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Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0300-483x(95)03034-dopenalex-snapshot
Assessing the risk assessment paradigm
inferredcites →https://doi.org/10.1016/s0165-1218(96)90092-0openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-7439(98)00125-7openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0300-483x(97)03631-7openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci00022a010openalex-snapshot
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inferredcites →https://doi.org/10.1021/ci960084oopenalex-snapshot
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inferredcites →https://doi.org/10.1109/72.668887openalex-snapshot
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inferredcites →https://doi.org/10.1177/026119299702500303openalex-snapshot
The Development and Validation of Expert Systems for Predicting Toxicity
inferredcites →https://doi.org/10.1289/ehp.919611openalex-snapshot
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9780470744307.gat079openalex-snapshot
Structure–Activity Relationships for Carcinogenic Potential
inferred← citeshttps://doi.org/10.1002/9783527629213.ch25openalex-snapshot
Quantum‐Chemical Descriptors in QSAR/QSPR Modeling: Achievements, Perspectives and Trends
inferred← citeshttps://doi.org/10.1002/chin.200007234openalex-snapshot
ChemInform Abstract: Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen‐Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network.
inferred← citeshttps://doi.org/10.1002/chin.200444298openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/etc.5620220505openalex-snapshot
DATABASE MINING WITH ADAPTIVE FUZZY PARTITION: APPLICATION TO THE PREDICTION OF PESTICIDE TOXICITY ON RATS
inferred← citeshttps://doi.org/10.1002/qsar.200390014openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/1-4020-4280-91376156openalex-snapshot
Allelopathy : a physiological process with ecological implications
inferred← citeshttps://doi.org/10.1007/1-4020-4280-9_5openalex-snapshot
Toxicity in allelopathy: In Silico Approach
inferred← citeshttps://doi.org/10.1007/3-540-48219-9_13openalex-snapshot
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
inferred← citeshttps://doi.org/10.1007/978-1-0716-0150-1_6openalex-snapshot
Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
inferred← citeshttps://doi.org/10.1007/978-3-642-19615-7_13openalex-snapshot
Annotation and classification of chemical space in chemogenomics
inferred← citeshttps://doi.org/10.1007/s11030-009-9113-4openalex-snapshot
QSAR modelling of carcinogenicity by balance of correlations
inferred← citeshttps://doi.org/10.1007/s11030-009-9190-4openalex-snapshot
Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses
inferred← citeshttps://doi.org/10.1016/b978-0-12-801559-9.00008-9openalex-snapshot
Impact of Artificial Neural Networks in QSAR and Computational Modeling
inferred← citeshttps://doi.org/10.1016/b978-044452710-3/50006-9openalex-snapshot
Algorithms for (Q)SAR model building
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.03.065openalex-snapshot
Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2015.05.056openalex-snapshot
Predicting carcinogenicity of organic compounds based on CPDB
inferred← citeshttps://doi.org/10.1016/j.comtox.2025.100358openalex-snapshot
Chronic and acute eco-toxicity modeling of carcinogenic and hazardous air pollutants toward humans for critical risk assessment and regulatory decision-making
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2023.114806openalex-snapshot
Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database
inferred← citeshttps://doi.org/10.1016/j.fct.2016.09.005openalex-snapshot
Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2021.120713openalex-snapshot
Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing
inferred← citeshttps://doi.org/10.1016/j.jmgm.2008.04.004openalex-snapshot
A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID study of the species differences in the carcinogenicity of benzene derivatives chemicals
inferred← citeshttps://doi.org/10.1016/j.taap.2013.06.029openalex-snapshot
Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches
inferred← citeshttps://doi.org/10.1016/j.theochem.2004.01.023openalex-snapshot
QSAR modelling of aldehyde toxicity by means of optimisation of correlation weights of nearest neighbouring codes
inferred← citeshttps://doi.org/10.1016/j.yrtph.2010.09.016openalex-snapshot
Improved in silico prediction of carcinogenic potency (TD50) and the risk specific dose (RSD) adjusted Threshold of Toxicological Concern (TTC) for genotoxic chemicals and pharmaceutical impurities
inferred← citeshttps://doi.org/10.1016/s0027-5107(01)00161-0openalex-snapshot
Predicting toxicity: a mechanism of action model of chemical mutagenicity
inferred← citeshttps://doi.org/10.1016/s0223-5234(01)01233-8openalex-snapshot
Database mining applied to central nervous system (CNS) activity
inferred← citeshttps://doi.org/10.1016/s0273-2300(03)00071-0openalex-snapshot
Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices
inferred← citeshttps://doi.org/10.1016/s1367-5931(00)00089-2openalex-snapshot
Optimization of chemical libraries by neural networks
inferred← citeshttps://doi.org/10.1021/acs.jpcc.8b05298openalex-snapshot
Detachment of HCO3– from the Active Site of Carbonic Anhydrase: Molecular Dynamics Simulation and Machine Learning
inferred← citeshttps://doi.org/10.1021/ci000134wopenalex-snapshot
Interpretation of Quantitative Structure−Property and −Activity Relationships
inferred← citeshttps://doi.org/10.1021/ci0003348openalex-snapshot
Results of a New Classification Algorithm Combining K Nearest Neighbors and Recursive Partitioning
inferred← citeshttps://doi.org/10.1021/ci025520nopenalex-snapshot
The Importance of Scaling in Data Mining for Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/ci025585qopenalex-snapshot
Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling
inferred← citeshttps://doi.org/10.1021/ci0256182openalex-snapshot
Modeling Toxicity by Using Supervised Kohonen Neural Networks
inferred← citeshttps://doi.org/10.1021/ci034193wopenalex-snapshot
QSAR in Ecotoxicity: An Overview of Modern Classification Techniques
inferred← citeshttps://doi.org/10.1021/ci0401219openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci049652nopenalex-snapshot
Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
inferred← citeshttps://doi.org/10.1021/ci0499368openalex-snapshot
Prediction of Noninteractive Mixture Toxicity of Organic Compounds Based on a Fuzzy Set Method
inferred← citeshttps://doi.org/10.1021/ci990116iopenalex-snapshot
Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds
inferred← citeshttps://doi.org/10.1021/cr030049yopenalex-snapshot
Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches
inferred← citeshttps://doi.org/10.1021/cr9901079openalex-snapshot
Quantitative Structure−Activity Relationships of Mutagenic and Carcinogenic Aromatic Amines
inferred← citeshttps://doi.org/10.1023/a:1025361621494openalex-snapshot
In silico prediction of drug toxicity
inferred← citeshttps://doi.org/10.1023/a:1026084514236openalex-snapshot
QSAR Modeling of Genotoxicity on Non-congeneric Sets of Organic Compounds
inferred← citeshttps://doi.org/10.1023/a:1026132402754openalex-snapshot
ANVAS: Artificial Neural Variables Adaptation System for descriptor selection
inferred← citeshttps://doi.org/10.1080/10590501.2011.629973openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/10629360701428920openalex-snapshot
Toxicity study of allelochemical-like pesticides by a combination of 3D-QSAR, docking, Local Binding Energy (LBE) and GRID approaches
inferred← citeshttps://doi.org/10.1080/10629360903563250openalex-snapshot
Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals
inferred← citeshttps://doi.org/10.1080/10659360600636212openalex-snapshot
Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures
inferred← citeshttps://doi.org/10.1080/17425255.2024.2356162openalex-snapshot
Recent progress in machine learning approaches for predicting carcinogenicity in drug development
inferred← citeshttps://doi.org/10.1080/26896583.2021.1883964openalex-snapshot
A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity
inferred← citeshttps://doi.org/10.1109/isda.2005.52openalex-snapshot
Hybrid intelligent systems for predictive toxicology - a distributed approach
inferred← citeshttps://doi.org/10.1109/kes.2000.885784openalex-snapshot
Clustering and classification techniques to assess aquatic toxicity
inferred← citeshttps://doi.org/10.1109/nafips.2001.943706openalex-snapshot
Integrating rules and neural nets for carcinogenicity prediction
inferred← citeshttps://doi.org/10.11190/jer.11.111openalex-snapshot
Development of the Chemical Safety Database System CAESAR
inferred← citeshttps://doi.org/10.1142/s0218001404003423openalex-snapshot
MULTICLASS CLASSIFIER FROM A COMBINATION OF LOCAL EXPERTS: TOWARD DISTRIBUTED COMPUTATION FOR REAL-PROBLEM CLASSIFIERS
inferred← citeshttps://doi.org/10.1142/s0218213007003308openalex-snapshot
E-MODELLING: FOUNDATIONS AND CASES FOR APPLYING AI TO LIFE SCIENCES
inferred← citeshttps://doi.org/10.1145/1147234.1147245openalex-snapshot
Data-driven modeling and prediction of acute toxicity of pesticide residues
inferred← citeshttps://doi.org/10.1186/1752-153x-4-s1-s3openalex-snapshot
New public QSAR model for carcinogenicity
inferred← citeshttps://doi.org/10.1186/s13040-016-0098-0openalex-snapshot
Machine learning algorithms for mode-of-action classification in toxicity assessment
inferred← citeshttps://doi.org/10.1201/9780203010822-9openalex-snapshot
QSARs for the Mutagenicity and Carcinogenicity of the Aromatic Amines
inferred← citeshttps://doi.org/10.1201/9780203010822.ch3openalex-snapshot
QSARs for Individual Classes of Chemical Mutagens and Carcinogens
inferred← citeshttps://doi.org/10.1201/9780203010822.ch4openalex-snapshot
QSARs for the Mutagenicity and Carcinogenicity of the Aromatic Amines
inferred← citeshttps://doi.org/10.1201/9780203642627.ch5openalex-snapshot
Whole Molecule and Atom-Based Topological Descriptors
inferred← citeshttps://doi.org/10.1205/psep.04389openalex-snapshot
Using an Ensemble of Neural Based QSARs for the Prediction of Toxicological Properties of Chemical Contaminants
inferred← citeshttps://doi.org/10.2116/analsci.26.897openalex-snapshot
A QSAR Study for Modeling of 8-Azaadenine Analogues Proposed as A1 Adenosine Receptor Antagonists Using Genetic Algorithm Coupling Adaptive Neuro-Fuzzy Inference System (ANFIS)
inferred← citeshttps://doi.org/10.2477/jccj.1.23openalex-snapshot
Prediction of Carcinogenicity of Chlorine-containing Organic Compounds by Neural Network.
inferred← citeshttps://doi.org/10.2477/jccj.4.89openalex-snapshot
Neural Network Prediction of Carcinogenicity of Diverse Organic Compounds
inferred← citeshttps://doi.org/10.2964/jsik.16.3_63openalex-snapshot
Toxicity Information and Its QSAR Prediction of Chemical Substances
inferred← citeshttps://doi.org/10.3390/molecules26010127openalex-snapshot
QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors
inferred← citeshttps://doi.org/10.48550/arxiv.0910.0542openalex-snapshot
Pre-processing in AI based Prediction of QSARs
inferred← citeshttps://doi.org/10.5772/7222openalex-snapshot
Fuzzy Filtering: A Mathematical Theory and Applications in Life Science
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