J Chem Inf Model - iLOGP: a simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach.

Tópicos

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Resumo

The n-octanol/water partition coefficient (log?Po/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 Po/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.

Resumo Limpo

noctanolwat partit coeffici logpow key physicochem paramet drug discoveri design develop present physicsbas approach show strong linear correl comput solvat free energi implicit solvent experiment log pow cleans data set molecul intern valid fivefold crossvalid data random predict power interest multipl linear model base two gbsa paramet sole test two differ extern set molecul martel druglik test set predict power best model n r mae rmse similar six wellestablish empir method drug test set model outperform compar empir methodolog n r mae rmse physic basi origin gbsa approach togeth predict capac comput effici s per molecul tridimension molecular graphic capabl lay foundat promis predictor implicit log p method ilogp complement portfolio drug design tool develop provid sib swiss institut bioinformat

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