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An Automated Group Contribution Method in Predicting Aquatic Toxicity: The Diatomic Fragment Approach
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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/aic.690300119openalex-snapshot
Estimation of critical properties with group contribution methods
inferredcites →https://doi.org/10.1016/0045-6535(92)90280-5openalex-snapshot
Classifying environmental pollutants
inferredcites →https://doi.org/10.1016/0378-3812(94)02593-popenalex-snapshot
Estimation of the acentric factor and the liquid molar volume at 298 K using a new group contribution method
inferredcites →https://doi.org/10.1021/ci0401219openalex-snapshot
Combining Unsupervised and Supervised Artificial Neural Networks to PredictAquatic Toxicity
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/minf.200900005openalex-snapshot
Support Vector Machine (SVM) as Alternative Tool to Assign Acute Aquatic Toxicity Warning Labels to Chemicals
inferred← citeshttps://doi.org/10.1016/j.aquatox.2022.106379openalex-snapshot
Ensemble multiclassification model for aquatic toxicity of organic compounds
inferred← citeshttps://doi.org/10.1016/j.aquatox.2025.107244openalex-snapshot
CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction
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Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2013.05.017openalex-snapshot
Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches
inferred← citeshttps://doi.org/10.1016/j.jare.2024.06.002openalex-snapshot
Multi-task aquatic toxicity prediction model based on multi-level features fusion
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2006.08.030openalex-snapshot
Correlation of the toxicity of organic compounds to tadpoles using the Abraham model
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.2005.02.035openalex-snapshot
Quantitative aquatic toxicity prediction: using group contribution and classification methods on polar and non-polar narcotics
inferred← citeshttps://doi.org/10.1021/ci200075gopenalex-snapshot
Identification of Toxifying and Detoxifying Moieties for Mutagenicity Prediction by Priority Assessment
inferred← citeshttps://doi.org/10.1021/ci300350ropenalex-snapshot
Estimation of Environment-Related Properties of Chemicals for Design of Sustainable Processes: Development of Group-Contribution+ (GC+) Property Models and Uncertainty Analysis
inferred← citeshttps://doi.org/10.1021/ci7004065openalex-snapshot
Definition and Detection of Outliers in Chemical Space
inferred← citeshttps://doi.org/10.1021/tx050164zopenalex-snapshot
Chemical Toxicity Correlations for Several Fish Species Based on the Abraham Solvation Parameter Model
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/c8em00220gopenalex-snapshot
In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods
inferred← citeshttps://doi.org/10.1039/d0ra02701dopenalex-snapshot
A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling
inferred← citeshttps://doi.org/10.1080/1062936x.2010.502300openalex-snapshot
Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1018938openalex-snapshot
A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1137353openalex-snapshot
QSAR study of the acute toxicity to fathead minnow based on a large dataset
inferred← citeshttps://doi.org/10.5012/bkcs.2012.33.2.613openalex-snapshot
Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches
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