Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds.
J Med Chem
; 47(23): 5601-4, 2004 Nov 04.
Article
in En
| MEDLINE
| ID: mdl-15509156
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.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
Pharmaceutical Preparations
/
Biological Availability
/
Drug Design
/
Quantitative Structure-Activity Relationship
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
J Med Chem
Journal subject:
QUIMICA
Year:
2004
Type:
Article
Affiliation country:
Ukraine