On the public citation graph
QSAR Study of Ethyl 2-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: An Inhibitor of AP-1 and NF-κB Mediated Gene Expression Based on Support Vector Machines
0 signed · 94 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bfb0052850openalex-snapshot
Network performance assessment for Neurofuzzy data modelling
inferredcites →https://doi.org/10.1016/1359-6446(96)10015-5openalex-snapshot
Transcription factors: a new frontier for drug discovery
inferredcites →https://doi.org/10.1016/s0014-5793(02)02835-1openalex-snapshot
Identifying genes related to drug anticancer mechanisms using support vector machine
inferredcites →https://doi.org/10.1016/s0097-8485(01)00113-9openalex-snapshot
Prediction of protein structural classes by support vector machines
inferredcites →https://doi.org/10.1016/s0169-7439(02)00046-1openalex-snapshot
A flexible classification approach with optimal generalisation performance: support vector machines
inferredcites →https://doi.org/10.1016/s0223-5234(99)80052-xopenalex-snapshot
Neural networks in drug discovery: have they lived up to their promise?
inferredcites →https://doi.org/10.1016/s0304-3800(01)00296-4openalex-snapshot
Support vector machines for identifying organisms — a comparison with strongly partitioned radial basis function networks
inferredcites →https://doi.org/10.1016/s0925-2312(01)00676-2openalex-snapshot
Modified support vector machines in financial time series forecasting
inferredcites →https://doi.org/10.1016/s0925-2312(02)00632-xopenalex-snapshot
Determination of the spread parameter in the Gaussian kernel for classification and regression
inferredcites →https://doi.org/10.1016/s0960-894x(00)00312-7openalex-snapshot
Novel inhibitors of AP-1 and NF-κB mediated gene expression: structure–activity relationship studies of ethyl 4-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-2-(trifluoromethyl)pyrimidine-5-carboxylate
inferredcites →https://doi.org/10.1021/ci025580topenalex-snapshot
Prediction of Protein Retention Times in Anion-Exchange Chromatography Systems Using Support Vector Regression
inferredcites →https://doi.org/10.1021/ci0256438openalex-snapshot
Diagnosing Breast Cancer Based on Support Vector Machines
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot
A tutorial on support vector regression
inferredcites →https://doi.org/10.2174/0929867023371265openalex-snapshot
Inhibitors of AP-1 and NF-κB Mediated Transcriptional Activation: Therapeutic Potential in Autoimmune Diseases and Structural Diversity
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781394167258.ch2openalex-snapshot
Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions
inferred← citeshttps://doi.org/10.1002/chin.200342232openalex-snapshot
QSAR Study of Ethyl 2‐[(3‐Methyl‐2,5‐dioxo(3‐pyrrolinyl))amino] ‐4‐(trifluoromethyl)pyrimidine‐5‐carboxylate: An Inhibitor of AP‐1 and NF‐ϰB Mediated Gene Expression Based on Support Vector Machines.
inferred← citeshttps://doi.org/10.1002/ffj.1876openalex-snapshot
Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis
inferred← citeshttps://doi.org/10.1002/minf.201200007openalex-snapshot
A Novel QSPR Model for Prediction of Gas to Dimethyl Sulfoxide Solvation Enthalpy of Organic Compounds Based on Support Vector Machine
inferred← citeshttps://doi.org/10.1002/minf.201200091openalex-snapshot
Prediction of Bovine Serum Albumin‐Water Partition Coefficients of a Wide Variety of Neutral Organic Compounds by Means of Support Vector Machine
inferred← citeshttps://doi.org/10.1002/qsar.200330860openalex-snapshot
Status of HTS Data Mining Approaches
inferred← citeshttps://doi.org/10.1002/qsar.200430884openalex-snapshot
Insight into the Bioactivity and Metabolism of Human Glucagon Receptor Antagonists from 3D‐QSAR Analyses
inferred← citeshttps://doi.org/10.1002/qsar.200510166openalex-snapshot
Study on the Structure‐Activity Relationship of New Anti‐HIV Nucleoside Derivatives Based on the Support Vector Machine Method
inferred← citeshttps://doi.org/10.1002/qsar.200530115openalex-snapshot
Comparative Study of Factor Xa Inhibitors Using Molecular Docking/SVM/HQSAR/3D‐QSAR Methods
inferred← citeshttps://doi.org/10.1002/qsar.200710095openalex-snapshot
QSPR Prediction of pKa for Benzoic Acids in Different Solvents
inferred← citeshttps://doi.org/10.1007/978-3-031-47508-5_42openalex-snapshot
Artificial Intelligence (AI) Applications in Chemistry
inferred← citeshttps://doi.org/10.1007/s00044-015-1354-4openalex-snapshot
QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach
inferred← citeshttps://doi.org/10.1007/s10311-016-0561-7openalex-snapshot
Better prediction of aqueous solubility of chlorinated hydrocarbons using support vector machine modeling
inferred← citeshttps://doi.org/10.1007/s10822-004-2722-1openalex-snapshot
QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines
inferred← citeshttps://doi.org/10.1007/s10822-005-0095-8openalex-snapshot
The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine
inferred← citeshttps://doi.org/10.1007/s10822-005-9003-5openalex-snapshot
Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines
inferred← citeshttps://doi.org/10.1007/s11030-006-8697-1openalex-snapshot
Molecular similarity and diversity in chemoinformatics: From theory to applications
inferred← citeshttps://doi.org/10.1016/b978-0-12-820007-0.00007-6openalex-snapshot
Structure–activity relationships and drug design
inferred← citeshttps://doi.org/10.1016/j.aca.2004.07.033openalex-snapshot
Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous–alcoholic solvents by capillary zone electrophoresis based on support vector machine
inferred← citeshttps://doi.org/10.1016/j.aca.2004.11.066openalex-snapshot
Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks
inferred← citeshttps://doi.org/10.1016/j.aca.2004.12.085openalex-snapshot
Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine
inferred← citeshttps://doi.org/10.1016/j.aca.2005.04.006openalex-snapshot
Study of quantitative structure–mobility relationship of the peptides based on the structural descriptors and support vector machines
inferred← citeshttps://doi.org/10.1016/j.aca.2008.01.003openalex-snapshot
Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression
inferred← citeshttps://doi.org/10.1016/j.aca.2008.06.048openalex-snapshot
Computational study of histamine H3-receptor antagonist with support vector machines and three dimension quantitative structure activity relationship methods
inferred← citeshttps://doi.org/10.1016/j.arabjc.2012.10.021openalex-snapshot
QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.08.057openalex-snapshot
A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
inferred← citeshttps://doi.org/10.1016/j.bmc.2009.05.038openalex-snapshot
Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites
inferred← citeshttps://doi.org/10.1016/j.chemolab.2006.11.004openalex-snapshot
Quantitative structure–activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2005.08.031openalex-snapshot
The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2005.09.053openalex-snapshot
Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals
inferred← citeshttps://doi.org/10.1016/j.chroma.2006.01.136openalex-snapshot
Quantitative structure–property relationships for pesticides in biopartitioning micellar chromatography
inferred← citeshttps://doi.org/10.1016/j.corsci.2014.02.023openalex-snapshot
Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design
inferred← citeshttps://doi.org/10.1016/j.corsci.2015.06.003openalex-snapshot
The discussion of descriptors for the QSAR model and molecular dynamics simulation of benzimidazole derivatives as corrosion inhibitors
inferred← citeshttps://doi.org/10.1016/j.drudis.2018.05.010openalex-snapshot
Machine learning in chemoinformatics and drug discovery
inferred← citeshttps://doi.org/10.1016/j.ejmech.2008.10.021openalex-snapshot
QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.09.006openalex-snapshot
Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.12.003openalex-snapshot
QSAR study on melanocortin-4 receptors by support vector machine
inferred← citeshttps://doi.org/10.1016/j.ejmech.2010.07.010openalex-snapshot
QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components
inferred← citeshttps://doi.org/10.1016/j.ejps.2006.04.002openalex-snapshot
Boosting support vector regression in QSAR studies of bioactivities of chemical compounds
inferred← citeshttps://doi.org/10.1016/j.ejps.2012.06.021openalex-snapshot
Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine
inferred← citeshttps://doi.org/10.1016/j.envpol.2006.08.030openalex-snapshot
Study on adsorption behavior of volatile and semivolatile organic vapors to air-dry soils based on QSPR methods
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2008.11.097openalex-snapshot
Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: Concerns to support vector machine
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2014.06.018openalex-snapshot
Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method
inferred← citeshttps://doi.org/10.1016/j.microc.2012.05.003openalex-snapshot
Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography–mass spectroscopy
inferred← citeshttps://doi.org/10.1016/j.molliq.2013.04.021openalex-snapshot
QSPR study on the viscosity of bis(trifluoromethylsulfonyl)imide-based ionic liquids
inferred← citeshttps://doi.org/10.1021/acs.jafc.2c07851openalex-snapshot
Novel Design of Citral-Thiourea Derivatives for Enhancing Antifungal Potential against Colletotrichum gloeosporioides
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01019openalex-snapshot
Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01422openalex-snapshot
The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01524openalex-snapshot
When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01982openalex-snapshot
Deep Learning vs Classical Methods in Potency and ADME Prediction: Insights from a Computational Blind Challenge
inferred← citeshttps://doi.org/10.1021/ci034173uopenalex-snapshot
Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs
inferred← citeshttps://doi.org/10.1021/ci034248uopenalex-snapshot
An Accurate QSPR Study of O−H Bond Dissociation Energy in Substituted Phenols Based on Support Vector Machines
inferred← citeshttps://doi.org/10.1021/ci034280oopenalex-snapshot
Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines
inferred← citeshttps://doi.org/10.1021/ci049820bopenalex-snapshot
QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine
inferred← citeshttps://doi.org/10.1021/ci049891aopenalex-snapshot
Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine
inferred← citeshttps://doi.org/10.1021/ci049934nopenalex-snapshot
Support Vector Machines-Based Quantitative Structure−Property Relationship for the Prediction of Heat Capacity
inferred← citeshttps://doi.org/10.1021/ci049965iopenalex-snapshot
Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression
inferred← citeshttps://doi.org/10.1021/ci100308fopenalex-snapshot
StructRank: A New Approach for Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/tx049782qopenalex-snapshot
Classification of the Carcinogenicity of N-Nitroso Compounds Based on Support Vector Machines and Linear Discriminant Analysis
inferred← citeshttps://doi.org/10.1039/d0cp03620jopenalex-snapshot
Application and assessment of deep learning for the generation of potential NMDA receptor antagonists
inferred← citeshttps://doi.org/10.1080/00319104.2012.708932openalex-snapshot
Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression
inferred← citeshttps://doi.org/10.1080/10629360902726700openalex-snapshot
Quantitative structure–property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere
inferred← citeshttps://doi.org/10.1080/10659360600562079openalex-snapshot
Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1111/cbdd.13742openalex-snapshot
A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2007.00561.xopenalex-snapshot
Insight into the Metabolism Rate of Quinone Analogues from Molecular Dynamics Simulation and 3D‐QSMR Methods
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2008.00656.xopenalex-snapshot
Computational Study of the Binding Mode of Epidermal Growth Factor Receptor Kinase Inhibitors
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2008.00695.xopenalex-snapshot
Exploring QSARs for Antiviral Activity of 4‐Alkylamino‐6‐(2‐hydroxyethyl)‐2‐methylthiopyrimidines by Support Vector Machine
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2009.00800.xopenalex-snapshot
Support Vector Machine‐Based Quantitative Structure–Activity Relationship Study of Cholesteryl Ester Transfer Protein Inhibitors
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2009.00840.xopenalex-snapshot
In Silico Log P Prediction for a Large Data Set with Support Vector Machines, Radial Basis Neural Networks and Multiple Linear Regression
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2010.00953.xopenalex-snapshot
Exploring QSARs for Inhibitory Activity of Non‐peptide HIV‐1 Protease Inhibitors by GA‐PLS and GA‐SVM
inferred← citeshttps://doi.org/10.1134/s0036024416090119openalex-snapshot
Prediction of solvation enthalpy of gaseous organic compounds in propanol
inferred← citeshttps://doi.org/10.1134/s1990793117020130openalex-snapshot
Ab initio and QSAR study of several etoposides as anticancer drugs: Solvent effect
inferred← citeshttps://doi.org/10.1142/s0219633614500126openalex-snapshot
Quantum chemical and molecular dynamics studies of imidazoline derivatives as corrosion inhibitor and quantitative structure–activity relationship (QSAR) analysis using the support vector machine (SVM) method
inferred← citeshttps://doi.org/10.1177/1087057105284334openalex-snapshot
Support Vector Machines in HTS Data Mining: Type I MetAPs Inhibition Study
inferred← citeshttps://doi.org/10.2174/1389450122999210104205732openalex-snapshot
Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery
inferred← citeshttps://doi.org/10.2174/978160805379711201010194openalex-snapshot
Overview of QSAR Modelling in Rational Drug Design
inferred← citeshttps://doi.org/10.3390/ijms241411488openalex-snapshot
Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review
inferred← citeshttps://doi.org/10.3866/pku.whxb201701163openalex-snapshot
Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.