Predicting human liver microsomal stability with machine learning techniques.
about
An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis SoftwareComputational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomesMetabolism-directed structure optimization of benzimidazole-based Francisella tularensis enoyl-reductase (FabI) inhibitors.Quantitative structure-pharmacokinetic relationships.Automatically updating predictive modeling workflows support decision-making in drug design.A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.MetStabOn-Online Platform for Metabolic Stability Predictions.In Silico ADME/Tox Predictions
P2860
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P2860
Predicting human liver microsomal stability with machine learning techniques.
description
2007 nî lūn-bûn
@nan
2007 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Predicting human liver microsomal stability with machine learning techniques.
@ast
Predicting human liver microsomal stability with machine learning techniques.
@en
Predicting human liver microsomal stability with machine learning techniques.
@nl
type
label
Predicting human liver microsomal stability with machine learning techniques.
@ast
Predicting human liver microsomal stability with machine learning techniques.
@en
Predicting human liver microsomal stability with machine learning techniques.
@nl
prefLabel
Predicting human liver microsomal stability with machine learning techniques.
@ast
Predicting human liver microsomal stability with machine learning techniques.
@en
Predicting human liver microsomal stability with machine learning techniques.
@nl
P2093
P1476
Predicting human liver microsomal stability with machine learning techniques.
@en
P2093
Kaoru Shimada
Kazunari Hattori
Misaki Suzuki
Takashi Moriya
Teruki Honma
Yojiro Sakiyama
P304
P356
10.1016/J.JMGM.2007.06.005
P50
P577
2007-06-27T00:00:00Z