What is it about?
Over 3000 compounds screened against malaria were used for machine learning using kNN and SVM. These were used for virtual screening the ChemBridge library. 176 compounds were selected for testing 18 had moderate activity and 7 had EC50 less than 2uM. Most active had EC50 of 95.6nM
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Why is it important?
A demonstration of CombiQSAR to show internal and external validation. Models could be used for antimalarial prediction and models available on chembench.
Perspectives
A great deal of work went into this on the modeling and testing side. The approach identified some novel scaffolds. The paper describes a workflow that others could apply elsewhere. Similar to our approaches with TB etc.
Dr Sean Ekins
Collaborations in Chemistry
Read the Original
This page is a summary of: Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening, Journal of Chemical Information and Modeling, January 2013, American Chemical Society (ACS),
DOI: 10.1021/ci300421n.
You can read the full text:
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