Selectivity data: assessment, predictions, concordance, and implications.

@article{Gao2013SelectivityDA,
  title={Selectivity data: assessment, predictions, concordance, and implications.},
  author={Cen Gao and Suntara Cahya and Christos A. Nicolaou and Jibo Wang and Ian A. Watson and David J. Cummins and Philip W. Iversen and Michal Vieth},
  journal={Journal of medicinal chemistry},
  year={2013},
  volume={56 17},
  pages={6991-7002}
}
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… CONTINUE READING