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Abstract

The prediction of transdermal absorption for arbitrary penetrant structures has
several important applications in the pharmaceutical industry. We propose a new
data-driven, predictive model for skin permeability coefficients kp based on an
ensemble model using k-nearest-neighbor models and ridge regression. The model
was trained and validated with a newly assembled data set containing
experimental data and structures for 110 compounds. On the basis of three
purely computational descriptors (molecular weight, calculated octanol/water
partition coefficient, and solvation free energy), we have developed a model
allowing for the reliable, purely computational prediction of skin permeability
coefficients. The model is both accurate and robust, as we showed in an
extensive validation (correlation coefficient for leave-one-out cross
validation: Q = 0.948, mean standard error: 0.2 for log kp).