title: Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds.
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.
authors.affiliation: Biomedical Department, Institute of Bioorganic and Petroleum Chemistry, Ukrainian Academy of Sciences, Murmanskaya 1, Kyiv, 02094, Ukraine. itetko@vcclab.org
authors.forename: Igor V
authors.initials: IV
authors.lastname: Tetko
authors.forename: Gennadiy I
authors.initials: GI
authors.lastname: Poda
mesh_terms: D020003:1-Octanol; D000284:Administration, Oral; D001682:Biological Availability; D002626:Chemistry, Pharmaceutical; D016208:Databases, Factual; D015195:Drug Design; D004345:Drug Industry; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D017149:Private Sector; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D012995:Solubility; D014867:Water
chemical_list: D004364:Pharmaceutical Preparations; D014867:Water; D020003:1-Octanol