posted on 2022-01-21, 04:29authored byGergely Zahoránszky-Kőhalmi, Vishal B. Siramshetty, Praveen Kumar, Manideep Gurumurthy, Busola Grillo, Biju Mathew, Dimitrios Metaxatos, Mark Backus, Tim Mierzwa, Reid Simon, Ivan Grishagin, Laura Brovold, Ewy A. Mathé, Matthew D. Hall, Samuel G. Michael, Alexander G. Godfrey, Jordi Mestres, Lars J. Jensen, Tudor I. Oprea
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, host–pathogen, and drug–target
interactions, remains a time-consuming effort that translates to a
delay in the development and delivery of a life-saving therapy. Here,
we describe a workflow we designed for a semiautomated integration
of rapidly emerging data sets 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,
63 278 host–host protein, and 1221 drug–target
interactions. The resultant Neo4j graph database named “Neo4COVID19”
is made publicly accessible via a web interface and via API calls
based on the Bolt protocol. Details for accessing the database are
provided on a landing page (https://neo4covid19.ncats.io/). 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.