Andreas Maier1*, Michael Hartung1, The Drugst.One Initiative, and Jan Baumbach1,2
1Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
2Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
andreas.maier-1 [at] uni-hamburg.de
Abstract
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of pharmaceuticals often remain poorly understood. In response, numerous computational systems and network medicine tools have been developed to prioritize drug repurposing candidates. However, such tools often require local installation and configuration or lack follow-up visual network mining capabilities. To address these challenges and simplify network exploration and drug repurposing candidate prediction, we have developed Drugst.One. It is a customizable plug-and-play solution with its own data warehousing system integrating multiple interaction databases to enable interactive modeling and analysis of the associations between proteins, drugs, and diseases. With just three lines of code, it has the capacity to convert any systems medicine software into an interactive web tool for identifying drug repurposing candidates, thus providing a powerful and accessible resource for advancing drug discovery efforts. To demonstrate the utility of Drugst.One’s low-code approach, we have integrated it with 20 existing computational systems medicine tools of various types, with the intent to expand the Drugst.One Initiative with additional collaboration partners.
Drugst.One is, to our knowledge, the first approach to unify and simplify web-based network-based visualization and drug repurposing, posing a valuable resource for the research community. Learn more about Drugst.One and the Drugst.One Initiative at https://drugst.one.
Keywords: Drug repurposing, Systems medicine, Interactive network enrichment, Biomedical network exploration, Network integration, Biomedical data analysis, Data visualization
Acknowledgement: REPO-TRIAL: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777111. This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains.
RePo4EU: This project is funded by the European Union under grant agreement No. 101057619. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them.
This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of “CLINSPECT-M” (grant FKZ161L0214A).
JB was partially funded by his VILLUM Young Investigator Grant nr.13154.
Collaborations partners of the Drugst.One Initiative have received the following additional funding:
This work was also partly supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract No. 22.00115.
This work was supported by the Technical University Munich – Institute for Advanced Study, funded by the German Excellence Initiative. This work was supported in part by the Intramural Research Programs (IRPs) of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 422216132.
This project has received funding from the European Research Council (ERC) Consolidator Grant 770827 and the Spanish State Research Agency AEI 10.13039/501100011033 grant number PID2019-105500GB-I00.
IJ was supported in part by funding from Natural Sciences Research Council (NSERC #203475), Canada Foundation for Innovation (CFI #225404, #30865), Ontario Research Fund (RDI #34876), IBM and Ian Lawson van Toch Fund.
SL has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193 for DECIDER.
