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In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.

Abstract：

: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, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 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.

journal_name

Int J Mol Sci

journal_title

International journal of molecular sciences

authors

Lee MH,Ta GH,Weng CF,Leong MK

doi

10.3390/ijms21103582

subject

Has Abstract

pub_date

2020-05-19 00:00:00

issue

10

issn

1422-0067

pii

ijms21103582

journal_volume

21

pub_type

杂志文章

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