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Top-Priority Fragment QSAR Approach in Predicting Pesticide Aquatic Toxicity

https://doi.org/10.1021/tx0601814
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.bmc.2004.09.003openalex-snapshot
Quantitative structure–activity relationship study using refractotopological state atom index on some neonicotinoid insecticides
inferredcites →https://doi.org/10.1016/j.bmcl.2005.12.085openalex-snapshot
QSAR models of quail dietary toxicity based on the graph of atomic orbitals
inferredcites →https://doi.org/10.1080/10629360008033227openalex-snapshot
A General QSAR Model for Predicting the Acute Toxicity of Pesticides toOncorhynchus mykiss

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/qsar.200710099openalex-snapshot
Review of (Quantitative) Structure–Activity Relationships for Acute Aquatic Toxicity
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_7openalex-snapshot
On Applications of QSARs in Food and Agricultural Sciences: History and Critical Review of Recent Developments
inferred← citeshttps://doi.org/10.1007/978-3-642-32750-6_5openalex-snapshot
Biological Activity and Toxicity: A Conceptual DFT Approach
inferred← citeshttps://doi.org/10.1007/s11356-022-19111-3openalex-snapshot
A critical review of effect modeling for ecological risk assessment of plant protection products
inferred← citeshttps://doi.org/10.1007/s42250-025-01239-7openalex-snapshot
Predicting Pesticide LC50 Toxicity Using Convolutional Neural Networks and Dragonfly-Support Vector Machine Methods with Quantitative Structure-Activity Relationship and 2D Molecular Fingerprints: A Case Study on Oncorhynchus Mykiss
inferred← citeshttps://doi.org/10.1016/b978-044452710-3/50005-7openalex-snapshot
Characterization of chemical structures
inferred← citeshttps://doi.org/10.1016/j.taap.2013.06.014openalex-snapshot
High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury
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/ci400130nopenalex-snapshot
Determination of Toxicant Mode of Action by Augmented Top Priority Fragment Class
inferred← citeshttps://doi.org/10.1021/ci7004065openalex-snapshot
Definition and Detection of Outliers in Chemical Space
inferred← citeshttps://doi.org/10.1021/tx400371wopenalex-snapshot
Multispecies QSAR Modeling for Predicting the Aquatic Toxicity of Diverse Organic Chemicals for Regulatory Toxicology
inferred← citeshttps://doi.org/10.1039/9781849732093-00031openalex-snapshot
Finding the Data to Develop and Evaluate (Q)SARs and Populate Categories for Toxicity Prediction
inferred← citeshttps://doi.org/10.1039/c7tx00144dopenalex-snapshot
In silico prediction of pesticide aquatic toxicity with chemical category approaches
inferred← citeshttps://doi.org/10.1080/1062936x.2011.569943openalex-snapshot
Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions
inferred← citeshttps://doi.org/10.1093/toxsci/kfp231openalex-snapshot
Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features
inferred← citeshttps://doi.org/10.1109/icitee49829.2020.9271739openalex-snapshot
Predicting Acute Aquatic Toxicity Towards Fathead Minnow (Pimephales Promelas) Using Neuro-Fuzzy Inference System (ANFIS)
inferred← citeshttps://doi.org/10.3390/ijms11103846openalex-snapshot
Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods
inferred← citeshttps://doi.org/10.4018/978-1-5225-6111-8.ch018openalex-snapshot
Computational Tools and Techniques to Predict Aquatic Toxicity of Some Halogenated Pollutants
inferred← citeshttps://doi.org/10.5806/ast.2011.24.6.544openalex-snapshot
Prediction and analysis of acute fish toxicity of pesticides to the rainbow trout using 2D-QSAR
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