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Integrative Chemical–Biological Read-Across Approach for Chemical Hazard Classification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Chemoinformatics and chemical genomics: potential utility of in silico methods
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Best Practices for QSAR Model Development, Validation, and Exploitation
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The Japanese toxicogenomics project: Application of toxicogenomics
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Approaches to Measure Chemical Similarity – a Review
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Instance-Based Learning Algorithms
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Integrating (Q)SAR models, expert systems and read-across approaches for the prediction of developmental toxicity
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A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments
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Application of computational toxicological approaches in human health risk assessment. I. A tiered surrogate approach
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The Ames Salmonella/microsome mutagenicity assay
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Anticonvulsant activity of hydrazones, Schiff and Mannich bases of isatin derivatives
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On Outliers and Activity CliffsWhy QSAR Often Disappoints
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Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
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Consensus QSAR Models: Do the Benefits Outweigh the Complexity?
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Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
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Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
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Toward a Comprehensive Molecular Design Framework for Reduced Hazard
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Quantitative Read-Across for Predicting the Acute Fish Toxicity of Organic Compounds
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Estimating Toxicity-Related Biological Pathway Altering Doses for High-Throughput Chemical Risk Assessment
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Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches
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Quantitative and Mechanistic Read Across for Predicting the Skin Sensitization Potential of Alkenes Acting via Michael Addition
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Promises and Pitfalls of Quantitative Structure−Activity Relationship Approaches for Predicting Metabolism and Toxicity
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Influence of Chloramphenicol on Rat Hepatic Microsomal Components and Biomarkers of Oxidative Stress: Protective Role of Antioxidants
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The liver pharmacological and xenobiotic gene response repertoire
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ToxPi GUI: an interactive visualization tool for transparent integration of data from diverse sources of evidence
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A Gene Expression Biomarker Provides Early Prediction and Mechanistic Assessment of Hepatic Tumor Induction by Nongenotoxic Chemicals
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Quantitative High-Throughput Screening for Chemical Toxicity in a Population-Based In Vitro Model
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Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data
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A Comprehensive Statistical Analysis of Predicting In Vivo Hazard Using High-Throughput In Vitro Screening
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Molecular Model of Anticonvulsant Drug Binding to the Voltage-Gated Sodium Channel Inner Pore
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Activating Effect of Benzbromarone, a Uricosuric Drug, on Peroxisome Proliferator-Activated Receptors
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Peroxisome proliferator-activated receptors (PPARs) and ovarian function – implications for regulating steroidogenesis, differentiation, and tissue remodeling
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The Generalization Paradox of Ensembles
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Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
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Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity
inferredcites →https://doi.org/10.1289/ehp.10573openalex-snapshot
Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity
inferredcites →https://doi.org/10.18356/244432ff-enopenalex-snapshot
Globally Harmonized System of Classification and Labelling of Chemicals (GHS)
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A Benefit-Risk Assessment of Benzbromarone in the Treatment of Gout
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The Challenges Involved in Modeling Toxicity Data In Silico: A Review
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Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints
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Read-Across and RASAR Tools from the DTC Laboratory
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Quantitative Nanostructure–Activity Relationships: Methods, Case Studies, and Perspectives
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Advancing ADMET Prediction via Biological Readacross-Inspired Multitask Learning
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Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration
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Annonaceae Terpenoids as Potential Leishmanicidal Agents
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Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study
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Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents
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Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods
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Exploring current read-across applications and needs among selected U.S. Federal Agencies
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Supporting read-across using biological data
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Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data
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Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard
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Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard_suppl
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Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across
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Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl1
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Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl2
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Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs
inferred← citeshttps://doi.org/10.14573/altex.2002291sopenalex-snapshot
Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs_suppl
inferred← citeshttps://doi.org/10.14573/altex.2006051openalex-snapshot
Limitations and uncertainties of acute fish toxicity assessments can be reduced using alternative methods
inferred← citeshttps://doi.org/10.14573/altex.2006262openalex-snapshot
Grouping of UVCB substances with new approach methodologies (NAMs) data
inferred← citeshttps://doi.org/10.14573/altex.2006262sopenalex-snapshot
Grouping of UVCB substances with new approach methodologies (NAMs) data_suppl
inferred← citeshttps://doi.org/10.15439/2025f1171openalex-snapshot
Multitask Learning for Six-Pack Toxicity Prediction
inferred← citeshttps://doi.org/10.1787/9789264274792-enopenalex-snapshot
Fundamental And Guiding Principles For (Q)SAR Analysis Of Chemical Carcinogens with Mechanistic Considerations
inferred← citeshttps://doi.org/10.2174/1568026614666140506121116openalex-snapshot
Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays
inferred← citeshttps://doi.org/10.2174/1568026620666200716114546openalex-snapshot
Ligand and Structure-based Virtual Screening of Lamiaceae Diterpenes with Potential Activity against a Novel Coronavirus (2019-nCoV)
inferred← citeshttps://doi.org/10.2788/11111openalex-snapshot
Alternative methods for regulatory toxicology – a state-of-the-art review
inferred← citeshttps://doi.org/10.2903/j.efsa.2025.9586openalex-snapshot
Guidance on the use of read‐across for chemical safety assessment in food and feed
inferred← citeshttps://doi.org/10.2903/sp.efsa.2019.en-1598openalex-snapshot
Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across
inferred← citeshttps://doi.org/10.3389/fenvs.2016.00012openalex-snapshot
Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data
inferred← citeshttps://doi.org/10.3389/fphar.2016.00321openalex-snapshot
Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties
inferred← citeshttps://doi.org/10.3389/ftox.2024.1394361openalex-snapshot
Assessing human carcinogenicity risk of agrochemicals without the rodent cancer bioassay
inferred← citeshttps://doi.org/10.3390/cimb44010028openalex-snapshot
Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity
inferred← citeshttps://doi.org/10.3390/nano10040708openalex-snapshot
Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment
inferred← citeshttps://doi.org/10.3390/nano11030639openalex-snapshot
Reliable Surface Analysis Data of Nanomaterials in Support of Risk Assessment Based on Minimum Information Requirements
inferred← citeshttps://doi.org/10.3390/toxics14020152openalex-snapshot
Read-Across Structural Analysis of PFAS Acute Oral Toxicity in Rats Powered by the Isalos Analytics Platform’s Automated Machine Learning
inferred← citeshttps://doi.org/10.47470/0016-9900-2025-104-5-670-673openalex-snapshot
Comparative analysis of methods for predicting the toxicity of chemicals (literature review)
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581898openalex-snapshot
Additional file 1 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581901openalex-snapshot
Additional file 2 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581904openalex-snapshot
Additional file 3 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581907openalex-snapshot
Additional file 4 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581910openalex-snapshot
Additional file 5 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581913openalex-snapshot
Additional file 6 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26581916openalex-snapshot
Additional file 7 of Investigation of chemical structure recognition by encoder–decoder models in learning progress
inferred← citeshttps://doi.org/10.6084/m9.figshare.26757160openalex-snapshot
Additional file 1 of A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example
inferred← citeshttps://doi.org/10.64898/2026.02.26.708255openalex-snapshot
ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling
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
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