ArticleJournal of cheminformatics2017
SkinSensDB: a curated database for skin sensitization assays.
Article in Journal of cheminformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- R3ACT: An Oxygen-Controlled Biosensor-Based NAM to Support Animal-Free Chemical Safety Assessment.NAM journal · 2026Article
- Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- An Integrated Testing Strategy and Online Tool for Assessing Skin Sensitization of Agrochemical Formulations.Toxics · 2024Article
- Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models.Current research in toxicology · 2024Article
- Amino Chemoassay Profiling of Aromatic Aldehydes-Unraveling Drivers of Their Skin Sensitization Potency.Chemical research in toxicology · 2023Article
- Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides.Journal of pesticide science · 2022Article
- SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization.International journal of environmental research and public health · 2022Article
- Review
- Skin sensitization in silico protocol.Regulatory toxicology and pharmacology : RTP · 2020Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
- Adverse outcome pathways: a concise introduction for toxicologists.Archives of toxicology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.
Identifiers
What OpenQuestion holds
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.