Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening

  • Sean Ekins, Julie Clark, Michele C. Connelly, Martina Sigal, Dena Hodges, Armand Guiguemde, R. Kiplin Guy, Alexander Tropsha, Liying Zhang, Denis Fourches, Alexander Sedykh, Hao Zhu, Alexander Golbraikh
  • Journal of Chemical Information and Computer Sciences, February 2013, American Chemical Society (ACS)
  • DOI: 10.1021/ci300421n

Virtual screening for antimalarials

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

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.


Dr Sean Ekins
Collaborations in Chemistry

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.

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The following have contributed to this page: Dr Sean Ekins