A fully computational model for predicting percutaneous drug absorption.
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 k(p) 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 k(p)).
Neumann
Dirk
D
Center for Bioinformatics Saar, Bldg. 36.1, Saarland University, Saarbrücken, Germany. d.neumann@bioinf.uni-sb.de
Kohlbacher
Oliver
O
Merkwirth
Christian
C
Lengauer
Thomas
T
D000818:Animals; D003198:Computer Simulation; D015195:Drug Design; D010539:Permeability; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012869:Skin Absorption; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations