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Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Applications of Support Vector Machines in Chemistry
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Handbook of Molecular Descriptors
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An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity
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MULTICASE 1. A Hierarchical Computer Automated Structure Evaluation Program
inferredcites →https://doi.org/10.1007/978-3-642-48736-1_5openalex-snapshot
Measuring Intra-Assay Agreement for the Ames Salmonella Assay
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An introduction to ROC analysis
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Towards the creation of an international toxicology information centre
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The Ames Salmonella/microsome mutagenicity assay
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Computational predictive programs (expert systems) in toxicology
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Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures
inferredcites →https://doi.org/10.1021/ci034032sopenalex-snapshot
Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods
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/ci049737oopenalex-snapshot
Classifying ‘Drug-likeness' with Kernel-Based Learning Methods
inferredcites →https://doi.org/10.1021/ci600205gopenalex-snapshot
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
inferredcites →https://doi.org/10.1021/jm040835aopenalex-snapshot
Derivation and Validation of Toxicophores for Mutagenicity Prediction
inferredcites →https://doi.org/10.1073/pnas.70.3.782openalex-snapshot
An Improved Bacterial Test System for the Detection and Classification of Mutagens and Carcinogens
inferredcites →https://doi.org/10.1073/pnas.72.3.979openalex-snapshot
Detection of carcinogens as mutagens: bacterial tester strains with R factor plasmids.
inferredcites →https://doi.org/10.1073/pnas.73.3.950openalex-snapshot
Detection of carcinogens as mutagens in the Salmonella/microsome test: assay of 300 chemicals: discussion.
inferredcites →https://doi.org/10.1080/15287398809531194openalex-snapshot
Computer‐assisted analysis of interlaboratory Ames test variability
inferredcites →https://doi.org/10.1093/mutage/11.5.471openalex-snapshot
Prediction of Salmonella mutagenicity
inferredcites →https://doi.org/10.1109/72.914517openalex-snapshot
An introduction to kernel-based learning algorithms
inferredcites →https://doi.org/10.1177/096032719101000405openalex-snapshot
Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Butenolides from Nectandra oppositifolia (Lauraceae) displayed anti-Trypanosoma cruzi activity via deregulation of mitochondria
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Large-scale chemical language models for accurate and interpretable prediction of reactivity and toxicity
inferred← citeshttps://doi.org/10.1016/j.rechem.2021.100199openalex-snapshot
In silico investigation of phytoconstituents of medicinal herb ‘Piper Longum’ against SARS-CoV-2 by molecular docking and molecular dynamics analysis
inferred← citeshttps://doi.org/10.1016/j.saa.2025.125960openalex-snapshot
A chemoinformatics study to prioritization of anticancer orally active lead compounds of pearl millet against adhesion G protein-coupled receptor
inferred← citeshttps://doi.org/10.1016/j.sajb.2024.04.045openalex-snapshot
Molecular docking and dynamics simulation reveal withanolides as potent antivirals against dengue virus
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2025.179133openalex-snapshot
Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites
inferred← citeshttps://doi.org/10.1016/j.taap.2013.09.015openalex-snapshot
A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities
inferred← citeshttps://doi.org/10.1016/j.tiv.2017.02.016openalex-snapshot
Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity
inferred← citeshttps://doi.org/10.1016/j.tox.2016.09.008openalex-snapshot
A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds
inferred← citeshttps://doi.org/10.1016/j.tox.2023.153676openalex-snapshot
Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems
inferred← citeshttps://doi.org/10.1016/j.tox.2023.153712openalex-snapshot
Assessment of the genotoxic and mutagenic effects induced by T-2 mycotoxin in HepG2 cells
inferred← citeshttps://doi.org/10.1016/j.toxlet.2020.07.003openalex-snapshot
Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints
inferred← citeshttps://doi.org/10.1016/j.vascn.2013.12.003openalex-snapshot
Progress in computational toxicology
inferred← citeshttps://doi.org/10.1016/j.watres.2017.11.033openalex-snapshot
Identification of mutagenic transformation products generated during oxidation of 3-methyl-4-nitrophenol solutions by orbitrap tandem mass spectrometry and quantitative structure–activity relationship analyses
inferred← citeshttps://doi.org/10.1016/j.yrtph.2012.01.007openalex-snapshot
In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: An industry survey
inferred← citeshttps://doi.org/10.1016/j.yrtph.2013.05.001openalex-snapshot
Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities
inferred← citeshttps://doi.org/10.1016/j.yrtph.2013.08.008openalex-snapshot
Validation of Toxtree and SciQSAR in silico predictive software using a publicly available benchmark mutagenicity database and their applicability for the qualification of impurities in pharmaceuticals
inferred← citeshttps://doi.org/10.1016/j.yrtph.2014.12.009openalex-snapshot
Evaluation of aromatic amines with different purities and different solvent vehicles in the Ames test
inferred← citeshttps://doi.org/10.1016/j.yrtph.2015.01.010openalex-snapshot
An evaluation of in-house and off-the-shelf in silico models: Implications on guidance for mutagenicity assessment
inferred← citeshttps://doi.org/10.1016/j.yrtph.2015.05.008openalex-snapshot
A practical application of two in silico systems for identification of potentially mutagenic impurities
inferred← citeshttps://doi.org/10.1016/j.yrtph.2015.12.006openalex-snapshot
Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained
inferred← citeshttps://doi.org/10.1016/j.yrtph.2016.01.008openalex-snapshot
It's difficult, but important, to make negative predictions
inferred← citeshttps://doi.org/10.1016/j.yrtph.2017.05.016openalex-snapshot
Utility of published DNA reactivity alerts
inferred← citeshttps://doi.org/10.1016/j.yrtph.2018.01.008openalex-snapshot
Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction
inferred← citeshttps://doi.org/10.1016/j.yrtph.2019.104488openalex-snapshot
Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses
inferred← citeshttps://doi.org/10.1016/j.yrtph.2023.105486openalex-snapshot
DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
inferred← citeshttps://doi.org/10.1016/j.yrtph.2024.105763openalex-snapshot
Ames mutagenicity of 15 aryl, benzyl, and aliphatic ring N-nitrosamines
inferred← citeshttps://doi.org/10.1016/j.yrtph.2026.106153openalex-snapshot
In silico prediction of Ames mutagenicity for organosilicon compounds: Exploring and enhancing chemical space boundaries
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.0c00264openalex-snapshot
The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.0c00322openalex-snapshot
Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.1c00187openalex-snapshot
A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00368openalex-snapshot
Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00372openalex-snapshot
Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00032openalex-snapshot
Equivariant Graph Neural Networks for Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.4c00466openalex-snapshot
AmesFormer: State-of-the-Art Mutagenicity Prediction with Graph Transformers
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.4c00523openalex-snapshot
Machine Learning Models Based on Enlarged Chemical Spaces for Screening Carcinogenic Chemicals
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.5c00198openalex-snapshot
Comparative Chemical Space Analysis of Pesticides and Substances with Genotoxicity Data
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.7b00083openalex-snapshot
Evaluation of Different Methods for Identification of Structural Alerts Using Chemical Ames Mutagenicity Data Set as a Benchmark
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00451openalex-snapshot
ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00200openalex-snapshot
Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00396openalex-snapshot
Interpretable Molecular Property Predictions Using Marginalized Graph Kernels
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00127openalex-snapshot
Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00512openalex-snapshot
BartSmiles: Generative Masked Language Models for Molecular Representations
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01246openalex-snapshot
Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02401openalex-snapshot
Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00143openalex-snapshot
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00144openalex-snapshot
Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01362openalex-snapshot
MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00357openalex-snapshot
ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End Point Toxicity Assessment
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00885openalex-snapshot
Reliability-Aware Deep Learning Framework for Chemical Genotoxicity Prediction with Uncertainty Quantification
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01425openalex-snapshot
Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00616openalex-snapshot
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00450openalex-snapshot
Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00633openalex-snapshot
LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c00514openalex-snapshot
Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b00865openalex-snapshot
Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c03551openalex-snapshot
Multimodal Multi-Granularity Fusion Model with Mamba Architecture for Ames Mutagenicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6c00049openalex-snapshot
In Silico ADMET: From Current Practices to Novel Profilers
inferred← citeshttps://doi.org/10.1021/acs.jnatprod.8b00890openalex-snapshot
Antitrypanosomal Activity of Acetogenins Isolated from the Seeds of Porcelia macrocarpa Is Associated with Alterations in Both Plasma Membrane Electric Potential and Mitochondrial Membrane Potential
inferred← citeshttps://doi.org/10.1021/acs.jpclett.1c03058openalex-snapshot
Extracting Predictive Representations from Hundreds of Millions of Molecules
inferred← citeshttps://doi.org/10.1021/acsfoodscitech.2c00217openalex-snapshot
In Silico Screening of Active Compounds in Garri for the Inhibition of Key Enzymes Linked to Diabetes Mellitus
inferred← citeshttps://doi.org/10.1021/acsomega.3c09744openalex-snapshot
Toward Quantum-Informed Atom Pairs
inferred← citeshttps://doi.org/10.1021/acsomega.7b02045openalex-snapshot
Distributed Representation of Chemical Fragments
inferred← citeshttps://doi.org/10.1021/acsomega.8b03173openalex-snapshot
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees
inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch014openalex-snapshot
Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models
inferred← citeshttps://doi.org/10.1021/ci100073wopenalex-snapshot
Large-Scale Learning of Structure−Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics
inferred← citeshttps://doi.org/10.1021/ci1000899openalex-snapshot
Benchmark Performance of MultiCASE Inc. Software in Ames Mutagenicity Set
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