ArticleGreen chemistry : an international journal and green chemistry resource : GC2016
Alarms about structural alerts.
Article in Green chemistry : an international journal and green chemistry resource : GC, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- Andrographolide as a Promising Diterpenoid Scaffold Against the Neurotropic NematodePharmaceuticals (Basel, Switzerland) · 2026Article
- Genotoxicity risk assessment of a 7-hydroxymitragynine-enriched Kratom preparation: An integrated in silico and in vitro approach.Toxicology reports · 2026Article
- A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data.Journal of cheminformatics · 2026Article
- Optimizing skin sensitization prediction across activity cliffs: a comparative analysis of K-nearest neighbours vs random Forest.Frontiers in toxicology · 2026Article
- Article
- MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data.Journal of cheminformatics · 2025Article
- QSAR: Using the Past to Study the Present.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.Journal of medicinal chemistry · 2023Article
- Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity.ACS omega · 2023Article
- The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.Chemical research in toxicology · 2023Article
- A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds.Acta pharmacologica Sinica · 2023Article
- Structure optimization of new tumor-selective Passerini α-acyloxy carboxamides as Caspase-3/7 activators.Scientific reports · 2022Article
- MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS.Environmental science & technology · 2022Article
- PreS/MD: Predictor of Sensitization Hazard for Chemical Substances Released From Medical Devices.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- STopTox: AnEnvironmental health perspectives · 2022Article
- SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.Frontiers in chemistry · 2022Article
- In Silico Models for Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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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.