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Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors

https://doi.org/10.1021/ci050520j
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-57292-0_10openalex-snapshot
Classification and Regression Trees
inferredcites →https://doi.org/10.1007/978-3-642-74140-1openalex-snapshot
Physical Property Prediction in Organic Chemistry
inferredcites →https://doi.org/10.1016/0040-4020(80)80168-2openalex-snapshot
Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges
inferredcites →https://doi.org/10.1016/0278-6915(85)90050-xopenalex-snapshot
Mutagenicity tests of lipid oxidation products in Salmonella typhimurium: Monohydroperoxides and secondary oxidation products of methyl linoleate and methyl linolenate
inferredcites →https://doi.org/10.1016/j.bmc.2005.09.047openalex-snapshot
QSAR analysis of phenolic antioxidants using MOLMAP descriptors of local properties
inferredcites →https://doi.org/10.1016/j.toxlet.2003.09.019openalex-snapshot
INRS activities on risk assessment of glycol ethers
inferredcites →https://doi.org/10.1016/j.toxlet.2003.12.077openalex-snapshot
The INSERM expert review on glycol ethers: findings and recommendations
inferredcites →https://doi.org/10.1016/s0027-5107(01)00161-0openalex-snapshot
Predicting toxicity: a mechanism of action model of chemical mutagenicity
inferredcites →https://doi.org/10.1016/s0169-7439(01)00181-2openalex-snapshot
Mixture deconvolution and analysis of Ames mutagenicity data
inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
inferredcites →https://doi.org/10.1021/ci034254qopenalex-snapshot
Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds
inferredcites →https://doi.org/10.1021/ci050039topenalex-snapshot
Graph Kernels for Molecular Structure−Activity Relationship Analysis with Support Vector Machines
inferredcites →https://doi.org/10.1021/ci0502707openalex-snapshot
Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers
inferredcites →https://doi.org/10.1021/cr030049yopenalex-snapshot
Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches
inferredcites →https://doi.org/10.1021/ja00073a034openalex-snapshot
A combined application of two different neural network types for the prediction of chemical reactivity
inferredcites →https://doi.org/10.1021/jm040835aopenalex-snapshot
Derivation and Validation of Toxicophores for Mutagenicity Prediction
inferredcites →https://doi.org/10.1021/tx030049topenalex-snapshot
Identification of the Structural Requirements for Mutagenicity by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals I: TA100 Model
inferredcites →https://doi.org/10.1080/1062936032000101484openalex-snapshot
Prediction of aromatic amines mutagenicity from theoretical molecular descriptors
inferredcites →https://doi.org/10.1080/15376520590945667openalex-snapshot
Current Status of Short-Term Tests for Evaluation of Genotoxicity, Mutagenicity, and Carcinogenicity of Environmental Chemicals and NCEs
inferredcites →https://doi.org/10.1093/mutage/geh043openalex-snapshot
Three new consensus QSAR models for the prediction of Ames genotoxicity

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inferred← citeshttps://doi.org/10.1002/9781119681397.ch23openalex-snapshot
In SilicoPlatforms for Predictive Ecotoxicology
inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/9783527816880.ch10openalex-snapshot
Calculation of Structure Descriptors
inferred← citeshttps://doi.org/10.1002/adts.202200503openalex-snapshot
The Melting Point Profile of Organic Molecules: A Chemoinformatic Approach
inferred← citeshttps://doi.org/10.1002/cem.1375openalex-snapshot
Feature importance sampling‐based adaptive random forest as a useful tool to screen underlying lead compounds
inferred← citeshttps://doi.org/10.1002/cem.2615openalex-snapshot
A 10‐year survey of trace metals in sediments using self‐organizing maps
inferred← citeshttps://doi.org/10.1002/chin.200715208openalex-snapshot
Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors.
inferred← citeshttps://doi.org/10.1002/cphc.201900593openalex-snapshot
Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid‐Containing Systems
inferred← citeshttps://doi.org/10.1002/ddr.20410openalex-snapshot
In silico structure‐activity‐relationship (SAR) models from machine learning: a review
inferred← citeshttps://doi.org/10.1002/ece3.6771openalex-snapshot
A brain and a head for a different habitat: Size variation in four morphs of Arctic charr (Salvelinus alpinus(L.)) in a deep oligotrophic lake
inferred← citeshttps://doi.org/10.1002/jcc.21198openalex-snapshot
Application of MOLMAP approach for QSAR modeling of various biological activities using substituent electronic descriptors
inferred← citeshttps://doi.org/10.1002/jcc.21411openalex-snapshot
In silico prediction and screening of γ‐secretase inhibitors by molecular descriptors and machine learning methods
inferred← citeshttps://doi.org/10.1002/minf.201000001openalex-snapshot
Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds
inferred← citeshttps://doi.org/10.1002/minf.201100107openalex-snapshot
Automated QSAR with a Hierarchy of Global and Local Models
inferred← citeshttps://doi.org/10.1002/minf.202400018openalex-snapshot
Chemoinformatic regression methods and their applicability domain
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/10.1007/978-1-4419-0197-2_2openalex-snapshot
(Q)SAR Models for Genotoxicity Assessment
inferred← citeshttps://doi.org/10.1007/978-1-4939-7899-1_5openalex-snapshot
Machine Learning Methods in Computational Toxicology
inferred← citeshttps://doi.org/10.1007/978-1-60761-839-3_13openalex-snapshot
Classification of Chemical Reactions and Chemoinformatic Processing of Enzymatic Transformations
inferred← citeshttps://doi.org/10.1007/978-3-319-08156-4_11openalex-snapshot
Predicting Biological Activity of 2,4,6-trisubstituted 1,3,5-triazines Using Random Forest
inferred← citeshttps://doi.org/10.1007/978-3-319-27555-0_5openalex-snapshot
Predicting Crystallisability of Organic Molecules Using Statistical Modelling Techniques
inferred← citeshttps://doi.org/10.1007/978-3-319-33609-1_12openalex-snapshot
An EEG Classification Approach Based on Intrinsic Signal Properties and Wavelets
inferred← citeshttps://doi.org/10.1007/978-3-540-74205-0_120openalex-snapshot
Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity
inferred← citeshttps://doi.org/10.1007/s00894-025-06619-xopenalex-snapshot
The use of an interpretable machine-learning approach to disclose the molecular assemblage on melting transition
inferred← citeshttps://doi.org/10.1007/s10822-008-9225-4openalex-snapshot
Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction
inferred← citeshttps://doi.org/10.1007/s10822-009-9275-2openalex-snapshot
Machine learning of chemical reactivity from databases of organic reactions
inferred← citeshttps://doi.org/10.1007/s10822-012-9561-2openalex-snapshot
Computer-aided design of novel antibacterial 3-hydroxypyridine-4-ones: application of QSAR methods based on the MOLMAP approach
inferred← citeshttps://doi.org/10.1007/s13369-024-09342-6openalex-snapshot
A Graph-Based Transformer Neural Network for Multi-Label ADR Prediction
inferred← citeshttps://doi.org/10.1016/b978-0-12-801238-3.11534-xopenalex-snapshot
Computational Toxicology
inferred← citeshttps://doi.org/10.1016/j.aca.2007.10.029openalex-snapshot
Classification of multiway analytical data based on MOLMAP approach
inferred← citeshttps://doi.org/10.1016/j.aca.2010.07.044openalex-snapshot
Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods
inferred← citeshttps://doi.org/10.1016/j.aca.2012.12.027openalex-snapshot
Effects of supervised Self Organising Maps parameters on classification performance
inferred← citeshttps://doi.org/10.1016/j.carres.2011.02.017openalex-snapshot
Prediction of the anomeric configuration, type of linkage, and residues in disaccharides from 1D 13C NMR data
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
inferred← citeshttps://doi.org/10.1016/j.chemphys.2025.112996openalex-snapshot
Machine learning and DFT elucidation of proton-coupled resonance mechanisms in acid-catalyzed ethyl 4-chloro-2-methoxyiminoacetoacetate isomerization
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106524openalex-snapshot
The prediction of molecular toxicity based on BiGRU and GraphSAGE
inferred← citeshttps://doi.org/10.1016/j.est.2024.112292openalex-snapshot
AI-based approach for predicting the storage performance of zinc oxide-based supercapacitor electrodes
inferred← citeshttps://doi.org/10.1016/j.fuel.2015.04.064openalex-snapshot
Differentiation of weathered oils using infrared indexes and self-organizing maps
inferred← citeshttps://doi.org/10.1016/j.jece.2025.119962openalex-snapshot
AsRTNet: A multimodal feature fusion model for improved prediction of retention time of arsenic compounds in environmental and metabolism samples
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.06.005openalex-snapshot
Predicting human liver microsomal stability with machine learning techniques
inferred← citeshttps://doi.org/10.1016/j.jmgm.2019.05.006openalex-snapshot
A nonparametric weighted feature extraction-based method for c-Jun N-terminal kinase-3 inhibitor prediction
inferred← citeshttps://doi.org/10.1016/j.jpha.2025.101411openalex-snapshot
Unveiling optimal molecular features for hERG insights with automatic machine learning
inferred← citeshttps://doi.org/10.1016/j.toxlet.2009.06.176openalex-snapshot
Prediction of mutagenicity based on empirical physicochemical descriptors
inferred← citeshttps://doi.org/10.1021/ac100706jopenalex-snapshot
Self Organizing Maps for Analysis of Polycyclic Aromatic Hydrocarbons 3-Way Data from Spilled Oils
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00227openalex-snapshot
An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01373openalex-snapshot
Using Machine Learning To Predict Partition Coefficient (Log P ) and Distribution Coefficient (Log D ) with Molecular Descriptors and Liquid Chromatography Retention Time
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00160openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00473openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01663openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00612openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c00129openalex-snapshot
Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma
inferred← citeshttps://doi.org/10.1021/acsomega.1c06805openalex-snapshot
Improving Compound Activity Classification via Deep Transfer and Representation Learning
inferred← citeshttps://doi.org/10.1021/ci800164uopenalex-snapshot
Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening
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/ci9003392openalex-snapshot
The Ensemble Bridge Algorithm: A New Modeling Tool for Drug Discovery Problems
inferred← citeshttps://doi.org/10.1021/ci900419kopenalex-snapshot
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
inferred← citeshttps://doi.org/10.1021/ie0704647openalex-snapshot
Prediction of Melting Points of Organic Compounds Using Extreme Learning Machines
inferred← citeshttps://doi.org/10.1038/npre.2011.6116openalex-snapshot
In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches
inferred← citeshttps://doi.org/10.1039/b713373aopenalex-snapshot
Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification
inferred← citeshttps://doi.org/10.1039/b811008eopenalex-snapshot
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inferred← citeshttps://doi.org/10.1039/c3mb70218aopenalex-snapshot
Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1
inferred← citeshttps://doi.org/10.1039/c4ce02403fopenalex-snapshot
A random forest model for predicting the crystallisability of organic molecules
inferred← citeshttps://doi.org/10.1039/c8ce00261dopenalex-snapshot
A random forest model for predicting crystal packing of olanzapine solvates
inferred← citeshttps://doi.org/10.1080/10400435.2018.1493707openalex-snapshot
SIT LESS: A prototype home-based system for monitoring older adults sedentary behavior
inferred← citeshttps://doi.org/10.1080/1062936x.2010.528254openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbab112openalex-snapshot
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
inferred← citeshttps://doi.org/10.1093/bib/bbac288openalex-snapshot
deepGraphh: AI-driven web service for graph-based quantitative structure–activity relationship analysis
inferred← citeshttps://doi.org/10.1109/icmla61862.2024.00089openalex-snapshot
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
inferred← citeshttps://doi.org/10.1109/icmtma.2010.521openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/j.1747-0285.2008.00731.xopenalex-snapshot
Quantum Topological QSAR Models based on the MOLMAP Approach
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2012.01411.xopenalex-snapshot
Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical‐Histopathology Annotations
inferred← citeshttps://doi.org/10.1186/1758-2946-3-51openalex-snapshot
An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential
inferred← citeshttps://doi.org/10.1186/s13065-015-0109-0openalex-snapshot
Diverse models for anti-HIV activity of purine nucleoside analogs
inferred← citeshttps://doi.org/10.1186/s13321-015-0110-6openalex-snapshot
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter
inferred← citeshttps://doi.org/10.1186/s13321-020-00479-8openalex-snapshot
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
inferred← citeshttps://doi.org/10.12677/mos.2021.104102openalex-snapshot
Avocado Price Prediction Based on Random Forest Algorithm
inferred← citeshttps://doi.org/10.1371/journal.pone.0266088openalex-snapshot
A feature transferring workflow between data-poor compounds in various tasks
inferred← citeshttps://doi.org/10.1517/17425255.2010.495118openalex-snapshot
The computational prediction of genotoxicity
inferred← citeshttps://doi.org/10.21595/jve.2014.15013openalex-snapshot
Human activity recognition: classifier performance evaluation on multiple datasets
inferred← citeshttps://doi.org/10.22159/ijap.2022v14i4.44067openalex-snapshot
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inferred← citeshttps://doi.org/10.2477/jccj.h2216openalex-snapshot
Molecular Evolutionary Algorithm for Structure Design and Structure Generation
inferred← citeshttps://doi.org/10.26434/chemrxiv-2021-r4xnx-v2openalex-snapshot
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inferred← citeshttps://doi.org/10.2788/26123openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fsysb.2026.1734322openalex-snapshot
AI-based methods for the assessment of DNA damage and repair mechanisms
inferred← citeshttps://doi.org/10.3390/en12214187openalex-snapshot
Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection
inferred← citeshttps://doi.org/10.3390/en14248505openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/molecules17043818openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/molecules27031039openalex-snapshot
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inferred← citeshttps://doi.org/10.3762/bjoc.20.212openalex-snapshot
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inferred← citeshttps://doi.org/10.3797/scipharm.1006-03openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/978-1-61520-893-7.ch020openalex-snapshot
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inferred← citeshttps://doi.org/10.6084/m9.figshare.14053374openalex-snapshot
Additional file 1 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
inferred← citeshttps://doi.org/10.6084/m9.figshare.14053377openalex-snapshot
Additional file 2 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
inferred← citeshttps://doi.org/10.6084/m9.figshare.14053380openalex-snapshot
Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
inferred← citeshttps://doi.org/10.70675/2b63c353z8184z4bbbzbfbcz9ce124edd263openalex-snapshot
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