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Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Empirical and Physics-Based Calculations of Physical–Chemical Properties
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Computational Approaches for Drug-Induced Liver Injury (DILI) Prediction: State of the Art and Challenges
inferred← citeshttps://doi.org/10.1016/b978-0-12-801505-6.00007-7openalex-snapshot
Validation of QSAR Models
inferred← citeshttps://doi.org/10.1016/b978-0-12-824156-1.00014-5openalex-snapshot
QSAR approach for combating cancer cells
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00058-3openalex-snapshot
Empirical and physics-based calculations of physical-chemical properties
inferred← citeshttps://doi.org/10.1016/b978-0-443-45105-8.00006-8openalex-snapshot
Approaches of computer-aided drug design/artificial intelligence in drug discovery
inferred← citeshttps://doi.org/10.1016/j.aac.2024.11.005openalex-snapshot
In silico modeling and experimental validation of 2-oxoimidazolidin-4-sulfonamides as low-toxicity fungicides against Phytophthora infestans
inferred← citeshttps://doi.org/10.1016/j.aca.2012.11.002openalex-snapshot
Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study
inferred← citeshttps://doi.org/10.1016/j.ailsci.2023.100070openalex-snapshot
Conformal efficiency as a metric for comparative model assessment befitting federated learning
inferred← citeshttps://doi.org/10.1016/j.ailsci.2025.100128openalex-snapshot
Enhancing uncertainty quantification in drug discovery with censored regression labels
inferred← citeshttps://doi.org/10.1016/j.arabjc.2014.11.063openalex-snapshot
Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer
inferred← citeshttps://doi.org/10.1016/j.bbagen.2016.05.019openalex-snapshot
Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine
inferred← citeshttps://doi.org/10.1016/j.bmc.2013.05.061openalex-snapshot
Autotaxin inhibition: Development and application of computational tools to identify site-selective lead compounds
inferred← citeshttps://doi.org/10.1016/j.bpc.2024.107179openalex-snapshot
Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein
inferred← citeshttps://doi.org/10.1016/j.cej.2020.126627openalex-snapshot
Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds
inferred← citeshttps://doi.org/10.1016/j.cej.2020.127998openalex-snapshot
Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation
inferred← citeshttps://doi.org/10.1016/j.chemolab.2011.01.011openalex-snapshot
QSAR modeling of antitubercular activity of diverse organic compounds
inferred← citeshttps://doi.org/10.1016/j.chemolab.2011.09.003openalex-snapshot
A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
inferred← citeshttps://doi.org/10.1016/j.chemolab.2013.08.005openalex-snapshot
Estimation of predictive accuracy of soft sensor models based on data density
inferred← citeshttps://doi.org/10.1016/j.chemolab.2014.06.005openalex-snapshot
Flour concentration prediction using GAPLS and GAWLS focused on data sampling issues and applicability domain
inferred← citeshttps://doi.org/10.1016/j.chemolab.2014.06.008openalex-snapshot
Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.09.011openalex-snapshot
QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors
inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.04.001openalex-snapshot
Detection of nonlinearity in soil property prediction models based on near-infrared spectroscopy
inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.04.006openalex-snapshot
Fuzzy clustering as rational partition method for QSAR
inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.09.007openalex-snapshot
A strategy on the definition of applicability domain of model based on population analysis
inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.09.018openalex-snapshot
A new measure of regression model accuracy that considers applicability domains
inferred← citeshttps://doi.org/10.1016/j.chemolab.2018.03.001openalex-snapshot
k-nearest neighbor normalized error for visualization and reconstruction – A new measure for data visualization performance
inferred← citeshttps://doi.org/10.1016/j.chemolab.2018.04.015openalex-snapshot
Automatic outlier sample detection based on regression analysis and repeated ensemble learning
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2010.11.043openalex-snapshot
In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2023.140660openalex-snapshot
Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2016.09.012openalex-snapshot
1,3-Oxazole derivatives as potential anticancer agents: Computer modeling and experimental study
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2020.107407openalex-snapshot
1,3-Oxazole derivatives of cytisine as potential inhibitors of glutathione reductase of Candida spp.: QSAR modeling, docking analysis and experimental study of new anti-Candida agents
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109480openalex-snapshot
ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning
inferred← citeshttps://doi.org/10.1016/j.comtox.2024.100309openalex-snapshot
New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity
inferred← citeshttps://doi.org/10.1016/j.corsci.2025.113080openalex-snapshot
Large language models predicting the corrosion inhibition efficiency of magnesium dissolution modulators
inferred← citeshttps://doi.org/10.1016/j.drudis.2009.09.010openalex-snapshot
Computational toxicology—a tool for early safety evaluation
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.drudis.2020.11.027openalex-snapshot
Uncertainty quantification in drug design
inferred← citeshttps://doi.org/10.1016/j.drudis.2025.104392openalex-snapshot
Chemical space visual navigation in the era of deep learning and Big Data
inferred← citeshttps://doi.org/10.1016/j.dyepig.2022.110470openalex-snapshot
Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2025.117824openalex-snapshot
Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2026.119804openalex-snapshot
A machine learning-based QSAR model for predicting toxicity of commercial pesticides to Eisenia fetida
inferred← citeshttps://doi.org/10.1016/j.ejmech.2010.11.042openalex-snapshot
Predicting hERG activities of compounds from their 3D structures: Development and evaluation of a global descriptors based QSAR model
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.2017.08.008openalex-snapshot
Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform
inferred← citeshttps://doi.org/10.1016/j.fct.2017.09.037openalex-snapshot
Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology
inferred← citeshttps://doi.org/10.1016/j.fct.2026.116422openalex-snapshot
Artificial intelligence and machine learning in fragrance safety assessment: Current applications and future opportunities at RIFM
inferred← citeshttps://doi.org/10.1016/j.hazadv.2026.101611openalex-snapshot
Investigating the mechanisms of emerging and legacy contaminants bioconcentration in fish tissues using machine learning models
inferred← citeshttps://doi.org/10.1016/j.jare.2023.05.002openalex-snapshot
Modulating autophagy to treat diseases: A revisited review on in silico methods
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.jocs.2013.10.003openalex-snapshot
Sensitivity, specificity, and accuracy of predictive models on phenols toxicity
inferred← citeshttps://doi.org/10.1016/j.jtbi.2015.01.008openalex-snapshot
QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors
inferred← citeshttps://doi.org/10.1016/j.mencom.2021.11.003openalex-snapshot
Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow
inferred← citeshttps://doi.org/10.1016/j.molliq.2020.114055openalex-snapshot
Quantitative structure toxicity analysis of ionic liquids toward acetylcholinesterase enzyme using novel QSTR models with index of ideality of correlation and correlation contradiction index
inferred← citeshttps://doi.org/10.1016/j.molliq.2022.120247openalex-snapshot
Machine learning models for phase transition and decomposition temperature of ionic liquids
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.susmat.2024.e00862openalex-snapshot
Computational and electrochemistry of effective triazolyl-benzimidazolone inhibitors in aggressive environment
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.trac.2025.118536openalex-snapshot
Deep learning-driven screening of emerging contaminants: Innovations and technological advances
inferred← citeshttps://doi.org/10.1016/j.vascn.2010.02.005openalex-snapshot
Troubleshooting computational methods in drug discovery
inferred← citeshttps://doi.org/10.1016/s0079-6468(10)49004-9openalex-snapshot
Computational Analysis of Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00411openalex-snapshot
In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00162openalex-snapshot
Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00238openalex-snapshot
Ensemble Quantitative Read-Across Structure–Activity Relationship Algorithm for Predicting Skin Cytotoxicity
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.4c00244openalex-snapshot
Estimation of the Skin Sensitization Potential of Chemicals of the Acyl Domain Using DFT-Based Calculations
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.5b00481openalex-snapshot
ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00018openalex-snapshot
Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00033openalex-snapshot
Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00273openalex-snapshot
Which Modern AI Methods Provide Accurate Predictions of Toxicological End Points? Analysis of Tox24 Challenge Results
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.6c00065openalex-snapshot
Beyond Heuristics: A Model-Agnostic Framework for Uncertainty Quantification in QSAR via Adaptive Conformal Prediction
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00447openalex-snapshot
In Silico Prediction of Human Renal Clearance of Compounds Using Quantitative Structure-Pharmacokinetic Relationship Models
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.est.3c00653openalex-snapshot
Advancing Computational Toxicology by Interpretable Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.est.3c05643openalex-snapshot
Chemical Space Covered by Applicability Domains of Quantitative Structure–Property Relationships and Semiempirical Relationships in Chemical Assessments
inferred← citeshttps://doi.org/10.1021/acs.est.9b04833openalex-snapshot
Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List
inferred← citeshttps://doi.org/10.1021/acs.estlett.4c00524openalex-snapshot
Screening Environmentally Benign Ionic Liquids for CO 2 Absorption Using Representation Uncertainty-Based Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.iecr.2c00971openalex-snapshot
Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00998openalex-snapshot
Trustworthiness, the Key to Grid-Based Map-Driven Predictive Model Enhancement and Applicability Domain Control
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01001openalex-snapshot
General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01294openalex-snapshot
GGL-Tox: Geometric Graph Learning for Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00181openalex-snapshot
xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning
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