iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach

被引:715
作者
Daina, Antoine [1 ]
Michielin, Olivier [1 ,2 ,3 ,4 ]
Zoete, Vincent [1 ]
机构
[1] SIB Swiss Inst Bioinformat, Mol Modeling Grp, CH-1015 Lausanne, Switzerland
[2] Univ Lausanne, Ludwig Ctr Canc Res, CH-1015 Lausanne, Switzerland
[3] Univ Lausanne, Dept Oncol, CH-1011 Lausanne, Switzerland
[4] CHU Vaudois, CH-1011 Lausanne, Switzerland
关键词
MOLECULAR-FORCE FIELD; CONTINUUM SOLVATION MODEL; FREE-ENERGY CALCULATIONS; IMPLICIT SOLVENT MODELS; LOG-P; PHYSICOCHEMICAL PARAMETERS; APPLICABILITY DOMAIN; CONFORMER GENERATION; WATER SOLUBILITY; QSAR MODELS;
D O I
10.1021/ci500467k
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
The n-octanol/water partition coefficient (log P-o/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log P-o/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per molecule), and tridimensional molecular graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.
引用
收藏
页码:3284 / 3301
页数:18
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