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Comments on the article "Evaluation of pK(a) estimation methods on 211 druglike compounds".

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 Model

journal_title

Journal of chemical information and modeling

authors

Shelley JC,Calkins D,Sullivan AP

doi

10.1021/ci100332m

subject

Has Abstract

pub_date

2011-01-24 00:00:00

pages

102-4

issue

1

eissn

1549-9596

issn

1549-960X

journal_volume

51

pub_type

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