A Machine Learning Approach to Pharmacological Profiling of the Quinone Scaffold in the NCI Database: A Compound Class Enriched in Those Effective Against Melanoma and Leukemia Cell Lines

摘要

We have carried out supervised machine learning on a subset (8741 compounds) of the public NCI Cancer Compound Library screened for effectiveness against 60 cancer cell lines. Our focus was on identifying quinone compounds and we found these to be over four-fold enriched compared to the entire NCI Cancer compound library. Two-Class classifications based upon the cell types'' tumor tissue origin classes, identified subsets of compounds that were most effective against either melanoma or leukemia cancer cell types. Both of these compound subsets were enriched in quinone compounds.