Discovering Novel and Diverse Iron-Chelators in Silico.

Specific iron chelation is a validated strategy in anticancer drug discovery. However, only a few chemical classes (4-5 categories) have been reported to date. We discovered in silico five new structurally diverse iron-chelators by screening through models based on previously known chelators. To encompass a larger chemical space and propose newer scaffolds, we used our iterative stochastic elimination (ISE) algorithm for model building and subsequent virtual screening (VS). The ISE models were developed by training a data set of 130 reported iron-chelators. The developed models are statistically significant with area under the receiver operating curve greater than 0.9. The models were used to screen the Enamine chemical database of ∼1.8 million molecules. The top ranked 650 molecules were reduced to 50 diverse structures, and a few others were eliminated due to the presence of reactive groups. Finally, 34 molecules were purchased and tested in vitro. Five compounds were identified with significant iron-chelation activity in Cal-G assay. Intracellular iron-chelation study revealed one compound as equivalent in potency to the iron chelating "gold standards" deferoxamine and deferiprone. The amount of discovered positives (5 out of 34) is expected by the realistic enrichment factor of the model.