Paper Info
Title
Computation of octanol-water partition coefficients by guiding an additive model with knowledge.
Abstract
We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of :3199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and Correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.
Year
DOI
Venue
2007
10.1021/ci700257y
JOURNAL OF CHEMICAL INFORMATION AND MODELING
DocType
Volume
Issue
Journal
47
6
ISSN
Citations 
PageRank 
1549-9596
24
1.25
References 
Authors
12
9
Name
Order
Citations
PageRank
Tiejun Cheng130222.39
Yuan Zhao219830.96
Xun Li31697.81
Fu Lin416210.90
Yong Xu5241.25
Xinglong Zhang6316.15
Yan Li736535.61
Renxiao Wang861051.40
Luhua Lai936933.78