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Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Population growth impairment of sulfur‐containing compounds to Tetrahymena pyriformis
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inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods
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First report on development of quantitative interspecies structure–carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2020.107318openalex-snapshot
In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104460openalex-snapshot
AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules
inferred← citeshttps://doi.org/10.1016/j.comtox.2019.01.004openalex-snapshot
Ranking strategies to support toxicity prediction: A case study on potential LXR binders
inferred← citeshttps://doi.org/10.1016/j.comtox.2019.100089openalex-snapshot
hERG liability classification models using machine learning techniques
inferred← citeshttps://doi.org/10.1016/j.dental.2009.11.158openalex-snapshot
QSAR models to predict mutagenicity of acrylates, methacrylates and α , β -unsaturated carbonyl compounds
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.06.027openalex-snapshot
REACH and in silico methods: an attractive opportunity for medicinal chemists
inferred← citeshttps://doi.org/10.1016/j.drudis.2020.07.005openalex-snapshot
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2019.110146openalex-snapshot
Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models
inferred← citeshttps://doi.org/10.1016/j.ejps.2024.106946openalex-snapshot
The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation
inferred← citeshttps://doi.org/10.1016/j.fct.2017.04.008openalex-snapshot
Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2020.123777openalex-snapshot
Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2021.126438openalex-snapshot
Chemical safety assessment of transformation products of landfill leachate formed during the Fenton process
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2025.140273openalex-snapshot
Exploring the behavior of fluorinated sulfonylureas herbicides, trifloxysulfuron and prosulfuron, under oxidative water treatment processes: Chlorination, ozonation, UV photolysis and hydroxyl radical reactions
inferred← citeshttps://doi.org/10.1016/j.jinorgbio.2025.112890openalex-snapshot
Online OCHEM multi-task model for solubility and lipophilicity prediction of platinum complexes
inferred← citeshttps://doi.org/10.1016/j.jmgm.2012.01.002openalex-snapshot
In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner
inferred← citeshttps://doi.org/10.1016/j.jmgm.2012.06.002openalex-snapshot
Quantitative structure–activity relationships (QSARs) using the novel marine algal toxicity data of phenols
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.05.006openalex-snapshot
Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach
inferred← citeshttps://doi.org/10.1016/j.micromeso.2011.06.011openalex-snapshot
A quantitative structure activity relationship approach to probe the influence of the functionalization on the drug encapsulation of porous metal-organic frameworks
inferred← citeshttps://doi.org/10.1016/j.procs.2010.04.022openalex-snapshot
Ridge regression ensemble for toxicity prediction
inferred← citeshttps://doi.org/10.1016/j.psep.2021.04.011openalex-snapshot
Risk assessment of organic aromatic compounds to Tetrahymena pyriformis in environmental protection by a simple QSAR model
inferred← citeshttps://doi.org/10.1016/j.psep.2022.12.067openalex-snapshot
Risk assessment of aromatic organic chemicals to T. pyriformis in environmental protection using regression-based QSTR and Read-Across algorithm
inferred← citeshttps://doi.org/10.1016/j.psep.2024.10.108openalex-snapshot
Global classification model for acute toxicity of organic compounds towards Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1016/j.psep.2025.108314openalex-snapshot
Predicting the toxicity of organic chemicals to Tetrahymena pyriformis based on machine learning and interpretable models
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2013.06.081openalex-snapshot
Validation of quantitative structure–activity relationship models to predict water-solubility of organic compounds
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2013.10.068openalex-snapshot
Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2020.140058openalex-snapshot
Importance of protozoa Tetrahymena in toxicological studies: A review
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2022.156572openalex-snapshot
Predicting electrophoretic mobility of TiO2, ZnO, and CeO2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models
inferred← citeshttps://doi.org/10.1016/j.seppur.2025.132441openalex-snapshot
A comprehensive back-extraction methodology for the regeneration of ionic liquids in oil extraction desulfurization
inferred← citeshttps://doi.org/10.1016/j.taap.2013.04.032openalex-snapshot
Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches
inferred← citeshttps://doi.org/10.1016/j.taap.2014.01.006openalex-snapshot
In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches
inferred← citeshttps://doi.org/10.1016/j.theochem.2009.01.007openalex-snapshot
QSAR using momentum-space and trivial feature count descriptors – An application to Tetrahymena pyriformis toxicity
inferred← citeshttps://doi.org/10.1016/j.tox.2012.12.012openalex-snapshot
A systematic identification of multiple toxin–target interactions based on chemical, genomic and toxicological data
inferred← citeshttps://doi.org/10.1016/j.tox.2022.153325openalex-snapshot
Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1016/j.watres.2020.115583openalex-snapshot
Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00384openalex-snapshot
Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.4c00560openalex-snapshot
Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00259openalex-snapshot
Hierarchical Quantitative Structure–Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity
inferred← citeshttps://doi.org/10.1021/acs.est.1c02656openalex-snapshot
Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
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.jcim.0c01164openalex-snapshot
Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01079openalex-snapshot
Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01073openalex-snapshot
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01526openalex-snapshot
Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00139openalex-snapshot
Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00539openalex-snapshot
Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00462openalex-snapshot
Chembench: A Publicly Accessible, Integrated Cheminformatics Portal
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00016openalex-snapshot
Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00274openalex-snapshot
Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00558openalex-snapshot
Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00124openalex-snapshot
Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00476openalex-snapshot
New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00517openalex-snapshot
Detailed Investigation of the Outstanding Peroxyl Radical Scavenging Activity of Two Novel Amino-Pyridinol-Based Compounds
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00526openalex-snapshot
Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.0c00385openalex-snapshot
Learning Molecular Representations for Medicinal Chemistry
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c00340openalex-snapshot
Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.5b00762openalex-snapshot
QSAR Modeling and Prediction of Drug–Drug Interactions
inferred← citeshttps://doi.org/10.1021/acsenvironau.2c00010openalex-snapshot
Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.8b00603openalex-snapshot
Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database
inferred← citeshttps://doi.org/10.1021/acsomega.2c05518openalex-snapshot
Chalcone Derivatives with a High Potential as Multifunctional Antioxidant Neuroprotectors
inferred← citeshttps://doi.org/10.1021/acsomega.7b00274openalex-snapshot
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
inferred← citeshttps://doi.org/10.1021/acsomega.8b03173openalex-snapshot
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees
inferred← citeshttps://doi.org/10.1021/acsptsci.2c00049openalex-snapshot
Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents
inferred← citeshttps://doi.org/10.1021/ci100072zopenalex-snapshot
Pharmaceutical Perspectives of Nonlinear QSAR Strategies
inferred← citeshttps://doi.org/10.1021/ci100176xopenalex-snapshot
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
inferred← citeshttps://doi.org/10.1021/ci100305gopenalex-snapshot
Consensus Ranking Approach to Understanding the Underlying Mechanism With QSAR
inferred← citeshttps://doi.org/10.1021/ci200028nopenalex-snapshot
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers
inferred← citeshttps://doi.org/10.1021/ci200123yopenalex-snapshot
Statistical Analysis and Compound Selection of Combinatorial Libraries for Soluble Epoxide Hydrolase
inferred← citeshttps://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferred← citeshttps://doi.org/10.1021/ci300060bopenalex-snapshot
Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1021/ci300338wopenalex-snapshot
Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
inferred← citeshttps://doi.org/10.1021/ci300421nopenalex-snapshot
Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci400213dopenalex-snapshot
Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions
inferred← citeshttps://doi.org/10.1021/ci500581qopenalex-snapshot
Study of Chromatographic Retention of Natural Terpenoids by Chemoinformatic Tools
inferred← citeshttps://doi.org/10.1021/ci500715eopenalex-snapshot
Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO
inferred← citeshttps://doi.org/10.1021/ci800151mopenalex-snapshot
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1021/ci900203nopenalex-snapshot
Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity
inferred← citeshttps://doi.org/10.1021/cr030440jopenalex-snapshot
Modeling Kinetics of Subcellular Disposition of Chemicals
inferred← citeshttps://doi.org/10.1021/envhealth.4c00026openalex-snapshot
Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1021/jm4004285openalex-snapshot
QSAR Modeling: Where Have You Been? Where Are You Going To?
inferred← citeshttps://doi.org/10.1021/mp100423uopenalex-snapshot
Why QSAR Fails: An Empirical Evaluation Using Conventional Computational Approach
inferred← citeshttps://doi.org/10.1021/nn1013484openalex-snapshot
Quantitative Nanostructure−Activity Relationship Modeling
inferred← citeshttps://doi.org/10.1021/tx300279fopenalex-snapshot
Locally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose
inferred← citeshttps://doi.org/10.1021/tx300393vopenalex-snapshot
Predicting Chemical Ocular Toxicity Using a Combinatorial QSAR Approach
inferred← citeshttps://doi.org/10.1021/tx400110fopenalex-snapshot
Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification
inferred← citeshttps://doi.org/10.1021/tx500100mopenalex-snapshot
QSTR Modeling for Qualitative and Quantitative Toxicity Predictions of Diverse Chemical Pesticides in Honey Bee for Regulatory Purposes
inferred← citeshttps://doi.org/10.1021/tx900189popenalex-snapshot
Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
inferred← citeshttps://doi.org/10.1021/tx900326kopenalex-snapshot
Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species
inferred← citeshttps://doi.org/10.1021/tx900451ropenalex-snapshot
Modeling Liver-Related Adverse Effects of Drugs Using kNearest Neighbor Quantitative Structure−Activity Relationship Method
inferred← citeshttps://doi.org/10.1038/s41374-020-00477-2openalex-snapshot
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches
inferred← citeshttps://doi.org/10.1038/s41467-021-23720-wopenalex-snapshot
Algebraic graph-assisted bidirectional transformers for molecular property prediction
inferred← citeshttps://doi.org/10.1039/9781849732093-00606openalex-snapshot
Using In Silico Toxicity Predictions: Case Studies for Skin Sensitisation
inferred← citeshttps://doi.org/10.1039/c2tb00366jopenalex-snapshot
Rationalization of the entrapping of bioactive molecules into a series of functionalized porous zirconium terephthalate MOFs
inferred← citeshttps://doi.org/10.1039/c4np00068dopenalex-snapshot
Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review
inferred← citeshttps://doi.org/10.1039/c6gc01836jopenalex-snapshot
QSAR models of human data can enrich or replace LLNA testing for human skin sensitization
inferred← citeshttps://doi.org/10.1039/c6ra00549gopenalex-snapshot
Computational-aided design of melatonin analogues with outstanding multifunctional antioxidant capacity
inferred← citeshttps://doi.org/10.1039/d0cp00305kopenalex-snapshot
Are 2D fingerprints still valuable for drug discovery?
inferred← citeshttps://doi.org/10.1039/d5em00357aopenalex-snapshot
Quantifying uncertainty in predicted chemical partition ratios required for chemical assessments
inferred← citeshttps://doi.org/10.1073/pnas.1818763116openalex-snapshot
Machine-learning approach to the design of OSDAs for zeolite beta
inferred← citeshttps://doi.org/10.1080/00268976.2024.2331620openalex-snapshot
Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
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