Predicting ADME properties in silico: methods and models.
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Pharmacointeraction network models predict unknown drug-drug interactionsInhibitor ranking through QM based chelation calculations for virtual screening of HIV-1 RNase H inhibitionOpen Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery DatasetsData mining of solubility parameters for computational prediction of drug-excipient miscibility.Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework.Discovery of estrogen receptor modulators: a review of virtual screening and SAR efforts.Enone- and chalcone-chloroquinoline hybrid analogues: in silico guided design, synthesis, antiplasmodial activity, in vitro metabolism, and mechanistic studies.A concise review of applications of micellar liquid chromatography to study biologically active compounds.Prioritizing Hits with Appropriate Trade-Offs Between HIV-1 Reverse Transcriptase Inhibitory Efficacy and MT4 Blood Cells Toxicity Through Desirability-Based Multiobjective Optimization and Ranking.Identification 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.Computational prediction of human drug metabolism.Modeling kinetics of subcellular disposition of chemicals.QSAR of cytochrome inhibitors.Pros and cons of methods used for the prediction of oral drug absorption.Drug-target interaction prediction via chemogenomic space: learning-based methods.In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans.The virtual cell based assay: Current status and future perspectives.In Silico Analysis to Compare the Effectiveness of Assorted Drugs Prescribed for Swine flu in Diverse Medicine SystemsExploring the multifunctionality of thioflavin- and deferiprone-based molecules as acetylcholinesterase inhibitors for potential application in Alzheimer's disease.Drug-target and disease networks: polypharmacology in the post-genomic era.Modeling Pharmacokinetics.A modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity.Predicting MDCK cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis.Developing imidazole analogues as potential inhibitor for Leishmania donovani trypanothione reductase: virtual screening, molecular docking, dynamics and ADMET approach.Predictive models for maximum recommended therapeutic dose of antiretroviral drugs.Protein-ligand interaction prediction: an improved chemogenomics approach.Febrifugine analogues as Leishmania donovani trypanothione reductase inhibitors: binding energy analysis assisted by molecular docking, ADMET and molecular dynamics simulation.Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization.Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing.Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.Learning a Local-Variable Model of Aromatic and Conjugated Systems.Probing voltage sensing domain of KCNQ2 channel as a potential target to combat epilepsy: a comparative study.Pharmacophore modeling, comprehensive 3D-QSAR, and binding mode analysis of TGR5 agonists.Discovery of a potential lead compound for treating leprosy with dapsone resistance mutation in M. leprae folP1.Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease.In Silico ADME/Tox Predictions
P2860
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P2860
Predicting ADME properties in silico: methods and models.
description
2002 nî lūn-bûn
@nan
2002 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
Predicting ADME properties in silico: methods and models.
@ast
Predicting ADME properties in silico: methods and models.
@en
Predicting ADME properties in silico: methods and models.
@nl
type
label
Predicting ADME properties in silico: methods and models.
@ast
Predicting ADME properties in silico: methods and models.
@en
Predicting ADME properties in silico: methods and models.
@nl
prefLabel
Predicting ADME properties in silico: methods and models.
@ast
Predicting ADME properties in silico: methods and models.
@en
Predicting ADME properties in silico: methods and models.
@nl
P2093
P1433
P1476
Predicting ADME properties in silico: methods and models.
@en
P2093
Darko Butina
Katrina Frankcombe
Matthew D Segall
P356
10.1016/S1359-6446(02)02288-2
P577
2002-06-01T00:00:00Z