Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 21, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/ijms21103582
Keywords
intestinal permeability; passive diffusion; active transport; in silico; quantitative structure-activity relationship; hierarchical support vector regression
Funding
- Ministry of Science and Technology, Taiwan
Ask authors/readers for more resources
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, qCV2 = 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available