| Literature DB >> 15509156 |
Igor V Tetko1, Gennadiy I Poda.
Abstract
Evaluation of the ALOGPS, ACD Labs LogD, and PALLAS PrologD suites to calculate the log D distribution coefficient resulted in high root-mean-squared error (RMSE) of 1.0-1.5 log for two in-house Pfizer's log D data sets of 17,861 and 640 compounds. Inaccuracy in log P prediction was the limiting factor for the overall log D estimation by these algorithms. The self-learning feature of the ALOGPS (LIBRARY mode) remarkably improved the accuracy in log D prediction, and an rmse of 0.64-0.65 was calculated for both data sets.Mesh：
Substances：
Year: 2004 PMID： 15509156 DOI： 10.1021/jm049509l
Source DB: PubMed Journal: J Med Chem ISSN： 0022-2623 Impact factor: 7.446