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Contemporary QSAR Classifiers Compared
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/1521-3838(200110)20:3<227::aid-qsar227>3.0.co;2-yopenalex-snapshot
Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies
inferredcites →https://doi.org/10.1002/qsar.200510200openalex-snapshot
CNS Permeability of Drugs Predicted by a Decision Tree
inferredcites →https://doi.org/10.1002/qsar.200530131openalex-snapshot
QSAR Study of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls using the Heuristic Method and Support Vector Machine
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
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Highly discriminating distance-based topological index
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Data Mining: Practical Machine Learning Tools and Techniques
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Application of boosting to classification problems in chemometrics
inferredcites →https://doi.org/10.1016/s0223-5234(00)00108-2openalex-snapshot
Validation of protein-based alignment in 3D quantitative structure–activity relationships with CoMFA models
inferredcites →https://doi.org/10.1016/s0925-2312(03)00431-4openalex-snapshot
The support vector machine under test
inferredcites →https://doi.org/10.1016/s0925-2312(97)90018-7openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1016/s1359-6446(00)00114-8openalex-snapshot
HTS in the new millennium
inferredcites →https://doi.org/10.1016/s1359-6446(02)02433-9openalex-snapshot
Discovery informatics: its evolving role in drug discovery
inferredcites →https://doi.org/10.1016/s1359-6446(99)01451-8openalex-snapshot
Computational methods for the prediction of ‘drug-likeness’
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/ci050263iopenalex-snapshot
Modeling of Cyclin-Dependent Kinase Inhibition by 1H-Pyrazolo[3,4-d]Pyrimidine Derivatives Using Artificial Neural Network Ensembles
inferredcites →https://doi.org/10.1021/ci050301yopenalex-snapshot
Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines
inferredcites →https://doi.org/10.1021/ci050375+openalex-snapshot
Ensemble of Linear Models for Predicting Drug Properties
inferredcites →https://doi.org/10.1021/ci0504216openalex-snapshot
Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure−Property Relationship Studies of Metal Complexation with Ionophores
inferredcites →https://doi.org/10.1021/ci050518sopenalex-snapshot
Classification Tree Models for the Prediction of Blood−Brain Barrier Passage of Drugs
inferredcites →https://doi.org/10.1021/ci050519kopenalex-snapshot
Assessing Different Classification Methods for Virtual Screening
inferredcites →https://doi.org/10.1021/ci050521bopenalex-snapshot
A Family of Ring System-Based Structural Fragments for Use in Structure−Activity Studies: Database Mining and Recursive Partitioning
inferredcites →https://doi.org/10.1021/ja01062a036openalex-snapshot
Deoxyribonucleoside-3′,5′ Cyclic Phosphates. Synthesis and Acid-Catalyzed and Enzymic Hydrolysis
inferredcites →https://doi.org/10.1021/jm00004a017openalex-snapshot
Prediction of Receptor Properties and Binding Affinity of Ligands to Benzodiazepine/GABAA Receptors Using Artificial Neural Networks
inferredcites →https://doi.org/10.1021/jm00050a010openalex-snapshot
Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity
inferredcites →https://doi.org/10.1021/jm0101343openalex-snapshot
Three-Dimensional Quantitative Structure−Activity Relationships of Cyclo-oxygenase-2 (COX-2) Inhibitors: A Comparative Molecular Field Analysis
inferredcites →https://doi.org/10.1021/jm049092jopenalex-snapshot
Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
inferredcites →https://doi.org/10.1021/jm981062ropenalex-snapshot
Three-Dimensional Quantitative Structure−Activity Relationship Analyses Using Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis To Elucidate Selectivity Differences of Inhibitors Binding to Trypsin, Thrombin, and Factor Xa
inferredcites →https://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferredcites →https://doi.org/10.1023/a:1012450327387openalex-snapshot
Choosing Multiple Parameters for Support Vector Machines
inferredcites →https://doi.org/10.1023/b:jcam.0000047814.85293.daopenalex-snapshot
Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors
inferredcites →https://doi.org/10.1038/194178b0openalex-snapshot
Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients
inferredcites →https://doi.org/10.1038/194180a0openalex-snapshot
Beta-Scintillation Counting of Radioactive Tracers Insoluble in Toluene
inferredcites →https://doi.org/10.1080/01621459.1937.10503522openalex-snapshot
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
inferredcites →https://doi.org/10.1093/bioinformatics/16.10.906openalex-snapshot
Support vector machine classification and validation of cancer tissue samples using microarray expression data
inferredcites →https://doi.org/10.1126/science.253.5018.390openalex-snapshot
Statistical Data Analysis in the Computer Age
inferredcites →https://doi.org/10.1126/science.253.5018.395openalex-snapshot
Enols and Other Reactive Species
inferredcites →https://doi.org/10.1214/aoms/1177731944openalex-snapshot
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Current Modeling Methods Used in QSAR/QSPR
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Developing Best Practices for Descriptor‐Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations
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Cross‐validated permutation feature importance considering correlation between features
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Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results
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Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding
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Benchmarking Variable Selection in QSAR
inferred← citeshttps://doi.org/10.1002/qsar.200810173openalex-snapshot
Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
inferred← citeshttps://doi.org/10.1002/qsar.200960051openalex-snapshot
Similarity Based Descriptors – Useful for Classification of Substrates of the Human Multidrug Transporter P‐Glycoprotein?
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Machine learning methods in chemoinformatics
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Predictive Cheminformatics in Drug Discovery: Statistical Modeling for Analysis of Micro-array and Gene Expression Data
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Classification Models for Safe Drug Molecules
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QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
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Representing descriptors derived from multiple conformations as uncertain features for machine learning
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Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction
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QSAR workbench: automating QSAR modeling to drive compound design
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An ensemble approach for in silico prediction of Ames mutagenicity
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Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes
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Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling
inferred← citeshttps://doi.org/10.1007/s11030-012-9415-9openalex-snapshot
Reliably assessing prediction reliability for high dimensional QSAR data
inferred← citeshttps://doi.org/10.1007/s44211-026-00919-9openalex-snapshot
Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies
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Prediction of P2Y12 antagonists using a novel genetic algorithm-support vector machine coupled approach
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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
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A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds
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Illustration of merits of semi-supervised learning in regression analysis
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Discovery of novel p90 ribosomal S6 kinase 2 inhibitors for potential cancer treatment through ligand-based and structure-based virtual screening methods
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T-Gen: Time series data generator for inverse analysis of machine learning models
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Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases
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Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables
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Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern
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Calculation and application of activity discriminants in lead optimization
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Sum of ranking differences compares methods or models fairly
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Computational Analysis of Structure–Activity Relationships
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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
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Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions
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XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
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Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity
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Exploiting Multiple Descriptor Sets in QSAR Studies
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Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
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Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index
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Building Highly Reliable Quantitative Structure–Activity Relationship Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection
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Advances in computational methods to predict the biological activity of compounds
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The review of the most used computational methods for studies of the relationships between molecular structure and biological activity
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The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors
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Prediction of Carcinogenicity of Noncongeneric Chemical Substances by a Support Vector Machine
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Kernel Methods for Predicting Yields of Chemical Reactions
inferred← citeshttps://doi.org/10.3389/conf.neuro.11.2009.08.080openalex-snapshot
Brainstorming: Consensus Learning in Practice
inferred← citeshttps://doi.org/10.3389/fddsv.2025.1713308openalex-snapshot
Exploring deep learning approaches in anticancer drug design: a review of recent advances
inferred← citeshttps://doi.org/10.3389/fenvs.2016.00009openalex-snapshot
Rigorous Selection of Random Forest Models for Identifying Compounds that Activate Toxicity-Related Pathways
inferred← citeshttps://doi.org/10.3390/ijms20092175openalex-snapshot
Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction
inferred← citeshttps://doi.org/10.3390/ijms21165847openalex-snapshot
Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies
inferred← citeshttps://doi.org/10.3390/ph15111383openalex-snapshot
Quantitative Structure–Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective
inferred← citeshttps://doi.org/10.35940/ijitee.i1096.0789s19openalex-snapshot
Quantitative Structure Activity Relationship for Drug Discovery
inferred← citeshttps://doi.org/10.3906/elk-1102-1033openalex-snapshot
A comparative review of regression ensembles on drug design datasets
inferred← citeshttps://doi.org/10.4155/fmc.11.23openalex-snapshot
Trade-Off Between Accuracy and Interpretability for PredictiveIn SilicoModeling
inferred← citeshttps://doi.org/10.70675/00fff70azab21z46edz8364z60031c95c00fopenalex-snapshot
Applications of proteochemometrics (PCM) : from species extrapolation to cell-line sensitivity modelling
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