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PubMed · 16959190

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

Maria Guangli·Cheng Yiyu
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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.

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Caco-2 CellsCell Membrane PermeabilityComputer SimulationHumansModels, BiologicalMolecular StructurePermeabilityPredictive Value of TestsSoftwareJournal ArticleResearch Support, Non-U.S. Gov't

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Maria Guangli, Cheng Yiyu. 2006. Predicting Caco-2 permeability using support vector machine and chemistry development kit.. https://pubmed.ncbi.nlm.nih.gov/16959190/

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