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

Book Title: NA
Year Published: 2004
Month Published: DEC
Author: Tetko, IV ; Bruneau, P
Book Group Author: NA
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 logD7.4 prediction looks surprising, because this property also depends on ionization constants pK(a). 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. (C) 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Pages: 3103-3110
URL: NA
Volume: 93
Number: 12
Journal: JOURNAL OF PHARMACEUTICAL SCIENCES
Journal ISO: J. Pharm. Sci.
Organization: NA
Publisher: JOHN WILEY & SONS INC
ISBN: NA
ISSN: 0022-3549
DOI: 10.1002/jps.20217
Keywords:

logP; logD; computational ADME; drug design; drug-like properties; associative neural network; QSAR; QSPR

Source: Web of Science
Series:
Series Number:
Document Type:
Subject Category: