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

Abstract:

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.

journal_name

J Pharm Pharm Sci

authors

Guangli M,Yiyu C

subject

Has Abstract

pub_date

2006-01-01 00:00:00

pages

210-21

issue

2

issn

1482-1826

journal_volume

9

pub_type

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