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

TLDR

The n‑octanol/water partition coefficient (log Pₒ/w) is a key physicochemical parameter for drug discovery, design, and development. We present a physics‑based approach that demonstrates a strong linear correlation between computed solvation free energy in implicit solvents and experimental log Pₒ/w for over 17,500 molecules. The method employs a multiple‑linear model based on two GB/SA parameters, validated by five‑fold cross‑validation and data randomization, and tested on two external molecule sets. On the Martel drug‑like set the model achieved r = 0.64 (MAE = 1.18, RMSE = 1.40), comparable to six empirical methods, and on a 17‑drug set it outperformed all empirical approaches (r = 0.94, MAE = 0.38, RMSE = 0.52); its physical basis, predictive accuracy, 1–2 s per molecule runtime, and 3D graphics support establish iLOGP as a promising drug‑design tool.

Abstract

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.