In silico approaches for predicting ADME properties of drugs.
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Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery DatasetsIdentification of novel acetylcholinesterase inhibitors through e-pharmacophore-based virtual screening and molecular dynamics simulations.Structure-based screening, ADMET profiling, and molecular dynamic studies on mGlu2 receptor for identification of newer antiepileptic agents.Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies.Modeling and Simulation of Intracellular Drug Transport and Disposition Pathways with Virtual Cell.Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions.QSAR of cytochrome inhibitors.Three-dimensional bio-printing: A new frontier in oncology research.The right compound in the right assay at the right time: an integrated discovery DMPK strategy.Insights on cytochrome p450 enzymes and inhibitors obtained through QSAR studies.Pros and cons of methods used for the prediction of oral drug absorption.Identification of potential inhibitors for AIRS from de novo purine biosynthesis pathway through molecular modeling studies - a computational approach.Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties.In Silico Analysis to Compare the Effectiveness of Assorted Drugs Prescribed for Swine flu in Diverse Medicine SystemsDrug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools.QSAR Prediction of Passive Permeability in the LLC-PK1 Cell Line: Trends in Molecular Properties and Cross-Prediction of Caco-2 Permeabilities.Identification of Novel Inhibitors for Tobacco Mosaic Virus Infection in Solanaceae Plants.Developing imidazole analogues as potential inhibitor for Leishmania donovani trypanothione reductase: virtual screening, molecular docking, dynamics and ADMET approach.In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers.Separate evaluation of intestinal and hepatic metabolism of three benzodiazepines in rats with cannulated portal and jugular veins: comparison with the profile in non-cannulated mice.Applying quantitative structure-activity relationship (QSAR) methodology for modeling postmortem redistribution of benzodiazepines and tricyclic antidepressants.Febrifugine analogues as Leishmania donovani trypanothione reductase inhibitors: binding energy analysis assisted by molecular docking, ADMET and molecular dynamics simulation.Combining in vitro and in silico approaches to find new drugs targeting the pathological proteins related to the Alzheimer's disease.Design, Synthesis, and Docking Study of Pyrimidine-Triazine Hybrids for GABA Estimation in Animal Epilepsy Models.Quantitative structure/activity relationship modelling of pharmacokinetic properties using genetic algorithm-combined partial least squares method.In Silico ADME/Tox Predictions
P2860
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P2860
In silico approaches for predicting ADME properties of drugs.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
In silico approaches for predicting ADME properties of drugs.
@ast
In silico approaches for predicting ADME properties of drugs.
@en
type
label
In silico approaches for predicting ADME properties of drugs.
@ast
In silico approaches for predicting ADME properties of drugs.
@en
prefLabel
In silico approaches for predicting ADME properties of drugs.
@ast
In silico approaches for predicting ADME properties of drugs.
@en
P356
P1476
In silico approaches for predicting ADME properties of drugs.
@en
P2093
Fumiyoshi Yamashita
Mitsuru Hashida
P304
P356
10.2133/DMPK.19.327
P577
2004-10-01T00:00:00Z