梁剛荐
Overview
| Works: | 10 works in 6 publications in 2 languages | |
|---|---|---|
Titles
In silico approaches to identify activators against AMP activated protein kinase (AMPK) /
by:
梁剛荐; Leong, Max K.; Perumal, Malliga
(Language materials, printed)
使用Hierarchical Support Vector Regression方法預測血腦屏障的滲透表面積乘積 = = Prediction of Permeability Surface Area Product of Blood Brain Barrier Using Hierarchical Support Vector Regression /
by:
梁剛荐; 詹奇君; Max-K Leong
(Language materials, printed)
使用Pharmacophore Ensemble/Support Vector Machine (PhE/SVM)模型來預測Breast Cancer Resistance Protein (BCRP/ABCG2)的抑制 = = Use of an In Silico Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) Model to Predict the Inhibition of Breast Cancer Resistance Protein (BCRP/ABCG2) /
by:
梁剛荐; Leong, Max K.; 丁逸龍
(Language materials, printed)
利用Hierarchical Support Vector Regression預測正辛醇 - 水分配係數 = = Prediction of n-Octanol-Water partition coefficient by Hierarchical Support Vector Regression /
by:
顏瑞庭; 梁剛荐; Leong, Max K.
(Language materials, printed)
使用Hierarchical Support Vector Regression發展Quantiative Structure Activity Relationship模型預測芳香族硝基化合物的致突變 = = Development of a Quantiative Structure Activity Relationship Model to Predict Mutagenicity of Atomatic Nitro Based on Hierarchical Support Vector Regression /
by:
呂侑宸; 梁剛荐; Lu, You-Chen; Leong, Max K.
(Language materials, printed)
使用Support Vector Machine發展 SVM-Pose/SVM-Score Ensemble Docking 應用於預測N-methyl-D-aspartate的生物活性 = = Development of a Novel Pose Selection Scheme Based on Support Vector Machine for Molecular Docking:Application to NMDA NR1 Ligand Binding /
by:
許人貴; 梁剛荐; Syu, Ren-Guei; Leong, Max K.
(Language materials, printed)
In silico prediction of Caco-2 permeability by quantitative structure activity relationship modeling based on hierarchical support vector regression /
by:
梁剛荐; Leong, Max-K.; 張欽雄
(Language materials, printed)
利用階層式支持向量回歸法預測空腸的滲透率 = = In Silico Prediction of Jejunum Permeability by Hierarchical Support Vector Regression /
by:
李明翰; 梁剛荐; Leong, Max K.
(Language materials, printed)
In silico prediction of n-Octanol–Water partition coefficient by various data fusion methods /
by:
羅日彥; 梁剛荐; Leong, Max K.
(Language materials, printed)
Subjects
定量構效關係
Intestinal permeability
階層式支持向量回歸
data fusion
乳腺癌抗性蛋白 (BCRP)
腸道滲透率
active transport
Breast cancer resistance protein (BCRP)
Support vector machine (SVM)
大鼠
Hierarchical support vector regression
pharmacophore ensemble/support vector machine (PhE/SVM)
空腸
BDDCS
Quantitative structure-activity relationship
machine learning
QSAR
分配系數
支援向量機 (SVM)
Pharmacophore ensemble/Support vector machine (PhE/SVM)
Rat
human colon carcinoma monolayer cell (Caco-2)
hierarchical support vector regression
Pharmacophore
階層式支持向量回歸法
passive diffusion
單向腸道灌注法
生物藥物處置分類系統
Jejunum
Single-pass intestinal perfusion
Partition coefficient
drug absorption
Caco-2 permeability