ArticleChemical research in toxicology2013
Integrative chemical-biological read-across approach for chemical hazard classification.
Article in Chemical research in toxicology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed, 144 citations in OpenAlex.
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- Human vs. cells vs. machine: A comparative analysis of toxicological points of departure derived from quantitative read-across (qRAx), in vitro data, and In silico predictions.Regulatory toxicology and pharmacology : RTP · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Article
- Guidance on the use of read-across for chemical safety assessment in food and feed.EFSA journal. European Food Safety Authority · 2025Article
- Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025Review
- "intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticles.Computational and structural biotechnology journal · 2025Article
- Molecular Similarity in Predictive Toxicology with a Focus on the q-RASAR Technique.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes.Chemical research in toxicology · 2024Article
- A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example.Journal of cheminformatics · 2024Article
- Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.Environmental health perspectives · 2024Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- Assessing human carcinogenicity risk of agrochemicals without the rodent cancer bioassay.Frontiers in toxicology · 2024Article
- Investigation of chemical structure recognition by encoder-decoder models in learning progress.Journal of cheminformatics · 2023Article
- Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment.Chemical research in toxicology · 2023Article
- Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods.Regulatory toxicology and pharmacology : RTP · 2023Article
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
- Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis.Computational toxicology (Amsterdam, Netherlands) · 2022Article
- The BlueScreen HC assay to predict the genotoxic potential of fragrance materials.Mutagenesis · 2022Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here, we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays ("biological" similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from both chemical and biological analogues whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.