about
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular dockingA machine learning-based method to improve docking scoring functions and its application to drug repurposingMechanistic insights into mode of action of novel natural cathepsin L inhibitors.Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity.Quantitative structure-activity relationship analysis of β-amyloid aggregation inhibitors.DemQSAR: predicting human volume of distribution and clearance of drugs.Machine learning methods in chemoinformaticsIdentification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein bindingHologram QSAR models of a series of 6-arylquinazolin-4-amine inhibitors of a new Alzheimer's disease target: dual specificity tyrosine-phosphorylation-regulated kinase-1A enzyme.Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin.Fragment based G-QSAR and molecular dynamics based mechanistic simulations into hydroxamic-based HDAC inhibitors against spinocerebellar ataxia.Materials Informatics: Statistical Modeling in Material Science.Severity of thought disorder predicts psychosis in persons at clinical high-risk.Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methodsThree dimensional quantitative structure-activity relationship of 5H-Pyrido[4,3-b]indol-4-carboxamide JAK2 inhibitors.3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparumPredicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D).Prediction of the effect of formulation on the toxicity of chemicals.Chemical predictive modelling to improve compound quality.Development and Testing of an LED-Based Near-Infrared Sensor for Human Kidney Tumor Diagnostics.Development of structure-activity relationship for metal oxide nanoparticles.High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm.Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita.New QSAR Models for Human Cytochromes P450, 1A2, 2D6 and 3A4 Implicated in the Metabolism of Drugs. Relevance of Dataset on Model Development.Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses.Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors.CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.Modeling Sequence-Dependent Peptide Fluctuations in Immunologic Recognition.Molecular Modeling on Berberine Derivatives toward BuChE: An Integrated Study with Quantitative Structure-Activity Relationships Models, Molecular Docking, and Molecular Dynamics Simulations.3D-QSAR predictions for bovine serum albumin-water partition coefficients of organic anions using quantum mechanically based descriptors.Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.QSAR studies for prediction of cross-β sheet aggregate binding affinity and selectivityMechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling ApproachHuman intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactionsQuantitative structure-retardation factor relationship of protein amino acids in different solvent mixtures for normal-phase thin-layer chromatography.Machine learning estimates of natural product conformational energies.
P2860
Q28276262-92AD77BB-3ADB-42CA-B20A-0A4DC78238E8Q28304560-BA8D8DA3-9470-401E-BF85-E574A6F6F3E8Q30579667-A4EE4282-9DCD-4A14-821C-1D95CB72037DQ33535332-C3735611-D887-4B16-B20A-BA829002A162Q33729965-53056F7A-F5FC-430A-BBF6-8DDC97B88C35Q33774384-AA3A722D-C599-4325-96D0-8B013E6FD503Q34078930-6444DE8C-C190-4F1F-B3DA-08490B82F9C9Q34271118-0A2DD3CB-E044-465F-BF0E-AD3D8E7A69F4Q34740499-74598A09-163F-4823-A8CC-70D61EFC789CQ35186490-A0C97DB4-AB26-417A-9D8C-B60E0D97F6F4Q35380915-2696AC16-E354-4FF5-A2B3-3D2B852AC416Q35529563-B70878C0-6D15-4A38-87FC-18186AC91ED1Q35824698-0CD14D60-A654-43A8-8CC8-A6A64A1B2E30Q36199516-DDE7A5E0-4F43-4502-81DF-7517F54CE9CAQ36377966-436F1C63-D83C-4FC2-A086-AA1AE29DF96AQ36746899-6BF4C623-CF9B-4762-A92B-912530D598D5Q37007281-C68667F9-6DD9-482F-9322-4A1850F5733BQ37185725-147BC455-AF1D-4D45-AFF5-4DA8CCBC3451Q37393936-D8473420-301A-4C50-8B14-A709D0F0B044Q37643371-9E9DEE47-E7DD-4E5F-AC07-AC45403A9B49Q38167262-15A88F36-D749-41B7-9F95-0BB66FF11792Q38616316-F357A804-7B05-4DE4-91B4-E541722E4B2CQ39149585-F880646F-5074-4BF8-ACA2-E2F40A75175FQ39389810-7A3521BD-2E6A-4C9C-9FCD-26B6EE1C4641Q39425631-3C1F6FD2-CB46-4134-B9A9-1B15A89A98F8Q39520117-78F1BE87-EC22-4C15-9E97-190B0E617934Q39535158-6E4CA9C7-E437-4FBA-BE9F-CAF22EFD75EBQ39537106-265C47DC-B07D-4CA9-91C3-D4DD6E231844Q39550895-B2C84F81-FE96-4F01-A18C-AF33D1381EE1Q39551485-3A7CCA82-0197-41B6-8A78-0ECEC2956ACAQ39867617-A1ED1D9F-FC96-4477-9EA0-DAC23E1AD145Q40127497-8DE1F5C7-08AF-44D3-ACF4-18335696D7ABQ40230831-AB8544A4-B76C-4BDB-B6EB-0D1E3665874CQ40430763-2784AC23-A956-460A-ADDC-82E4505AC0E6Q40469636-548BEDF3-A947-40D4-A05E-2B9FAD857309Q40998017-B9A3EA6D-1FB0-4BEC-8B43-47B70079982EQ41103388-6915B08C-3F37-43B3-8E57-1D5F7C49D5F8Q41172510-FF26917A-CAF1-4ADC-B02D-BAAA0BD8C15AQ41364462-C04B0FF0-4FE3-4E7C-A269-51CA3D4A63F1Q41876045-E5CFF632-8551-4F9A-A23D-992E451F0452
P2860
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
2007年學術文章
@zh-hant
name
y-Randomization and its variants in QSPR/QSAR.
@en
y-Randomization and its variants in QSPR/QSAR.
@nl
type
label
y-Randomization and its variants in QSPR/QSAR.
@en
y-Randomization and its variants in QSPR/QSAR.
@nl
prefLabel
y-Randomization and its variants in QSPR/QSAR.
@en
y-Randomization and its variants in QSPR/QSAR.
@nl
P356
P1476
y-Randomization and its variants in QSPR/QSAR.
@en
P2093
Christoph Rücker
Gerta Rücker
P304
P356
10.1021/CI700157B
P577
2007-09-20T00:00:00Z