CoVex stories - Part two 

(April 19, 2020)

Check the CoVex website:

Also check part 1 of this COVID19 systems medicine hackaton blog.


As we promised in our first ExBio vs. COVID-19 (where we described our work on release  1.0 of CoVex) we continued updating and adding new features to improve the CoVex platform and satisfy the needs of the users. We implemented a bunch of new features based on the feedback that we received from you and the COVID-19 community. Additionally, we also explored four different application scenarios as an inspiration on how scientists can test hypotheses and extract new drug repurposing candidates using CoVex. We are also in the process of drafting a corresponding publication.

Some Figures

CoVex support to four application scenarios

The concept of this demonstration is to start with a hypothesis and to subsequently survey drug candidates associated with it using CoVex. Here is an overview of the four application scenarios that we have tested :


  1. Starting with viral proteins, one can identify drugs targeting host proteins that connect the viral seeds.

  2. Starting with a set of proteins of interest as proxies, we identify pathways connecting them to (selected or all) viral proteins. Subsequently, we identify drugs targeting this mechanism.

  3. Starting with a set of drugs of interest, one may find pathways to (selected or all) viral proteins extracting a potentially druggable host mechanism.

  4. Hypothesis-driven, hybrid approach with seeds in different levels to be connected for druggable mechanism extraction.

The starting hypotheses, seeds, intermediate steps with CoVex, and the outcomes of each experiment will be detailed and discussed in our manuscript.

Change log: New features

Here, we overview the new features added in the version 1.1 of CoVex.


  • Custom Protein(s) Upload: The user can upload a custom list of proteins (e.g.  proteins from a specific tissue such as lung), which either will be added to the current selection or all proteins from the list that are also visible in the current network will be selected.

  • New Ways of Selection: ‘Invert’, ‘Add/Remove’ are the newly added ways of selection. The former inverts the current selection (all visible proteins that are selected get deselected and vice versa) and the latter adds/removes all seed proteins that are currently visible.

  • Task-specific Selection: A separate selection list is maintained for each task. In the previous version, the selection list was global.

  • Enrichment Analysis: This feature employs an external service called g:Profiler to enrich a set of selected proteins.

  • Closest Baits: In the result network,  the closest baits for each drug and drug targets as well as the corresponding distances are displayed.

  • New Input Parameters for Algorithms: ‘max degree’ and ‘hub penalty’ were added as input parameters for most of the algorithms.

  • Graph Export: The user can export the graphs (e.g. protein-protein interaction graph ) as ‘graphml’ file, which can be imported to other graph analysis tools.

  • Improvement for Large Network Results: The animation of the network is disabled if the resulting network is large.

  • Addition of ‘in-literature’ drugs: Besides ‘in-trial’ drugs, the new drugs discussed in literature were added to the drug database and can be used in the analysis.

Next steps

COVID-19 has significant impact on pretty much all aspects of our lives and mankind in a race to discover a treatment that will put an end to the spread of the SARS-CoV-2 pandemic. The hopes of millions of affected people around the globe rely on medical research filed to submit an effective solution. To contribute to this effort and share our findings, we are about to finish with a first manuscript draft that details the use of CoVex and its utility for integrated drug repurposing using network and systems medicine. We are also preparing a short new video on the power of network and systems medicine for drug repurposing research and how it can be applied to anti-SARS-CoV-2 drug hunting using different algorithms and machine learning tools.


Chair of Experimental Bioinformatics

TUM School of Life Sciences Weihenstephan

Technical University of Munich

Tel: +49-8161-71-2136

E-Mail: exbio[a.t_))

Office: OG-L06