Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty

Matched molecular pair analysis (MMPA)
has become a major tool
for analyzing large chemistry data sets for promising chemical transformations.
However, the dependence of MMPA predictions on data constraints such
as the number of pairs involved, experimental uncertainty, source
of the experiments, and variability of the true physical effect has
not yet been described. In this contribution the statistical basics
for judging MMPA are analyzed. We illustrate the connection between
overall MMPA statistics and individual pairs with a detailed comparison
of average CHEMBL hERG MMPA results versus pairs with extreme transformation
effects. Comparing the CHEMBL results to Novartis data, we find that
significant transformation effects agree very well if the experimental
uncertainty is considered. This indicates that caution must be exercised
for predictions from insignificant MMPAs, yet highlights the robustness
of statistically validated MMPA and shows that MMPA on public databases
can yield results that are very useful for medicinal chemistry.