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

Abstract

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.

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

“…The curated dataset consisted of 571 chalcones, which were the subject for SAR analysis using matched molecular pairs (MMP, Figure 1) of analysis [59] that reveal changes in properties measured between structures with high similarity in this case, evaluated by MACCS keys descriptor [60] and Tanimoto coefficient (>0.7) [61] e.g. lost or gain of activity results of specific changes on structure by comparison between two structures [59,62]. …”

Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets

“…a tenth of a unit. From a medicinal chemistry standpoint that difference might be negligible [30]. Therefore, it is paramount to pay especial attention to the effect size in addition to the P values.…”

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

“…cut-off at 2 fold change). The interpretation of the relative property value change in the MMP analysis is also dependent on the experimental error [39], [40]. Commonly authors use e.g.…”

“…By assuming different experimental errors Kramer et al showed how the minimum number of pairs necessary to achieve significance can be calculated, as they explained the difference between statistical significance and effect size estimation [21], [44]. Relevant experimental errors for public and industry SAR databases are nowadays published [21], [39].…”