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.
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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
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.
Dr Sean Ekins
Collaborations in Chemistry
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This page is a summary of: Machine learning models identify molecules active against the Ebola virus in vitro, F1000Research, January 2016, Faculty of 1000, Ltd.,
DOI: 10.12688/f1000research.7217.2.
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