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Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A2BAdenosine Receptor Antagonists

https://doi.org/10.1021/ci300516w
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Combining Pattern Classifiers
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Structural Predictions of Adenosine 2B Antagonist Affinity Using Molecular Field Analysis
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Handbook of Molecular Descriptors
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Recent developments in the field of A3 adenosine receptor antagonists
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Pyrazolo[4,3‐e]1,2,4‐triazolo[1,5‐c]pyrimidine derivatives as adenosine receptor ligands: A starting point for searching A2B adenosine receptor antagonists
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Progress in the pursuit of therapeutic adenosine receptor antagonists
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Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?
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Best Practices for QSAR Model Development, Validation, and Exploitation
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
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The Nature of Statistical Learning Theory
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An Introduction to the Bootstrap
inferredcites →https://doi.org/10.1016/j.bmc.2008.01.002openalex-snapshot
1-, 3- and 8-substituted-9-deazaxanthines as potent and selective antagonists at the human A2B adenosine receptor
inferredcites →https://doi.org/10.1016/j.bmc.2008.09.067openalex-snapshot
1,3-Dialkyl-8-(hetero)aryl-9-OH-9-deazaxanthines as potent A2B adenosine receptor antagonists: Design, synthesis, structure–affinity and structure–selectivity relationships
inferredcites →https://doi.org/10.1016/j.chemolab.2003.10.003openalex-snapshot
A distance measure between models: a tool for similarity/diversity analysis of model populations
inferredcites →https://doi.org/10.1016/j.ejmech.2004.07.008openalex-snapshot
8-Substituted-9-deazaxanthines as adenosine receptor ligands: design, synthesis and structure-affinity relationships at A2B
inferredcites →https://doi.org/10.1016/j.ejmech.2006.11.016openalex-snapshot
ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds
inferredcites →https://doi.org/10.1016/j.ejmech.2012.10.035openalex-snapshot
Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors
inferredcites →https://doi.org/10.1016/s0031-6997(24)01511-4openalex-snapshot
International Union of Pharmacology. XXV. Nomenclature and Classification of Adenosine Receptors
inferredcites →https://doi.org/10.1016/s0223-5234(03)00042-4openalex-snapshot
Recent developments in the field of A2A and A3 adenosine receptor antagonists
inferredcites →https://doi.org/10.1016/s0968-0896(03)00484-xopenalex-snapshot
New strategies for the synthesis of A3 adenosine receptor antagonists
inferredcites →https://doi.org/10.1016/s1359-6446(99)01421-xopenalex-snapshot
Adenosine receptors as potential therapeutic targets
inferredcites →https://doi.org/10.1021/ci015504aopenalex-snapshot
Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 1. Theory of the Novel 3D Molecular Descriptors
inferredcites →https://doi.org/10.1021/ci0155053openalex-snapshot
Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies
inferredcites →https://doi.org/10.1021/ci034129eopenalex-snapshot
Using Ensembles to Classify Compounds for Drug Discovery
inferredcites →https://doi.org/10.1021/ci034203topenalex-snapshot
Combinatorial QSAR of Ambergris Fragrance Compounds
inferredcites →https://doi.org/10.1021/ci0500379openalex-snapshot
Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci0504317openalex-snapshot
Combinatorial QSAR Modeling of P-Glycoprotein Substrates
inferredcites →https://doi.org/10.1021/ci100031xopenalex-snapshot
Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database
inferredcites →https://doi.org/10.1021/ci200091hopenalex-snapshot
A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition
inferredcites →https://doi.org/10.1021/ci300146hopenalex-snapshot
GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design
inferredcites →https://doi.org/10.1021/ci600563wopenalex-snapshot
Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models
inferredcites →https://doi.org/10.1021/ci900311jopenalex-snapshot
Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study
inferredcites →https://doi.org/10.1021/ci950187ropenalex-snapshot
Spectral Moments of the Edge Adjacency Matrix in Molecular Graphs. 1. Definition and Applications to the Prediction of Physical Properties of Alkanes
inferredcites →https://doi.org/10.1021/ci960113vopenalex-snapshot
Spectral Moments of the Edge-Adjacency Matrix of Molecular Graphs. 2. Molecules Containing Heteroatoms and QSAR Applications
inferredcites →https://doi.org/10.1021/ci970030uopenalex-snapshot
Spectral Moments of the Edge Adjacency Matrix in Molecular Graphs. 3. Molecules Containing Cycles
inferredcites →https://doi.org/10.1021/cr0682195openalex-snapshot
Adenosine Receptor Antagonists: Translating Medicinal Chemistry and Pharmacology into Clinical Utility
inferredcites →https://doi.org/10.1021/jm001047yopenalex-snapshot
Pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine Derivatives as Highly Potent and Selective Human A3Adenosine Receptor Antagonists: Influence of the Chain at the N8Pyrazole Nitrogen
inferredcites →https://doi.org/10.1021/jm010570popenalex-snapshot
1,2,4-Triazolo[5,1-i]purine Derivatives as Highly Potent and Selective Human Adenosine A3 Receptor Ligands
inferredcites →https://doi.org/10.1021/jm010818aopenalex-snapshot
Fluorosulfonyl- and Bis-(β-chloroethyl)amino-phenylamino Functionalized Pyrazolo[4,3-e]1,2,4-triazolo[1,5-c]pyrimidine Derivatives: Irreversible Antagonists at the Human A3 Adenosine Receptor and Molecular Modeling Studies
inferredcites →https://doi.org/10.1021/jm010924copenalex-snapshot
7-Substituted 5-Amino-2-(2-furyl)pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidines as A2AAdenosine Receptor Antagonists: A Study on the Importance of Modifications at the Side Chain on the Activity and Solubility
inferredcites →https://doi.org/10.1021/jm0109614openalex-snapshot
Synthesis, Biological Activity, and Molecular Modeling Investigation of New Pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine Derivatives as Human A3 Adenosine Receptor Antagonists
inferredcites →https://doi.org/10.1021/jm021023mopenalex-snapshot
Design, Synthesis, and Biological Evaluation of C9- and C2-Substituted Pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidines as New A2Aand A3Adenosine Receptors Antagonists
inferredcites →https://doi.org/10.1021/jm030852kopenalex-snapshot
Pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine Derivatives as Adenosine Receptor Antagonists. Influence of the N5 Substituent on the Affinity at the Human A3 and A2B Adenosine Receptor Subtypes: A Molecular Modeling Investigation
inferredcites →https://doi.org/10.1021/jm0309654openalex-snapshot
Design, Synthesis, and Biological Evaluation of New 8-Heterocyclic Xanthine Derivatives as Highly Potent and Selective Human A2BAdenosine Receptor Antagonists
inferredcites →https://doi.org/10.1021/jm201292wopenalex-snapshot
Novel 1,3-Dipropyl-8-(3-benzimidazol-2-yl-methoxy-1-methylpyrazol-5-yl)xanthines as Potent and Selective A2BAdenosine Receptor Antagonists
inferredcites →https://doi.org/10.1023/a:1010920819831openalex-snapshot
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
inferredcites →https://doi.org/10.1023/a:1022859003006openalex-snapshot
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
inferredcites →https://doi.org/10.1080/08839510500313653openalex-snapshot
EVALUATION OF CLASSIFIERS FOR AN UNEVEN CLASS DISTRIBUTION PROBLEM
inferredcites →https://doi.org/10.1093/bioinformatics/btm344openalex-snapshot
A review of feature selection techniques in bioinformatics
inferredcites →https://doi.org/10.1109/tkde.2005.50openalex-snapshot
Using AUC and accuracy in evaluating learning algorithms
inferredcites →https://doi.org/10.1109/tpami.2006.211openalex-snapshot
Rotation Forest: A New Classifier Ensemble Method
inferredcites →https://doi.org/10.1124/dmd.108.023507openalex-snapshot
Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques
inferredcites →https://doi.org/10.1517/14728214.12.3.479openalex-snapshot
Emerging adenosine receptor agonists
inferredcites →https://doi.org/10.2174/138161207782794257openalex-snapshot
Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening
inferredcites →https://doi.org/10.2174/1568026033392264openalex-snapshot
Medicinal Chemistry and Pharmacology of A2B Adenosine Receptors
inferredcites →https://doi.org/10.2174/156802607780906771openalex-snapshot
Medicinal Chemistry and Bioinformatics - Current Trends in Drugs Discovery with Networks Topological Indices
inferredcites →https://doi.org/10.2174/156802612800166819openalex-snapshot
From QSAR models of Drugs to Complex Networks: State-of-Art Review and Introduction of New Markov-Spectral Moments Indices
inferredcites →https://doi.org/10.5860/choice.48-5729openalex-snapshot
Data mining and knowledge discovery handbook
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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QSAR and docking analysis of A2B adenosine receptor antagonists based on non-xanthine scaffold
inferred← citeshttps://doi.org/10.1007/s10910-018-0855-zopenalex-snapshot
An ensemble approach for in silico prediction of Ames mutagenicity
inferred← citeshttps://doi.org/10.1007/s11030-015-9608-0openalex-snapshot
Latest QSAR study of adenosine A $$_{\mathrm{2B}}$$ 2 B receptor affinity of xanthines and deazaxanthines
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Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human $$\mathbf{A}_{3}$$ A 3 adenosine receptor ligands
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Molecular modeling approaches for the discovery of adenosine A2B receptor antagonists: current status and future perspectives
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Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01439openalex-snapshot
A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data
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Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)
inferred← citeshttps://doi.org/10.1080/07391102.2018.1517612openalex-snapshot
Exploring the structural determinants of novel xanthine derivatives as A2Badenosine receptor antagonists: a computational study
inferred← citeshttps://doi.org/10.2174/1381612822666160302103542openalex-snapshot
Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A 2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors
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Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity
inferred← citeshttps://doi.org/10.3390/mol2net-1-b033openalex-snapshot
Virtual screening tailored ensembles of QSAR models for the discovery of dual A2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors
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