Author: Lee, Ming-Han; Ta, Giang Huong; Weng, Ching-Feng; Leong, Max K.
Title: In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression Cord-id: b6xandfh Document date: 2020_5_19
ID: b6xandfh
Snippet: The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descrip
Document: The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r(2) = 0.93, [Formula: see text] = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q(2) = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q(2) = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.
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