, Simon, R, Grishagin, I, Brovold, L, Mathé, EA, Hall, MD, Michael, SG, Godfrey, AG, Mestres, J, Jensen, LJ & Oprea, TI 2022, 'A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research', Journal of Chemical Information and Modeling, vol. 62, no. 3, pp. 718-729. https://doi.org/10.1021/acs.jcim.1c00431

APA

Zahoránszky-Kőhalmi, G., Siramshetty, V. B., Kumar, P., Gurumurthy, M., Grillo, B., Mathew, B., Metaxatos, D., Backus, M., Mierzwa, T., Simon, R., Grishagin, I., Brovold, L., Mathé, E. A., Hall, M. D., Michael, S. G., Godfrey, A. G., Mestres, J., Jensen, L. J., & Oprea, T. I. (2022). A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. Journal of Chemical Information and Modeling, 62(3), 718-729. https://doi.org/10.1021/acs.jcim.1c00431

Vancouver

Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B et al. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. Journal of Chemical Information and Modeling. 2022;62(3):718-729. https://doi.org/10.1021/acs.jcim.1c00431

Author

Zahoránszky-Kőhalmi, Gergely ; Siramshetty, Vishal B ; Kumar, Praveen ; Gurumurthy, Manideep ; Grillo, Busola ; Mathew, Biju ; Metaxatos, Dimitrios ; Backus, Mark ; Mierzwa, Tim ; Simon, Reid ; Grishagin, Ivan ; Brovold, Laura ; Mathé, Ewy A ; Hall, Matthew D ; Michael, Samuel G ; Godfrey, Alexander G ; Mestres, Jordi ; Jensen, Lars J. ; Oprea, Tudor I. / A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. In: Journal of Chemical Information and Modeling. 2022 ; Vol. 62, No. 3. pp. 718-729.

Bibtex

@article{d85743cd03074cb5a023780fabb1f781,
title = "A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research",
abstract = "Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named {"}Neo4COVID19{"} is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.Availability: https://neo4covid19.ncats.io.",
author = "Gergely Zahor{\'a}nszky-K{\H o}halmi and Siramshetty, {Vishal B} and Praveen Kumar and Manideep Gurumurthy and Busola Grillo and Biju Mathew and Dimitrios Metaxatos and Mark Backus and Tim Mierzwa and Reid Simon and Ivan Grishagin and Laura Brovold and Math{\'e}, {Ewy A} and Hall, {Matthew D} and Michael, {Samuel G} and Godfrey, {Alexander G} and Jordi Mestres and Jensen, {Lars J.} and Oprea, {Tudor I.}",
year = "2022",
doi = "10.1021/acs.jcim.1c00431",
language = "English",
volume = "62",
pages = "718--729",
journal = "Journal of Chemical Information and Modeling",
issn = "1549-9596",
publisher = "American Chemical Society",
number = "3",

}

RIS

TY - JOUR

T1 - A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

AU - Zahoránszky-Kőhalmi, Gergely

AU - Siramshetty, Vishal B

AU - Kumar, Praveen

AU - Gurumurthy, Manideep

AU - Grillo, Busola

AU - Mathew, Biju

AU - Metaxatos, Dimitrios

AU - Backus, Mark

AU - Mierzwa, Tim

AU - Simon, Reid

AU - Grishagin, Ivan

AU - Brovold, Laura

AU - Mathé, Ewy A

AU - Hall, Matthew D

AU - Michael, Samuel G

AU - Godfrey, Alexander G

AU - Mestres, Jordi

AU - Jensen, Lars J.

AU - Oprea, Tudor I.

PY - 2022

Y1 - 2022

N2 - Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.Availability: https://neo4covid19.ncats.io.

AB - Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.Availability: https://neo4covid19.ncats.io.

U2 - 10.1021/acs.jcim.1c00431

DO - 10.1021/acs.jcim.1c00431

M3 - Journal article

C2 - 33173863

VL - 62

SP - 718

EP - 729

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

SN - 1549-9596

IS - 3

ER -

ID: 261516977