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Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Neuronale Netze in der Chemie
inferredcites →https://doi.org/10.1002/qsar.200810084openalex-snapshot
Are the Chemical Structures in Your QSAR Correct?
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Introductory Statistics with R
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Hierarchical QSAR technology based on the Simplex representation of molecular structure
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A new algorithm for optimal, distance-based experimental design
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Artificial neural networks in classification of NIR spectral data: Design of the training set
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D-optimal onion designs in statistical molecular design
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A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)
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Assessing the reliability of a QSAR model's predictions
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Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis
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Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): A study based on 865 compounds
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Chemometric methods in molecular design
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Kohonen and counterpropagation artificial neural networks in analytical chemistry
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Design and Optimization in Organic Synthesis
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Comparison of algorithms for dissimilarity-based compound selection
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Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors
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Optimization in Irregularly Shaped Regions: pH and Solvent Strength in Reversed-Phase High-Performance Liquid Chromatography Separations
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training−Test Sets and Consensus Modeling
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Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
inferredcites →https://doi.org/10.1021/ci900203nopenalex-snapshot
Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity
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Quantitative Structure−Activity Relationship Studies of [(Biphenyloxy)propyl]isoxazole Derivatives. Inhibitors of Human Rhinovirus 2 Replication
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Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds to the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method
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Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
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Prediction of Fathead Minnow Acute Toxicity of Organic Compounds from Molecular Structure
inferredcites →https://doi.org/10.1023/a:1020869118689openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot
Rational selection of training and test sets for the development of validated QSAR models
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Computer Aided Design of Experiments
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TETRATOX: TETRAHYMENA PYRIFORMIS POPULATION GROWTH IMPAIRMENT ENDPOINTA SURROGATE FOR FISH LETHALITY
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Base-line model for identifying the bioaccumulation potential of chemicals
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A Hierarchical Clustering Methodology for the Estimation of Toxicity
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Application of Nonlinear Regression Functions for the Modeling of Retention in Reversed-Phase LC
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Identification of individual structural fragments of N,N′-(bis-5-nitropyrimidyl)dispirotripiperazine derivatives for cytotoxicity and antiherpetic activity allows the prediction of new highly active compounds
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A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms
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Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
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Current Bioconcentration Test Methods and Theory
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The analysis of structure-anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure).
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Applied Statistics: A Handbook of Techniques
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Short Tour of the Predictive Modeling Process
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Linear Regression and Its Cousins
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inferred← citeshttps://doi.org/10.1016/j.etdah.2024.100156openalex-snapshot
A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances
inferred← citeshttps://doi.org/10.1016/j.fecs.2023.100101openalex-snapshot
Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China
inferred← citeshttps://doi.org/10.1016/j.foodchem.2016.08.018openalex-snapshot
An approach to the simultaneous quantitative analysis of metabolites in table wines by 1H NMR self-constructed three-dimensional spectra
inferred← citeshttps://doi.org/10.1016/j.foodchem.2026.148841openalex-snapshot
A similarity-augmented q-RASPR framework for the antioxidant potential prediction and identification of functional food compounds
inferred← citeshttps://doi.org/10.1016/j.fuel.2023.128338openalex-snapshot
A hybrid data-driven machine learning framework for predicting the performance of coal and biomass gasification processes
inferred← citeshttps://doi.org/10.1016/j.fuel.2025.136065openalex-snapshot
An optimized machine learning framework for prediction of coal abrasive index: Leveraging supervised learning, metaheuristic optimization, and interpretability analysis
inferred← citeshttps://doi.org/10.1016/j.fufo.2026.101126openalex-snapshot
A unified multi-task transformer framework for predicting drug half-lives in plasma and tissues across six food-producing species
inferred← citeshttps://doi.org/10.1016/j.heliyon.2020.e04514openalex-snapshot
2D-QSAR and docking study of a series of coumarin derivatives as inhibitors of CDK (anticancer activity) with an application of the molecular docking method
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e24551openalex-snapshot
Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e38309openalex-snapshot
In silico design and ADMET evaluation of new inhibitors for PIM1 kinase using QSAR studies, molecular docking, and molecular dynamic simulation
inferred← citeshttps://doi.org/10.1016/j.ijhydene.2026.153747openalex-snapshot
Integration of PRO/II simulation and XGBoost machine learning for predicting and optimizing hydrogen-rich syngas from air-blown biomass–plastic co-gasification
inferred← citeshttps://doi.org/10.1016/j.jare.2018.03.003openalex-snapshot
Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2025.137917openalex-snapshot
Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2025.139134openalex-snapshot
Bridging chemistry and toxicology: Chemometric-driven prediction and prioritization of various drugs addressing hazardous effects for human safety
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2025.139533openalex-snapshot
Machine learning-assisted comparative QSTR, i-QSTTR, qRASTR, and i-qRASTTR modelling for toxicity of Ionic liquids against three different bacteria S. aureus, E. coli, and P. aeruginosa
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2026.141274openalex-snapshot
Predictive q-RASAR modeling of intrinsic toxicity of pharmaceuticals and bioactive compounds in cats
inferred← citeshttps://doi.org/10.1016/j.jics.2022.100847openalex-snapshot
Towards further understanding the structural insights of isoxazoles analogues against leishmaniasis using QSAR, molecular docking and molecular dynamics model
inferred← citeshttps://doi.org/10.1016/j.jics.2026.102840openalex-snapshot
Decoding fish toxicity: An intelligent consensus-based approach for toxicity prediction and identification of influential structural fragments in Heteropneustes fossilis
inferred← citeshttps://doi.org/10.1016/j.jmat.2020.12.018openalex-snapshot
Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle
inferred← citeshttps://doi.org/10.1016/j.jmgm.2014.08.004openalex-snapshot
Modeling structure–activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS
inferred← citeshttps://doi.org/10.1016/j.jmgm.2018.11.013openalex-snapshot
QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods
inferred← citeshttps://doi.org/10.1016/j.jmgm.2018.11.019openalex-snapshot
Molecular docking, molecular dynamics simulations and QSAR studies on some of 2-arylethenylquinoline derivatives for inhibition of Alzheimer's amyloid-beta aggregation: Insight into mechanism of interactions and parameters for design of new inhibitors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2019.107525openalex-snapshot
Use of the index of ideality of correlation to improve aquatic solubility model
inferred← citeshttps://doi.org/10.1016/j.jmgm.2023.108450openalex-snapshot
Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons
inferred← citeshttps://doi.org/10.1016/j.jrmge.2023.09.017openalex-snapshot
Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods
inferred← citeshttps://doi.org/10.1016/j.marpolbul.2025.118593openalex-snapshot
An alternative to in vivo/in vitro toxicity testing: Consensus-based prediction for toxicity assessment and prioritisation of diverse chemicals in Japanese eel to safeguard aquatic and public health
inferred← citeshttps://doi.org/10.1016/j.molliq.2022.118511openalex-snapshot
User-assisted methodology targeted for building structure interpretable QSPR models for boosting CO2 capture with ionic liquids
inferred← citeshttps://doi.org/10.1016/j.molliq.2023.122884openalex-snapshot
QSPR models for solvation enthalpy based on quantum chemical descriptors
inferred← citeshttps://doi.org/10.1016/j.molstruc.2016.11.012openalex-snapshot
QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines
inferred← citeshttps://doi.org/10.1016/j.molstruc.2020.127808openalex-snapshot
Integrated computational investigation to develop molecular design of quinazoline scaffold as promising inhibitors of plasmodium lactate dehydrogenase
inferred← citeshttps://doi.org/10.1016/j.molstruc.2020.129597openalex-snapshot
Balanced QSAR analysis to identify the structural requirements of ABBV-075 (Mivebresib) analogues as bromodomain and extraterminal domain (BET) family bromodomain inhibitor
inferred← citeshttps://doi.org/10.1016/j.molstruc.2021.130366openalex-snapshot
Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2020.101604openalex-snapshot
Accelerated discovery of high-efficient N-annulated perylene organic sensitizers for solar cells via machine learning and quantum chemistry
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2024.111089openalex-snapshot
ML-based Read-Across Structure-Property Relationship (RASPR) strategy for predicting protein resistance of self-assembled monolayers (SAMs) as anti-biofouling materials
inferred← citeshttps://doi.org/10.1016/j.nxsust.2026.100328openalex-snapshot
A read-across data gap filling approach for the emission of Volatile Organic Compounds (VOCs) in plastic waste recycling
inferred← citeshttps://doi.org/10.1016/j.psep.2024.05.095openalex-snapshot
Comprehensive ecotoxicological assessment of pesticides on multiple avian species: Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across
inferred← citeshttps://doi.org/10.1016/j.saa.2021.119870openalex-snapshot
A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2022.157455openalex-snapshot
Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning
inferred← citeshttps://doi.org/10.1016/j.seppur.2023.123464openalex-snapshot
Free volume in physical absorption of carbon dioxide in ionic liquids: Molecular dynamics supported modeling
inferred← citeshttps://doi.org/10.1016/j.tifs.2023.104165openalex-snapshot
QSAR in natural non-peptidic food-related compounds: Current status and future perspective
inferred← citeshttps://doi.org/10.1016/j.tiv.2013.11.002openalex-snapshot
Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli
inferred← citeshttps://doi.org/10.1016/j.tox.2022.153111openalex-snapshot
Skin sensitization quantitative QSAR models based on mechanistic structural alerts
inferred← citeshttps://doi.org/10.1016/j.toxlet.2025.06.019openalex-snapshot
Revolutionizing toxicity predictions of diverse chemicals to protect human health: Comparative QSAR and q-RASAR modeling
inferred← citeshttps://doi.org/10.1016/j.toxrep.2025.102017openalex-snapshot
Risk assessment of industrial chemicals towards salmon species amalgamating QSAR, q-RASAR, and ARKA framework
inferred← citeshttps://doi.org/10.1016/j.yebeh.2019.106451openalex-snapshot
New anticonvulsant candidates prevent P-glycoprotein (P-gp) overexpression in a pharmacoresistant seizure model in mice
inferred← citeshttps://doi.org/10.1021/acs.accounts.0c00770openalex-snapshot
Predicting Reaction Yields via Supervised Learning
inferred← citeshttps://doi.org/10.1021/acs.analchem.6b02075openalex-snapshot
Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00379openalex-snapshot
Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5b00197openalex-snapshot
Mechanism-Based QSAR Modeling of Skin Sensitization
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00459openalex-snapshot
Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental Validation
inferred← citeshttps://doi.org/10.1021/acs.chemrev.0c00004openalex-snapshot
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.energyfuels.7b00616openalex-snapshot
A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure
inferred← citeshttps://doi.org/10.1021/acs.est.3c00648openalex-snapshot
Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure
inferred← citeshttps://doi.org/10.1021/acs.est.6c04014openalex-snapshot
Identifying Genotoxicity Forcing Agents from Individual Aliphatic and Aromatic Disinfection Byproducts in Drinking Water and Wastewater Effluents
inferred← citeshttps://doi.org/10.1021/acs.est.6c06820openalex-snapshot
Activity Landscape Roughness Anticipates Machine-Learning Reliability Across Environmental Chemistry Endpoints
inferred← citeshttps://doi.org/10.1021/acs.est.9b03957openalex-snapshot
Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure
inferred← citeshttps://doi.org/10.1021/acs.iecr.5c04602openalex-snapshot
Machine Learning-Based QSPR Modeling of log CMC Values of Per- and Polyfluoroalkyl Substances (PFASs) and the Identification of Property Cliffs
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00251openalex-snapshot
Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00055openalex-snapshot
Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00630openalex-snapshot
First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00646openalex-snapshot
Predictive Models for Fast and Effective Profiling of Kinase Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00713openalex-snapshot
Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01101openalex-snapshot
Physicochemical Property Models for Poly- and Perfluorinated Alkyl Substances and Other Chemical Classes
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02465openalex-snapshot
Toward More Trustworthy QSAR: A Systematic Discussion on Data Set Partitioning
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00044openalex-snapshot
In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00277openalex-snapshot
Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01964openalex-snapshot
Q performance 2: Toward Unbiased Selection of Machine-Learning Regression Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00029openalex-snapshot
PVLOO-Based Training Set Selection Improves the External Predictability of QSAR/QSPR Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00118openalex-snapshot
Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00553openalex-snapshot
Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00386openalex-snapshot
Applications of Discrete Shmaliy Moments on the Quantitative Analysis of Multitarget Compounds Based on the Chemical Spectra
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00798openalex-snapshot
Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b00487openalex-snapshot
In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development
inferred← citeshttps://doi.org/10.1021/acs.jpcb.0c08674openalex-snapshot
Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation
inferred← citeshttps://doi.org/10.1021/acs.organomet.2c00089openalex-snapshot
Advancing Base Metal Catalysis through Data Science: Insight and Predictive Models for Ni-Catalyzed Borylation through Supervised Machine Learning
inferred← citeshttps://doi.org/10.1021/acscatal.2c01970openalex-snapshot
Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling
inferred← citeshttps://doi.org/10.1021/acschemneuro.9b00650openalex-snapshot
The Brain Exposure Efficiency (BEE) Score
inferred← citeshttps://doi.org/10.1021/ci400084kopenalex-snapshot
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
inferred← citeshttps://doi.org/10.1021/ci400715nopenalex-snapshot
Optimization of Molecular Representativeness
inferred← citeshttps://doi.org/10.1021/ci500342qopenalex-snapshot
Generalized Workflow for Generating Highly Predictive in Silico Off-Target Activity Models
inferred← citeshttps://doi.org/10.1021/ci500611vopenalex-snapshot
Discovering Structural Alerts for Mutagenicity Using Stable Emerging Molecular Patterns
inferred← citeshttps://doi.org/10.1021/envhealth.4c00014openalex-snapshot
AquaticTox: A Web-Based Tool for Aquatic Toxicity Evaluation Based on Ensemble Learning to Facilitate the Screening of Green Chemicals
inferred← citeshttps://doi.org/10.1021/je501093vopenalex-snapshot
Quantitative Structure–Property Relationship Predictions of Critical Properties and Acentric Factors for Pure Compounds
inferred← citeshttps://doi.org/10.1021/jm4004285openalex-snapshot
QSAR Modeling: Where Have You Been? Where Are You Going To?
inferred← citeshttps://doi.org/10.1038/s41524-024-01331-5openalex-snapshot
The mastery of details in the workflow of materials machine learning
inferred← citeshttps://doi.org/10.1038/s41598-017-05964-zopenalex-snapshot
A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
inferred← citeshttps://doi.org/10.1038/s41598-024-55628-yopenalex-snapshot
Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors
inferred← citeshttps://doi.org/10.1038/s43586-021-00022-5openalex-snapshot
Automation and computer-assisted planning for chemical synthesis
inferred← citeshttps://doi.org/10.1038/s44160-022-00233-yopenalex-snapshot
Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning
inferred← citeshttps://doi.org/10.1039/c3mb70105kopenalex-snapshot
Structural features of falcipain-3 inhibitors: an in silico study
inferred← citeshttps://doi.org/10.1039/c4tx00174eopenalex-snapshot
In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts
inferred← citeshttps://doi.org/10.1039/c5mb00356copenalex-snapshot
In silico research to assist the investigation of carboxamide derivatives as potent TRPV1 antagonists
inferred← citeshttps://doi.org/10.1039/c5ra10729fopenalex-snapshot
Multivariate statistical analysis methods in QSAR
inferred← citeshttps://doi.org/10.1039/c7tx00144dopenalex-snapshot
In silico prediction of pesticide aquatic toxicity with chemical category approaches
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