Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation. (February 2019) - Chen, Jonathan J.; Schmucker, Lyndsey N.; Visco, Donald P.
Graphical abstract: Highlights: Trained predictive SVM classification/regression models with available hPK-M2 data. Screened PubChem Compound Database, ∼72 million compounds, for lead candidates. Select candidates identified and purchased for activity validation experiments. Candidate screening hit-rate: 1st round – 1/12 (8.13%), 2nd round – 6/11 (54.5%). 2nd round result analysis for extrapolation effects: stepwise extrapolation advised. Abstract: Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.