Machine learning models identify molecules active against the Ebola virus in vitro

  • Sean Ekins, Joel S. Freundlich, Alex M. Clark, Manu Anantpadma, Robert A. Davey, Peter Madrid
  • F1000Research, January 2016, Faculty of 1000, Ltd.
  • DOI: 10.12688/f1000research.7217.2

Identifying Ebola virus inhibitors using machine learning

What is it about?

This paper assesses machine learning approaches to predict activity against Ebola virus in vitro. 3 compounds were selected using a Bayesian model and all were active in the hundreds of nM range. One of the molecules is an antimalarial called pyronaridine.

Why is it important?

This work is the first example of using Machine learning to suggest compounds to test and is backed up by in vitro testing. Its also important as it shows we can repurpose an EU approved antimalarial as an antiviral. The approach is broadly applicable.

Perspectives

Dr Sean Ekins
Collaborations in Chemistry

This work is the first example of using Machine learning to suggest compounds to test and is backed up by in vitro testing. Its also important as it shows we can repurpose an EU approved antimalarial as an antiviral. The approach is broadly applicable.

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http://dx.doi.org/10.12688/f1000research.7217.2

The following have contributed to this page: Dr Sean Ekins