Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection.
about
In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2)Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart.Predicting disease trait with genomic data: a composite kernel approach.Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.In silico prediction of human serum albumin binding for drug leads.The Development of CK2 Inhibitors: From Traditional Pharmacology to in Silico Rational Drug Design.Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitorsStructure-based design of novel combinatorially generated NBTIs as potential DNA gyrase inhibitors against various Staphylococcus aureus mutant strains.Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking SchemeCombined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists.Natural products as leads in schistosome drug discovery.Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors.Quantum chemistry in environmental pesticide risk assessment.Studies of Staphylococcus aureus FabI inhibitors: fragment-based approach based on holographic structure-activity relationship analyses.Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment.Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors.A structure-activity relationship study of the toxicity of ionic liquids using an adapted Ferreira-Kiralj hydrophobicity parameter.Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models.Ecotoxicity interspecies QAAR models from Daphnia toxicity of pharmaceuticals and personal care products.A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR.QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae.3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods.Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches.QSAR models for antioxidant activity of new coumarin derivatives.Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches.Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking studyDevelopment of validated quantitative structure-retention relationship models for retention indices of plant essential oils.Performance of (Q)SAR models for predicting Ames mutagenicity of aryl azo and benzidine based compounds.Prediction of antiprion activity of therapeutic agents with structure-activity models.Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface watersOn the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity.QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.Predictive QSAR Models for the Toxicity of Disinfection Byproducts.Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach.Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants.A quantitative property-property relationship for the internal diffusion coefficients of organic compounds in solid materials.Quantitative insight into the design of compounds recognized by the L-type amino acid transporter 1 (LAT1).
P2860
Q28540667-893DCB4F-1BA7-467E-85C9-6AB411152310Q30680780-74BD4963-6E5D-4113-9AB2-C81AAB406056Q31105144-62808984-275E-405E-94AF-B4FE8B754B3FQ33458433-F26B1FDF-0EA1-4B02-82E5-C5F6862DB665Q34609144-8BADEE94-9B0F-414A-A7A9-8DCF7E7CFFB5Q36288208-5937E066-190C-4733-8EBC-73F65CAFB40DQ36329533-2637F2E7-2ED8-47F4-B6EF-6B0DEFB955FCQ36394817-E278B017-6E87-4AAB-A39A-EF191B9FFC19Q37563518-658BD4AB-71D7-4B5F-AC52-34CF25EDC394Q37672962-628112E0-77C6-470D-9CAB-815013B26A48Q38334179-C0865A43-BF55-4354-A899-1C5650FE3BF3Q38386043-CF44F777-BC85-4505-B7A2-BE9D45772E31Q38668657-33AA22E3-8C53-4F4B-94C3-A219D2B2B239Q38724099-B0FDA4E0-6714-4730-A054-19793D67B37EQ38765020-E1D891C4-30C2-4F43-8610-A36B5980CCBAQ38832855-FDB10529-02F6-4704-8FBD-6DB207A051EBQ38838799-ADFDAC44-CD20-49EC-B188-3A4A959ABBC4Q38920552-01A87042-1A83-4C79-8407-11301CECD1E6Q38943960-3E57886A-E239-4F48-87E5-3E2D0DEB4F3FQ39255331-2B69F829-AA58-4062-847E-70D7965FAEF2Q39410504-60AAA171-A17D-4A5D-821C-2A803CA08FD7Q39540764-B4C764FD-3AF2-4E0A-8517-03BD8740F754Q39733954-16DE0C11-5AE7-4603-82D7-4044622F320FQ40015462-CBAE04C5-2F9E-4EDF-AAA0-F70283A6A56DQ40427676-BB53534B-A089-45D0-A2A3-B4E777412633Q40469636-F02E6D56-18C0-42EA-8ABE-06B5979C3C2EQ40752848-BA35BAC3-CBCE-4182-90A9-1810D746B95AQ40823345-30912A02-6FCE-4E7F-9381-2F599570B602Q41361567-CDEDF53C-5AA3-40D2-AD3D-A19C4DC2CFB7Q43642130-AF1DCA6C-C8BB-4511-90F7-3C6373D0802AQ43649059-480DECB9-ACEF-4B80-BB22-A7F1B73347D2Q43966734-08F31190-94A8-4C12-B401-8FA2B019140CQ47153508-08EE3B03-5128-45AC-A12E-F99BC51F690FQ47297106-67A8DF08-23F3-4372-8937-2952119748E7Q47580799-94DB9880-E3A4-4676-8B5A-F0194DA62A72Q47588412-2C360FB2-AA01-40EC-BA21-DA402EBA7606Q47882424-8284ED54-7BCC-4B1B-89F4-28D37CE93F13Q47927557-F1B87355-A0E0-4432-A1D4-2FF59346F98EQ48370618-8A5A302A-417F-4F48-BC02-71120528F9A3Q48554104-EFB28A2E-F58E-4D78-8C74-FE5D8E5B6E34
P2860
Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection.
description
2012 nî lūn-bûn
@nan
2012 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Real external predictivity of ...... d for scatter plot inspection.
@ast
Real external predictivity of ...... d for scatter plot inspection.
@en
Real external predictivity of ...... d for scatter plot inspection.
@nl
type
label
Real external predictivity of ...... d for scatter plot inspection.
@ast
Real external predictivity of ...... d for scatter plot inspection.
@en
Real external predictivity of ...... d for scatter plot inspection.
@nl
prefLabel
Real external predictivity of ...... d for scatter plot inspection.
@ast
Real external predictivity of ...... d for scatter plot inspection.
@en
Real external predictivity of ...... d for scatter plot inspection.
@nl
P356
P1476
Real external predictivity of ...... d for scatter plot inspection.
@en
P2093
Nicola Chirico
Paola Gramatica
P304
P356
10.1021/CI300084J
P577
2012-07-13T00:00:00Z