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10.3390/ijms21103582 http://scihub22266oqcxt.onion/10.3390/ijms21103582 32438630!7279352!32438630     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Mol+Sci 2020 ; 21 (10): ä Nephropedia Template TP {{tp|p=32438630|t=2020. In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression |pdf=|usr=}}{{32438630}} gab.com Text In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression JO: Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=32438630 #FOAvip 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, q CV 2 = 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. Twit Text FOAVip In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression ????Int J Mol Sci http://www.kidney.de/pget.php?&tw=1&pmid=32438630 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32438630 #FOAvip English Wikipedia <ref>{{Cite pmid|32438630}}</ref> In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression #MMPMID32438630 Lee MH; Ta GH; Weng CF; Leong MK Int J Mol Sci 2020[May]; 21 (10): ä PMID32438630show ga 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, q CV 2 = 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. |*Computer Simulation[MESH] |*Support Vector Machine[MESH] |Animals[MESH] |Humans[MESH] |Intestines/*physiology[MESH] |Permeability[MESH] |Rats[MESH] |Regression Analysis[MESH]In Silico Prediction of Intestinal Permeability by Hierarchical Suppor(epmc).pdf DeepDyve Pubget Overpricing