In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.
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Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficientsApplying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drugComprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state.DemQSAR: predicting human volume of distribution and clearance of drugs.Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization.BDDCS class prediction for new molecular entities.pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures.Prediction of volume of distribution at steady state in humans: comparison of different approaches.The right compound in the right assay at the right time: an integrated discovery DMPK strategy.Getting the MAX out of Computational Models: The Prediction of Unbound-Brain and Unbound-Plasma Maximum Concentrations.Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning.Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models.Monographs on drugs which are frequently analyzed in therapeutic drug monitoring/Arzneimittel-Monographien für Medikamente, die regelmäßig im Rahmen des Therapeutic Drug Monitorings analysiert werdenEnantiomeric pairs reveal that key medicinal chemistry parameters vary more than simple physical property based models can explain
P2860
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P2860
In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.
description
2009 nî lūn-bûn
@nan
2009 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
In silico prediction of volume ...... ls on a 669 compound data set.
@ast
In silico prediction of volume ...... ls on a 669 compound data set.
@en
type
label
In silico prediction of volume ...... ls on a 669 compound data set.
@ast
In silico prediction of volume ...... ls on a 669 compound data set.
@en
prefLabel
In silico prediction of volume ...... ls on a 669 compound data set.
@ast
In silico prediction of volume ...... ls on a 669 compound data set.
@en
P2093
P356
P1476
In silico prediction of volume ...... ls on a 669 compound data set.
@en
P2093
Clayton Springer
Franco Lombardo
Giuliano Berellini
P304
P356
10.1021/JM9004658
P407
P577
2009-07-01T00:00:00Z