Best Practices for QSAR Model Development, Validation, and Exploitation
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How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusionChemoinformatics: Achievements and Challenges, a Personal ViewNanoinformatics: emerging databases and available toolsWeb tools for predictive toxicology model buildingEfficient and biologically relevant consensus strategy for Parkinson's disease gene prioritization.ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modelingeTOXlab, an open source modeling framework for implementing predictive models in production environmentsIntegrated Computational Solution for Predicting Skin Sensitization Potential of MoleculesA structure-based model for predicting serum albumin bindingExploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation AssaysIn Silico Discovery of Novel Potent Antioxidants on the Basis of Pulvinic Acid and Coumarine Derivatives and Their Experimental EvaluationA methodology for the design of experiments in computational intelligence with multiple regression modelsQSAR DataBank - an approach for the digital organization and archiving of QSAR model informationModeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type CompoundsComputer-aided design of carbon nanotubes with the desired bioactivity and safety profilesPublic (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future DevelopmentStructure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors.Deep learning for computational chemistry.An integrated approach with new strategies for QSAR models and lead optimization.Chembench: A Publicly Accessible, Integrated Cheminformatics Portal.Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase.From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions.Nonlinear dimensionality reduction for visualizing toxicity data: distance-based versus topology-based approaches.Tales from the war on error: the art and science of curating QSAR data.Predicting disease trait with genomic data: a composite kernel approach.Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation.Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set.Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells.Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).Chembench: a cheminformatics workbench.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.Development and implementation of (Q)SAR modeling within the CHARMMing web-user interfaceMachine-learning techniques applied to antibacterial drug discoveryScrutinizing MHC-I binding peptides and their limits of variationCombining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS project.The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding
P2860
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P2860
Best Practices for QSAR Model Development, Validation, and Exploitation
description
2010 nî lūn-bûn
@nan
2010 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Best Practices for QSAR Model Development, Validation, and Exploitation
@ast
Best Practices for QSAR Model Development, Validation, and Exploitation
@en
Best Practices for QSAR Model Development, Validation, and Exploitation
@nl
type
label
Best Practices for QSAR Model Development, Validation, and Exploitation
@ast
Best Practices for QSAR Model Development, Validation, and Exploitation
@en
Best Practices for QSAR Model Development, Validation, and Exploitation
@nl
prefLabel
Best Practices for QSAR Model Development, Validation, and Exploitation
@ast
Best Practices for QSAR Model Development, Validation, and Exploitation
@en
Best Practices for QSAR Model Development, Validation, and Exploitation
@nl
P921
P3181
P356
P1476
Best Practices for QSAR Model Development, Validation, and Exploitation
@en
P304
P3181
P356
10.1002/MINF.201000061
P577
2010-07-06T00:00:00Z