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Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database.

https://arctichealth.org/en/permalink/ahliterature9350
Source
J Pharm Sci. 2004 Dec;93(12):3103-10
Publication Type
Article
Date
Dec-2004
Author
Igor V Tetko
Pierre Bruneau
Author Affiliation
Biomedical Department, IBPC, Ukrainian Academy of Sciences, Murmanskaya 1, Kyiv, 02094, Ukraine. itetko@vcclab.org
Source
J Pharm Sci. 2004 Dec;93(12):3103-10
Date
Dec-2004
Language
English
Publication Type
Article
Keywords
1-Octanol - chemistry - metabolism
Databases, Factual - statistics & numerical data
Drug Industry - methods - statistics & numerical data
Predictive value of tests
Research Support, Non-U.S. Gov't
Software
Water - chemistry - metabolism
Abstract
The ALOGPS 2.1 was developed to predict 1-octanol/water partition coefficients, logP, and aqueous solubility of neutral compounds. An exclusive feature of this program is its ability to incorporate new user-provided data by means of self-learning properties of Associative Neural Networks. Using this feature, it calculated a similar performance, RMSE = 0.7 and mean average error 0.5, for 2569 neutral logP, and 8122 pH-dependent logD(7.4), distribution coefficients from the AstraZeneca "in-house" database. The high performance of the program for the logD(7.4) prediction looks surprising, because this property also depends on ionization constants pKa. Therefore, logD(7.4) is considered to be more difficult to predict than its neutral analog. We explain and illustrate this result and, moreover, discuss a possible application of the approach to calculate other pharmacokinetic and biological activities of chemicals important for drug development.
PubMed ID
15514985 View in PubMed
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