Abstract:
:The recent article "Evaluation of pK(a) Estimation Methods on 211 Druglike Compounds" ( Manchester, J.; et al. J. Chem Inf. Model. 2010, 50, 565-571 ) reports poor results for the program Epik. Here, we highlight likely sources for the poor performance and describe work done to improve the performance. Running Epik in the mode intended to calculate pK(a) values for sequentially adding/removing protons, as needed to reproduce the experimental conditions, improves the root mean squared error (RMSE) from 3.0 to 2.18 for the 85 public compounds available from the paper. Despite this improvement, there are still other programs in the Manchester paper that outperform Epik. The primary reason is that the public portion of the data set is not diverse and Epik is missing a few key functional groups in this data set that are heavily represented. We show that incorporation of these missing functional groups into the Epik training set improves the RMSE for the public compounds to 1.04. Furthermore, these enhancements help improve the overall performance of Epik on a large druglike test set.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Shelley JC,Calkins D,Sullivan APdoi
10.1021/ci100332msubject
Has Abstractpub_date
2011-01-24 00:00:00pages
102-4issue
1eissn
1549-9596issn
1549-960Xjournal_volume
51pub_type
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