Selectivity Data: Assessment, Predictions, Concordance, and Implications

Could
high-quality in silico predictions in drug discovery eventually
replace part or most of experimental testing? To evaluate the agreement
of selectivity data from different experimental or predictive sources,
we introduce the new metric concordance minimum significant ratio
(cMSR). Empowered by cMSR, we find the overall level of agreement
between predicted and experimental data to be comparable to that found
between experimental results from different sources. However, for
molecules that are either highly selective or potent, the concordance
between different experimental sources is significantly higher than
the concordance between experimental and predicted values. We also
show that computational models built from one data set are less predictive
for other data sources and highlight the importance of bias correction
for assessing selectivity data. Finally, we show that small-molecule
target space relationships derived from different data sources and
predictive models share overall similarity but can significantly differ
in details.