On two novel parameters for validation of predictive QSAR models.
about
Chemical Structure-Biological Activity Models for Pharmacophores' 3D-InteractionsHomoSAR: bridging comparative protein modeling with quantitative structural activity relationship to design new peptides.Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart.European Chemicals Agency dossier submissions as an experimental data source: refinement of a fish toxicity model for predicting acute LC50 values.Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.Advances in computational methods to predict the biological activity of compounds.Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.The using of a piglets as a model for evaluating the dipyrone hematological effects.QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors.Advances in quantitative structure-activity relationship models of antimalarials.Advances in quantitative structure-activity relationship models of antioxidants.Advances in quantitative structure-activity relationship models of anti-Alzheimer's agents.QSAR studies in the discovery of novel type-II diabetic therapies.Studies of the benzopyran class of selective COX-2 inhibitors using 3D-QSAR and molecular docking.QSAR models of antiproliferative activity of imidazo[2,1-b][1,3,4]thiadiazoles in various cancer cell lines.Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing.A structure-activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter.Hologram quantitative structure-activity relationship and comparative molecular interaction field analysis of aminothiazole and thiazolesulfonamide as reversible LSD1 inhibitors.The advancement of multidimensional QSAR for novel drug discovery - where are we headed?Structure-based quantitative structure--activity relationship modeling of estrogen receptor β-ligands.QSAR, DFT and molecular modeling studies of peptides from HIV-1 to describe their recognition properties by MHC-I.Exploring structural requirements for peripherally acting 1,5-diaryl pyrazole-containing cannabinoid 1 receptor antagonists for the treatment of obesity.Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.Structural requirements for potential HIV-integrase inhibitors identified using pharmacophore-based virtual screening and molecular dynamics studies.Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents.In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers.Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose.Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals.Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening.Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols.QSAR analysis of benzophenone derivatives as antimalarial agents.How good are ensembles in improving QSAR models? The case with eCoRIA.Combined 3D-QSAR, molecular docking, molecular dynamics simulation, and binding free energy calculation studies on the 5-hydroxy-2H-pyridazin-3-one derivatives as HCV NS5B polymerase inhibitors.On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties.QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis.Synthesis, antifeedant activity against Coleoptera and 3D QSAR study of alpha-asarone derivatives.An in silico exploration of the interaction mechanism of pyrazolo[1,5-a]pyrimidine type CDK2 inhibitors.Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.).Molecular modeling studies on benzimidazole carboxamide derivatives as PARP-1 inhibitors using 3D-QSAR and docking.
P2860
Q26741313-D0CFBE0D-FB57-419D-B0D1-4D9C3149FAF1Q30009545-BD5B052B-A322-4F63-820F-11CD75B5E944Q30680780-A940C163-0B12-4095-A111-E38EEE8CB2B8Q30874127-5D8ADB85-CBD8-4883-BAF1-6B2253B61E86Q33458433-ABE0B609-A8DD-489B-8FD2-CC82E829B196Q34289760-B532EE3C-E6A3-44DF-B9A5-EF7975AF07ECQ35218171-5A31853B-08B7-4B24-98A9-BEE91E6CB11BQ36203571-89057560-54B0-448C-A33E-E10F329BBAE7Q36404536-09378FE6-17D0-4917-AA4B-5AB3B23DC92AQ38029140-0723DAE4-E450-4B5F-B26A-71C6939D9C98Q38088335-F7E92717-4C6A-45E0-93D1-363F1E8AF0B5Q38206286-78527237-D8BF-4046-8652-2446BEA3E834Q38630598-A517EB63-4B63-47CF-BBD5-2F995B311564Q38640162-D5CA5C00-DA24-4A02-861C-88A5CD86B299Q38741806-449A3F72-6552-48BA-80F0-433BDC1B3F22Q38882213-742B8744-88C5-46F2-A8C2-DA462BB5CFF6Q38920552-8B8185BC-B726-446B-BE4A-1F41C6C919F3Q38979020-ABF74242-E73A-444A-9D54-7DC9BADF6D37Q39340690-091734B3-5DAF-4796-B33D-484FB41FB392Q39735325-A934E66E-020C-4004-B521-01433926A55DQ40110552-EE9653BF-0CD8-4B92-961B-EE0B7446DC1FQ40726993-C27D55F9-41DB-410C-B406-8F82B71905A6Q40752848-B6F3E8E4-97AE-455A-8E83-C9D03E676220Q40818771-BA62BE3B-83C8-470F-9068-56C55056AB82Q40859217-30BDBF55-406A-48A8-A2D9-457F5DFFD9E2Q41100646-B743D12E-484B-4912-9E69-7797247EACF0Q41365161-C76E7A81-9B88-438C-8A6E-CD435C7A9852Q41514243-03C346E7-FEBB-4F1C-8B80-73B4BB9B111FQ41681901-5FCC33E0-98E0-45A3-A3DA-74716BD5A82AQ42083463-AEF9BB1D-5582-4C8D-A9AD-ACCE219AEFE8Q42124833-7AE32699-D494-482B-9909-8C677EFFD255Q42222984-C0F673ED-1020-44A7-8ED5-F694E74FBB01Q42276983-CC2F581F-896D-446B-AA08-CC80A7AE907EQ44372955-35A94BD6-2553-4DDB-B909-2643E07444F5Q44451284-7507C8F2-81C6-4009-B133-F0A9F5A5505EQ45074793-CF66C73D-0D61-4F9B-A482-8178EBC9DFEFQ45327842-13E0C921-F49C-4BDF-A94B-135792C8C448Q45950000-69AF93F1-C451-4944-A8DA-6D60DDFB97C8Q46275491-44D950F9-006B-4CE9-B47A-8F1D8A116B1AQ46485114-B4FE0744-923F-4BA3-8E6E-213397DD227F
P2860
On two novel parameters for validation of predictive QSAR models.
description
2009 nî lūn-bûn
@nan
2009 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի ապրիլին 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
On two novel parameters for validation of predictive QSAR models.
@ast
On two novel parameters for validation of predictive QSAR models.
@en
On two novel parameters for validation of predictive QSAR models.
@nl
type
label
On two novel parameters for validation of predictive QSAR models.
@ast
On two novel parameters for validation of predictive QSAR models.
@en
On two novel parameters for validation of predictive QSAR models.
@nl
prefLabel
On two novel parameters for validation of predictive QSAR models.
@ast
On two novel parameters for validation of predictive QSAR models.
@en
On two novel parameters for validation of predictive QSAR models.
@nl
P2093
P1433
P1476
On two novel parameters for validation of predictive QSAR models
@en
P2093
Indrani Mitra
Partha Pratim Roy
Somnath Paul
P304
P356
10.3390/MOLECULES14051660
P407
P50
P577
2009-04-29T00:00:00Z