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GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Applications of Support Vector Machines in Chemistry
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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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An Introduction to the Bootstrap
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Encyclopedia of Complexity and Systems Science
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Construction of a classifier using AdaBoost for information filtering
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Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)
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A general framework for distance-based consensus in ordinal ranking models
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A distance measure between models: a tool for similarity/diversity analysis of model populations
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QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression
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QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm
inferredcites →https://doi.org/10.1016/j.knosys.2009.11.020openalex-snapshot
Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation
inferredcites →https://doi.org/10.1016/j.talanta.2010.11.039openalex-snapshot
Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies
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QSAR for dihydrofolate reductase inhibitors with molecular graph structural descriptors
inferredcites →https://doi.org/10.1021/ci020058sopenalex-snapshot
Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
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Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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On Outliers and Activity CliffsWhy QSAR Often Disappoints
inferredcites →https://doi.org/10.1021/ci0601315openalex-snapshot
Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction
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A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models
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The Pharmacophore Kernel for Virtual Screening with Support Vector Machines
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Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
inferredcites →https://doi.org/10.1021/ci100222kopenalex-snapshot
Elaborate Ligand-Based Modeling Reveals New Nanomolar Heat Shock Protein 90α Inhibitors
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StructRank: A New Approach for Ligand-Based Virtual Screening
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Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection
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Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models
inferredcites →https://doi.org/10.1021/ci7000082openalex-snapshot
3D QSAR Methods: Phase and Catalyst Compared
inferredcites →https://doi.org/10.1021/ci700443vopenalex-snapshot
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
inferredcites →https://doi.org/10.1021/ci8001952openalex-snapshot
Using Molecular Docking, 3D-QSAR, and Cluster Analysis for Screening Structurally Diverse Data Sets of Pharmacological Interest
inferredcites →https://doi.org/10.1021/ci800366fopenalex-snapshot
Novel Inhibitors of Human Histone Deacetylase (HDAC) Identified by QSAR Modeling of Known Inhibitors, Virtual Screening, and Experimental Validation
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Feature Selection and Linear/Nonlinear Regression Methods for the Accurate Prediction of Glycogen Synthase Kinase-3β Inhibitory Activities
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A Model-Based Ensembling Approach for Developing QSARs
inferredcites →https://doi.org/10.1021/ci900118vopenalex-snapshot
Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors
inferredcites →https://doi.org/10.1021/ci900406xopenalex-snapshot
Gaussian Processes for Classification: QSAR Modeling of ADMET and Target Activity
inferredcites →https://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferredcites →https://doi.org/10.1021/jm030584qopenalex-snapshot
Application of Predictive QSAR Models to Database Mining: Identification and Experimental Validation of Novel Anticonvulsant Compounds
inferredcites →https://doi.org/10.1021/jm049896zopenalex-snapshot
Pruned Receptor Surface Models and Pharmacophores for Three-Dimensional Database Searching
inferredcites →https://doi.org/10.1021/jm100600yopenalex-snapshot
Development, Validation, and Use of Quantitative Structure−Activity Relationship Models of 5-Hydroxytryptamine (2B) Receptor Ligands to Identify Novel Receptor Binders and Putative Valvulopathic Compounds among Common Drugs
inferredcites →https://doi.org/10.1021/jm701130zopenalex-snapshot
High Confidence Predictions of Drug−Drug Interactions: Predicting Affinities for Cytochrome P450 2C9 with Multiple Computational Methods
inferredcites →https://doi.org/10.1021/mp100423uopenalex-snapshot
Why QSAR Fails: An Empirical Evaluation Using Conventional Computational Approach
inferredcites →https://doi.org/10.1021/tx900326kopenalex-snapshot
Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species
inferredcites →https://doi.org/10.1080/00401706.1979.10489764openalex-snapshot
Nonparametrics: Statistical Methods Based on Ranks
inferredcites →https://doi.org/10.1109/csse.2008.1040openalex-snapshot
Genetic Algorithm Based Optimization for AdaBoost
inferredcites →https://doi.org/10.1109/icpr.2008.4761264openalex-snapshot
Feature selection combining genetic algorithm and Adaboost classifiers
inferredcites →https://doi.org/10.1109/iscis.2007.4456870openalex-snapshot
A pattern classification approach for boosting with genetic algorithms
inferredcites →https://doi.org/10.1111/j.1747-0285.2008.00675.xopenalex-snapshot
Proteochemometric Modeling of the Inhibition Complexes of Matrix Metalloproteinases with N‐Hydroxy‐2‐[(Phenylsulfonyl)Amino]Acetamide Derivatives Using Topological Autocorrelation Interaction Matrix and Model Ensemble Averaging
inferredcites →https://doi.org/10.1111/j.1747-0285.2011.01236.xopenalex-snapshot
QSAR Models for Phosphoramidate Prodrugs of 2′‐Methylcytidine as Inhibitors of Hepatitis C Virus Based on PSO Boosting
inferredcites →https://doi.org/10.1137/0129023openalex-snapshot
Preference Structures. II: Distances Between Asymmetric Relations
inferredcites →https://doi.org/10.1177/026119290503300508openalex-snapshot
QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review
inferredcites →https://doi.org/10.2174/092986711795933731openalex-snapshot
Strategies in the Rational Drug Design
inferredcites →https://doi.org/10.2174/138161207780765954openalex-snapshot
Artificial Intelligence Approaches for Rational Drug Design and Discovery
inferredcites →https://doi.org/10.2174/156802608786786552openalex-snapshot
Variable Selection Methods in QSAR: An Overview
inferredcites →https://doi.org/10.2174/156802608786786589openalex-snapshot
Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential
inferredcites →https://doi.org/10.2307/3001968openalex-snapshot
Individual Comparisons by Ranking Methods
inferredcites →https://doi.org/10.2741/3788openalex-snapshot
Theoretical and computational approaches to ligand-based drug discovery
inferredcites →https://doi.org/10.4155/fmc.11.18openalex-snapshot
Integrating Structure-Based and Ligand-Based Approaches for Computational Drug Design
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
inferredcites →https://doi.org/10.5860/choice.32-1541openalex-snapshot
Applications of artificial intelligence in chemistry
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Prediction of blood‐brain barrier permeability using machine learning approaches based on various molecular representation
inferred← citeshttps://doi.org/10.1007/978-1-4939-2239-0_8openalex-snapshot
AutoWeka: Toward an Automated Data Mining Software for QSAR and QSPR Studies
inferred← citeshttps://doi.org/10.1007/978-3-031-27409-1_69openalex-snapshot
Binary Classification with Genetic Algorithms. A Study on Fitness Functions
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Computer-Aided Drug Design
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Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?
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MLRMPA: An R package of multiple linear regression model population analysis based on a cluster sampling technique for variable selection of high dimensional data
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QSAR classification model for diverse series of antimicrobial agents using classification tree configured by modified particle swarm optimization
inferred← citeshttps://doi.org/10.1016/j.drudis.2017.02.004openalex-snapshot
Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery
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StackDILI: Enhancing Drug-Induced Liver Injury Prediction through Stacking Strategy with Effective Molecular Representations
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Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein–Ligand Binding Classification
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CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring
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Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A2BAdenosine Receptor Antagonists
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Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor
inferred← citeshttps://doi.org/10.1063/1.4991192openalex-snapshot
Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine
inferred← citeshttps://doi.org/10.1109/tevc.2024.3382145openalex-snapshot
A Survey on Evolutionary Computation-Based Drug Discovery
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A combined drug discovery strategy based on machine learning and molecular docking
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Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy
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A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents
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A glimpse at molecular descriptor selection algorithms in QSAR studies: including advantages and problems
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
inferred← citeshttps://doi.org/10.2174/1568026619666190510100313openalex-snapshot
Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity
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Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
inferred← citeshttps://doi.org/10.3390/cimb44110382openalex-snapshot
MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs
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An Introduction to the Basic Concepts in QSAR-Aided Drug Design
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An Introduction to the Basic Concepts in QSAR-Aided Drug Design
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Additional file 1 of Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy
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Additional file 2 of Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy
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