J Chem Inf Model - Comments on the article Evaluation of pK(a) estimation methods on 211 druglike compounds.

Tópicos

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Resumo

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.

Resumo Limpo

recent articl evalu pka estim method druglik compound manchest j et al j chem inf model report poor result program epik highlight like sourc poor perform describ work done improv perform run epik mode intend calcul pka valu sequenti addingremov proton need reproduc experiment condit improv root mean squar error rmse public compound avail paper despit improv still program manchest paper outperform epik primari reason public portion data set divers epik miss key function group data set heavili repres show incorpor miss function group epik train set improv rmse public compound furthermor enhanc help improv overal perform epik larg druglik test set

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