In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.
Int J Mol Sci
; 21(10)2020 May 19.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1934080
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.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Máquina de Vectores de Soporte
/
Intestinos
Tipo de estudio:
Estudio pronóstico
Límite:
Animales
/
Humanos
Idioma:
Inglés
Año:
2020
Tipo del documento:
Artículo
País de afiliación:
Ijms21103582
Similares
MEDLINE
...
LILACS
LIS