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.
Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana 46285, United States.
Cen
C
Gao
Suntara
S
Cahya
Christos A
CA
Nicolaou
Jibo
J
Wang
Ian A
IA
Watson
David J
DJ
Cummins
Philip W
PW
Iversen
Michal
M
Vieth
D003198:Computer Simulation; D055808:Drug Discovery