Predicting Caco-2 permeability using support vector machine and chemistry development kit.

PURPOSE: To predict Caco-2 permeability is a valuable target for pharmaceutical research. Most of the Caco-2 prediction models are based on commercial or special software which limited their practical value. This study represents the relationship between Caco-2 permeability and molecular descriptors totally based on open source software.
METHODS: The Caco-2 prediction model was constructed based on descriptors generated by open source software Chemistry Development Kit (CDK) and a support vector machine (SVM) method. Number of H-bond donors and three molecular surface area descriptors constructed the prediction model.
RESULTS: The correlation coefficients (r) of the experimental and predicted Caco-2 apparent permeability for the training set and the test set were 0.88 and 0.85, respectively.
CONCLUSION: The results suggest that the SVM method is effective for predicting Caco-2 permeability. Membrane permeability of compounds is determined by number of H-bond donors and molecular surface area properties.