Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening

Liying Zhang, Denis Fourches, Alexander Sedykh, Hao Zhu, Alexander Golbraikh, Sean Ekins, Julie Clark, Michele C. Connelly, Martina Sigal, Dena Hodges, Armand Guiguemde, R. Kiplin Guy, Alexander Tropsha

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

Quantitative structure-activity relationship (QSAR) models have been developed for a data set of 3133 compounds defined as either active or inactive against P. falciparum. Because the data set was strongly biased toward inactive compounds, different sampling approaches were employed to balance the ratio of actives versus inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the data set. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.

Original languageEnglish (US)
Pages (from-to)475-492
Number of pages18
JournalJournal of chemical information and modeling
Volume53
Issue number2
DOIs
StatePublished - Feb 25 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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