ArticleChemical research in toxicology2012
Predicting chemical ocular toxicity using a combinatorial QSAR approach.
Article in Chemical research in toxicology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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Who cites it
27 citing papers in PubMed, 56 citations in OpenAlex.
- Ocular Lymphatics in Health and Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Advances in the application of computational toxicology in cosmetic safety assessment.Archives of toxicology · 2026Review
- Pesticides' Cornea Permeability-How Serious Is This Problem?Pharmaceutics · 2025Article
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.Environmental science & technology · 2022Article
- Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.Artificial intelligence in the life sciences · 2021Article
- Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.Environmental science & technology · 2021Article
- Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing.Methods and protocols · 2020Review
- Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Analysis of model PMEcotoxicology and environmental safety · 2020Article
- Big Data and Artificial Intelligence Modeling for Drug Discovery.Annual review of pharmacology and toxicology · 2020Review
- Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network.Molecules (Basel, Switzerland) · 2019Article
- Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data.Ecotoxicology and environmental safety · 2019Article
- Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.Environmental health perspectives · 2019Article
- Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.Molecular pharmaceutics · 2019Article
- Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.BioMed research international · 2019Article
- Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.ACS nano · 2017Article
- Article
- Nanotoxicology: Seeing the trees for the forest.Nature nanotechnology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Regulatory agencies require testing of chemicals and products to protect workers and consumers from potential eye injury hazards. Animal screening, such as the rabbit Draize test, for potential environmental toxicants is time-consuming and costly. Therefore, virtual screening using computational models to tag potential ocular toxicants is attractive to toxicologists and policy makers. We have developed quantitative structure-activity relationship (QSAR) models for a set of small molecules with animal ocular toxicity data compiled by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods. The data set was initially curated by removing duplicates, mixtures, and inorganics. The remaining 75 compounds were used to develop QSAR models. We applied both k nearest neighbor and random forest statistical approaches in combination with Dragon and Molecular Operating Environment descriptors. Developed models were validated on an external set of 34 compounds collected from additional sources. The external correct classification rates (CCR) of all individual models were between 72 and 87%. Furthermore, the consensus model, based on the prediction average of individual models, showed additional improvement (CCR = 0.93). The validated models could be used to screen external chemical libraries and prioritize chemicals for in vivo screening as potential ocular toxicants.
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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.