Computation of Octanol-Water Partition Coefficients by Guiding an Additive Model with Knowledge

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• 作者：Tiejun Cheng ; Yuan Zhao ; Xun Li ; Fu Lin ; Yong Xu ; Xinglong Zhang ; Yan Li ; Renxiao Wang ; Luhua Lai
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2007
• 出版时间：November 2007
• 年：2007
• 卷：47
• 期：6
• 页码：2140 - 2148
• 全文大小：236K
• 年卷期：v.47,no.6(November 2007)
• ISSN：1549-960X

文摘

We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP valueof a query compound by using the known logP value of a reference compound as a starting point. Thedifference in the logP values of the query compound and the reference compound is then estimated by anadditive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and twocorrection factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliablelogP data through a multivariate linear regression analysis. For a given query compound, the compoundshowing 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 alongwith 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. Onboth test sets, XLOGP3 produces more accurate predictions than most of the other methods with averageunsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not relyon an extensive classification of fragments and correction factors in order to improve accuracy. It is alsoable to utilize the ever-increasing experimentally measured logP data more effectively.