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Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR modelHow drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusionStructural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR.KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterialsUnbiased descriptor and parameter selection confirms the potential of proteochemometric modellingMethods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARsMolecular ChemometricsTowards interoperable and reproducible QSAR analyses: Exchange of datasetsProteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small moleculesInterpretable correlation descriptors for quantitativestructure-activity relationshipsPrediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling.Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small moleculesReliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validationDPRESS: Localizing estimates of predictive uncertaintyQSAR modeling: where have you been? Where are you going to?Quantitative nanostructure-activity relationship modelingPredicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compoundsProteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitorsSignificantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram dataInsight into the interactions between novel isoquinolin-1,3-dione derivatives and cyclin-dependent kinase 4 combining QSAR and molecular dockingQuantitative structure-property relationship (QSPR) modeling of drug-loaded polymeric micelles via genetic function approximationTemplate CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated AffinitiesTrust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling researchConformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous CompoundsModeling protein-peptide recognition based on classical quantitative structure-affinity relationship approach: implication for proteome-wide inference of peptide-mediated interactions.Development of QSAR-Improved Statistical Potential for the Structure-Based Analysis of ProteinPeptide Binding Affinities.Proteochemometric modeling in a Bayesian framework.Structural determinants of Tau aggregation inhibitor potencyMutatomics analysis of the systematic thermostability profile of Bacillus subtilis lipase A.Comparative QSAR analyses of competitive CYP2C9 inhibitors using three-dimensional molecular descriptors.New public QSAR model for carcinogenicity.Design, synthesis, binding and docking-based 3D-QSAR studies of 2-pyridylbenzimidazoles--a new family of high affinity CB1 cannabinoid ligands.Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase.Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart.An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data.Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot.Selection of data sets for QSARs: analyses of Tetrahymena toxicity from aromatic compounds.Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.Performance of multicomponent self-organizing regression (MCSOR) in QSAR, QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets.Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set.
P2860
Q23913000-03AA86B8-536B-4185-B4A9-8B05AECAF045Q24273350-30F051F9-28B1-44AB-A7EE-D9484A2EC6A0Q24537620-85EC3D70-8F0C-429B-BA11-15037A71F63CQ24621477-96753050-415B-4E87-BE0D-BBE1CBBA50EDQ24805088-E28E315B-12B9-4CE7-9A93-A7FAFD3E733FQ24815757-1B650CDF-EA1D-48AE-B28F-E0B18FC761ECQ27134682-6277EBCA-E17C-441D-A4E2-A794A6EB2F81Q27134746-EA7C0C7B-088F-4483-AA17-E80C3FD9084CQ27702464-CC7F4221-5E2E-4901-958E-E11D2BFDAC79Q27867880-877D782A-BFA9-4567-BB86-78E0B358D272Q27902278-4769716A-472C-4365-A39E-F9F4266259BAQ27902305-9B3F8353-F6C7-4679-A967-914B94907DBBQ27902330-127C35E4-5E35-44CE-BD57-3F01186EFFEEQ27998700-2E0F44F8-87C6-483D-AFB2-C5E03EF22FCDQ28222668-2C1CF138-8EA8-4009-8A94-D97108E3A2A6Q28385022-49C18EB6-17E9-4504-B9A2-1688CE45B242Q28386501-606F9DBB-0F73-4D2B-84BD-99589EB60A60Q28476537-35DE6E33-1DF9-49CC-BE4C-B0E7D8B7AC02Q28486299-EDE11DD1-2DA1-437F-B008-F6EFC397F11CQ28537720-1FE986E7-29F1-4A56-A0B0-E3B38ECE6340Q28544461-CEAE0F14-73EF-4A77-BFBF-9A6A3E7F0053Q28648497-99493955-6632-46A7-8025-F4BD54FAA4D6Q28748220-5D05D1DB-42B1-42D2-B58D-063A7A65D553Q28828821-47BCAE90-D5D3-4460-9E7C-8CD50BCA2850Q30009536-6E70F45E-E990-460D-BBB0-14CC56411780Q30009550-A05B4187-937A-4CD8-A64A-FD369D51ABABQ30086274-56ED7DFA-7603-44CB-8F4E-CEC76C4C7226Q30353954-F141BC6E-F2BC-4D61-A2F1-45A269FB95E5Q30362557-6E277D8E-0972-42EE-AC8E-8BFAC31008AEQ30401651-34478B1E-E872-4D96-9C86-CAE93DDB78C7Q30495818-48F1DF64-3A36-4446-A5DF-50C8ED4057C7Q30612575-F20A65FD-2944-46BB-855C-7A129B5833FFQ30620538-2F37486A-7031-4C39-933D-E6A9FD8B4E1AQ30680780-D29CA68C-7946-46E9-ABDE-B3CCB6DAD5A1Q30713511-7046DB35-770A-4262-8F77-3082119B10B5Q30731706-EA814E79-4685-435E-AB3D-8EE2CBB39F0FQ30784588-8142279F-133F-4665-A40D-A89D2E681290Q31019492-BE7A62E9-C2DD-44D7-A4DC-2073986A0457Q31084841-50BA7D9A-90D4-46CA-8F4F-856CA33A8580Q31119557-FE968FB4-0046-41EF-8C64-E9F2115E9FF7
P2860
description
2002 nî lūn-bûn
@nan
2002 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի հունվարին 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
Beware of q2!
@ast
Beware of q2!
@en
type
label
Beware of q2!
@ast
Beware of q2!
@en
prefLabel
Beware of q2!
@ast
Beware of q2!
@en
P3181
P1476
Beware of q2!
@en
P304
P3181
P356
10.1016/S1093-3263(01)00123-1
P577
2002-01-01T00:00:00Z