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Predicting Hepatotoxicity Using ToxCastin VitroBioactivity and Chemical Structure
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Bayesian Methods in Virtual Screening and Chemical Biology
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Activity profiles of 309 ToxCast™ chemicals evaluated across 292 biochemical targets
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Progress in computational toxicology
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Hepatotoxicity: The adverse effects of drugs and other chemicals on the liver
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The Hallmarks of Cancer
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Phytoremediation of the Herbicides Atrazine and Metolachlor by Transgenic Rice Plants Expressing Human CYP1A1, CYP2B6, and CYP2C19
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QSAR Modeling: Where Have You Been? Where Are You Going To?
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Update on EPA’s ToxCast Program: Providing High Throughput Decision Support Tools for Chemical Risk Management
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Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification
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Differential Phosphorylation of the Transcription Factor Oct1 During the Cell Cycle
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Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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The development and application of in silico models for drug induced liver injury
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In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox
inferred← citeshttps://doi.org/10.1039/d5em00562kopenalex-snapshot
Adverse outcome pathway-informed machine learning for predicting vascular toxicity of emerging organic pollutants
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Potential targets of endocrine-disrupting chemicals related to breast cancer identified by ToxCast and deep learning models
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Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways
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Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests
inferred← citeshttps://doi.org/10.1080/17460441.2018.1534824openalex-snapshot
Testing for drug-human serum albumin binding using fluorescent probes and other methods
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Advancing toxicity AI-based prediction with multilevel systems biology: a case study on genotoxicity
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Genetic toxicology at the crossroads—from qualitative hazard evaluation to quantitative risk assessment
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ProTox-II: a webserver for the prediction of toxicity of chemicals
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Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data
inferred← citeshttps://doi.org/10.1093/toxsci/kfx287openalex-snapshot
In Silico Prediction of Chemical-Induced Hepatocellular Hypertrophy Using Molecular Descriptors
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Identifying Attributes That Influence In Vitro -to- In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals
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The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency
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Assessment of liver injury potential of investigational medicines in drug development
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KNOWLEDGE GRAPH AIDS COMPREHENSIVE EXPLANATION OF DRUG TOXICITY
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Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data
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A New Evolutionary Machine Learning Approach for Identifying Pyrene Induced Hepatotoxicity and Renal Dysfunction in Rats
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Toxicity Prediction Using Pre-trained Autoencoder
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Automated combination of the results of elemental and structural analysis with the help of a learning knowledgebase to identify the contained analytes: A theoretical reflection
inferred← citeshttps://doi.org/10.1155/2021/2293871openalex-snapshot
Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury
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Comprehensive Phytochemical Characterisation and Integrated In Vitro, In Vivo and In Silico Evaluation of the Antidiabetic Potential of Urtica dioica Leaf Extract
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Review of machine learning and deep learning models for toxicity prediction
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Generic and Automated Data Evaluation in Analytical Measurement
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Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.1186/s13321-016-0162-2openalex-snapshot
Computational methods for prediction of in vitro effects of new chemical structures
inferred← citeshttps://doi.org/10.1186/s13321-020-00422-xopenalex-snapshot
KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development
inferred← citeshttps://doi.org/10.1186/s13321-025-01068-3openalex-snapshot
Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation
inferred← citeshttps://doi.org/10.1289/ehp.1509748openalex-snapshot
Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment
inferred← citeshttps://doi.org/10.1289/ehp6779openalex-snapshot
On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals
inferred← citeshttps://doi.org/10.1371/journal.pone.0196963openalex-snapshot
Uncertainty quantification in ToxCast high throughput screening
inferred← citeshttps://doi.org/10.1371/journal.pone.0217564openalex-snapshot
Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions
inferred← citeshttps://doi.org/10.1371/journal.pone.0253855openalex-snapshot
Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs
inferred← citeshttps://doi.org/10.14573/altex.1602071openalex-snapshot
Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data
inferred← citeshttps://doi.org/10.14573/altex.2002281openalex-snapshot
Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across
inferred← citeshttps://doi.org/10.14573/altex.2002281s1openalex-snapshot
Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl1
inferred← citeshttps://doi.org/10.14573/altex.2002281s2openalex-snapshot
Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl2
inferred← citeshttps://doi.org/10.17615/jtkk-d512openalex-snapshot
INTEROPERABILITY IN TOXICOLOGY: CONNECTING CHEMICAL, BIOLOGICAL, AND COMPLEX DISEASE DATA
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Qi Fu Yin ameliorates neuroinflammation through inhibiting RAGE and TLR4/NF-κB pathway in AD model rats
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Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents
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Development of a hepatotoxicity prediction model using in vitro assay data of key molecular events
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A New Insight Into Toxicity of Database Compounds from Ginger (Zingiber officinale) by Modelling Study
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Insilico toxicity prediction by using ProTox-II computational tools
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Endocrine-Mediated Mechanisms of Metabolic Disruption and New Approaches to Examine the Public Health Threat
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Molecular similarity-based predictions of the Tox21 screening outcome
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QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays
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Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon
inferred← citeshttps://doi.org/10.3389/ftox.2022.933197openalex-snapshot
Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study
inferred← citeshttps://doi.org/10.3390/ijerph13040372openalex-snapshot
A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals
inferred← citeshttps://doi.org/10.3390/molecules24081604openalex-snapshot
Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures
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Faculty Opinions recommendation of Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells.
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Chemically induced skin carcinogenesis: Updates in experimental models (Review)
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Synthesis and preliminary hepatotoxicity evaluation of new caffeine-8-(2-thio)-propanoic hydrazid-hydrazone derivatives
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Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human α4β2 Nicotinic Acetylcholine Receptor
inferred← citeshttps://doi.org/10.48550/arxiv.2101.01837openalex-snapshot
Classification of chemical compounds based on the correlation between \textit{in vitro} gene expression profiles
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A Screening Method to Identify Potential Endocrine Disruptors Using Chemical Toxicity Big Data and a Deep Learning Model with a Focus on Cleaning and Laundry Products
inferred← citeshttps://doi.org/10.6084/m9.figshare.12127560openalex-snapshot
Additional file 1 of KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605380openalex-snapshot
Additional file 1 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605383openalex-snapshot
Additional file 2 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605386openalex-snapshot
Additional file 3 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605389openalex-snapshot
Additional file 4 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605392openalex-snapshot
Additional file 5 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605395openalex-snapshot
Additional file 6 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605398openalex-snapshot
Additional file 7 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605401openalex-snapshot
Additional file 8 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.6084/m9.figshare.13605404openalex-snapshot
Additional file 9 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
inferred← citeshttps://doi.org/10.70675/a6af77b4z321dz4ab5z8b89zb2a887c44ddcopenalex-snapshot
Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.