On the public citation graph

Predicting Chemical Ocular Toxicity Using a Combinatorial QSAR Approach

https://doi.org/10.1021/tx300393v
0 signed · 85 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470134955openalex-snapshot
A Comprehensive Guide to the Hazardous Properties of Chemical Substances
inferredcites →https://doi.org/10.1007/978-0-387-79054-1openalex-snapshot
Introductory Statistics with R
inferredcites →https://doi.org/10.1007/bf02853156openalex-snapshot
Effect of anti-infective ophthalmic solutions on corneal cells in vitro
inferredcites →https://doi.org/10.1007/s11095-008-9609-0openalex-snapshot
QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds
inferredcites →https://doi.org/10.1016/j.ijpharm.2008.03.015openalex-snapshot
Ocular pharmacokinetics of acyclovir amino acid ester prodrugs in the anterior chamber: Evaluation of their utility in treating ocular HSV infections
inferredcites →https://doi.org/10.1016/s0022-3565(25)08751-8openalex-snapshot
METHODS FOR THE STUDY OF IRRITATION AND TOXICITY OF SUBSTANCES APPLIED TOPICALLY TO THE SKIN AND MUCOUS MEMBRANES
inferredcites →https://doi.org/10.1016/s0039-6257(01)00211-9openalex-snapshot
The Draize Eye Test
inferredcites →https://doi.org/10.1016/s0887-2333(00)00065-5openalex-snapshot
Extent of initial corneal injury as a basis for alternative eye irritation tests
inferredcites →https://doi.org/10.1016/s0887-2333(97)00117-3openalex-snapshot
A Quantitative Structure–Activity Relationship (QSAR) for a Draize Eye Irritation Database
inferredcites →https://doi.org/10.1016/s0887-2333(98)00066-6openalex-snapshot
Interlaboratory validation of the in vitro eye irritation tests for cosmetic ingredients. (3) Evaluation of the haemolysis test
inferredcites →https://doi.org/10.1021/cc9800071openalex-snapshot
A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases
inferredcites →https://doi.org/10.1021/ci700443vopenalex-snapshot
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
inferredcites →https://doi.org/10.1021/ci800151mopenalex-snapshot
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
inferredcites →https://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferredcites →https://doi.org/10.1021/tx900189popenalex-snapshot
Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
inferredcites →https://doi.org/10.1021/tx990098zopenalex-snapshot
Quantitative Component Analysis of Mixtures for Risk Assessment: Application to Eye Irritation
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1023/a:1014853731428openalex-snapshot
Membrane-Interaction QSAR Analysis: Application to the Estimation of Eye Irritation by Organic Compounds
inferredcites →https://doi.org/10.1023/a:1025386326946openalex-snapshot
Rational selection of training and test sets for the development of validated QSAR models
inferredcites →https://doi.org/10.1080/15569520802618585openalex-snapshot
A current practice for predicting ocular toxicity of systemically delivered drugs
inferredcites →https://doi.org/10.1093/toxsci/59.2.335openalex-snapshot
Prediction of Eye Irritation from Organic Chemicals Using Membrane-Interaction QSAR Analysis
inferredcites →https://doi.org/10.1093/toxsci/kfi319openalex-snapshot
A Study of the Relationship between Cornea Permeability and Eye Irritation Using Membrane-Interaction QSAR Analysis
inferredcites →https://doi.org/10.1111/j.1442-9071.2008.01699.xopenalex-snapshot
Toxicology of the retina: advances in understanding the defence mechanisms and pathogenesis of drug‐ and light‐induced retinopathy
inferredcites →https://doi.org/10.1111/j.1468-1331.2005.01025.xopenalex-snapshot
Ocular complications of neurological therapy
inferredcites →https://doi.org/10.1111/j.1749-6632.1998.tb10641.xopenalex-snapshot
Draize Eye Scores and Eye Irritation Thresholds in Man Can Be Combined into One QSARa
inferredcites →https://doi.org/10.1136/oem.56.1.1openalex-snapshot
Effects of experimental exposure to triethylamine on vision and the eye.
inferredcites →https://doi.org/10.1515/9783110856156-034openalex-snapshot
The Potential Ocular Phototoxicity of Antidepressant Drugs
inferredcites →https://doi.org/10.18356/a6a925c3-enopenalex-snapshot
Globally Harmonized System of Classification and Labelling of Chemicals (GHS)
inferredcites →https://doi.org/10.1897/01-439openalex-snapshot
Quantitative structure-activity relationships for predicting skin and eye irritation
inferredcites →https://doi.org/10.2131/jts.34.611openalex-snapshot
Inter-laboratory study of short time exposure (STE) test for predicting eye irritation potential of chemicals and correspondence to globally harmonized system (GHS) classification
inferredcites →https://doi.org/10.2174/138161207782794257openalex-snapshot
Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening
inferredcites →https://doi.org/10.3892/mmr.2012.880openalex-snapshot
Toxic effect of using twenty percent alcohol on corneal epithelial tight junctions during LASEK

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9781119053248.ch14openalex-snapshot
Strategies and Assays for Minimizing Risk of Ocular Toxicity During Early Development of Systemically Administered Drugs
inferred← citeshttps://doi.org/10.1002/jat.4586openalex-snapshot
In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches
inferred← citeshttps://doi.org/10.1007/978-981-19-7834-0_22openalex-snapshot
Role of Artificial Intelligence in the Toxicity Prediction of Drugs
inferred← citeshttps://doi.org/10.1007/s00204-025-04282-yopenalex-snapshot
Advances in the application of computational toxicology in cosmetic safety assessment
inferred← citeshttps://doi.org/10.1007/s10822-014-9748-9openalex-snapshot
Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers
inferred← citeshttps://doi.org/10.1007/s11095-015-1687-1openalex-snapshot
Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling
inferred← citeshttps://doi.org/10.1016/b978-0-12-820045-2.00008-8openalex-snapshot
Evaluating safety and toxicity
inferred← citeshttps://doi.org/10.1016/j.ailsci.2021.100028openalex-snapshot
Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2014.02.014openalex-snapshot
Quantification of contributions of molecular fragments for eye irritation of organic chemicals using QSAR study
inferred← citeshttps://doi.org/10.1016/j.drudis.2017.08.010openalex-snapshot
From machine learning to deep learning: progress in machine intelligence for rational drug discovery
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2019.04.019openalex-snapshot
Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2020.110216openalex-snapshot
Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling
inferred← citeshttps://doi.org/10.1016/j.envint.2024.108995openalex-snapshot
Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish
inferred← citeshttps://doi.org/10.1016/j.fct.2022.113420openalex-snapshot
Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies
inferred← citeshttps://doi.org/10.1016/j.taap.2025.117457openalex-snapshot
Machine learning-driven prediction of eye irritation toxicity: Integration of in silico and in vitro study
inferred← citeshttps://doi.org/10.1016/j.tiv.2013.11.002openalex-snapshot
Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli
inferred← citeshttps://doi.org/10.1016/j.toxlet.2025.03.008openalex-snapshot
Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin
inferred← citeshttps://doi.org/10.1016/j.vascn.2013.12.003openalex-snapshot
Progress in computational toxicology
inferred← citeshttps://doi.org/10.1016/j.yrtph.2016.03.014openalex-snapshot
A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes
inferred← citeshttps://doi.org/10.1021/acs.est.1c02656openalex-snapshot
Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
inferred← citeshttps://doi.org/10.1021/acs.est.2c01040openalex-snapshot
Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data
inferred← citeshttps://doi.org/10.1021/acs.est.3c00648openalex-snapshot
Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure
inferred← citeshttps://doi.org/10.1021/acs.est.3c00653openalex-snapshot
Advancing Computational Toxicology by Interpretable Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jafc.0c04624openalex-snapshot
Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00601openalex-snapshot
Reason Vectors: Abstract Representation of Chemistry–Biology Interaction Outcomes, for Reasoning and Prediction
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b01297openalex-snapshot
Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets
inferred← citeshttps://doi.org/10.1021/acsnano.7b07093openalex-snapshot
Predicting Nano–Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling
inferred← citeshttps://doi.org/10.1021/acsomega.7b00274openalex-snapshot
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
inferred← citeshttps://doi.org/10.1021/ci400480sopenalex-snapshot
Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation
inferred← citeshttps://doi.org/10.1038/nnano.2016.5openalex-snapshot
Seeing the trees for the forest
inferred← citeshttps://doi.org/10.1038/s41374-020-00477-2openalex-snapshot
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches
inferred← citeshttps://doi.org/10.1080/15287394.2021.1956661openalex-snapshot
In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method
inferred← citeshttps://doi.org/10.1080/15569527.2025.2607700openalex-snapshot
Alternative models for evaluation of ocular toxicological effects of environmental chemicals and pharmaceuticals: a review
inferred← citeshttps://doi.org/10.1093/toxsci/kfaa005openalex-snapshot
Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data
inferred← citeshttps://doi.org/10.1111/risa.12109openalex-snapshot
A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes
inferred← citeshttps://doi.org/10.1146/annurev-pharmtox-010919-023324openalex-snapshot
Big Data and Artificial Intelligence Modeling for Drug Discovery
inferred← citeshttps://doi.org/10.1155/2019/6847685openalex-snapshot
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
inferred← citeshttps://doi.org/10.1184/r1/6721226openalex-snapshot
Quantitative Risk Assessment of the Pulmonary Toxicity of Nano- particles by Machine-Learning-Enabled Meta-Analysis
inferred← citeshttps://doi.org/10.1186/1476-069x-13-57openalex-snapshot
The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees
inferred← citeshttps://doi.org/10.1186/s13321-016-0117-7openalex-snapshot
ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
inferred← citeshttps://doi.org/10.1289/ehp3614openalex-snapshot
Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across
inferred← citeshttps://doi.org/10.14573/altex.1601252openalex-snapshot
Supporting read-across using biological data
inferred← citeshttps://doi.org/10.14573/altex.2403211openalex-snapshot
Data-driven derivation of an adverse outcome pathway linking vascular endothelial growth factor receptor (VEGFR), endocrine disruption, and atherosclerosis
inferred← citeshttps://doi.org/10.14573/altex.2403211s1openalex-snapshot
Data-driven derivation of an adverse outcome pathway linking vascular endothelial growth factor receptor (VEGFR), endocrine disruption, and atherosclerosis_suppl1
inferred← citeshttps://doi.org/10.1787/84b83321-enopenalex-snapshot
Second Edition - Guidance Document on Integrated Approaches to Testing and Assessment (IATA) for Serious Eye Damage and Eye Irritation
inferred← citeshttps://doi.org/10.2788/11111openalex-snapshot
Alternative methods for regulatory toxicology – a state-of-the-art review
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/10.3390/diagnostics16101416openalex-snapshot
Ocular Lymphatics in Health and Disease
inferred← citeshttps://doi.org/10.3390/ijtm1030016openalex-snapshot
Artificial Intelligence in Translational Medicine
inferred← citeshttps://doi.org/10.3390/molecules24183383openalex-snapshot
Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/mps3040074openalex-snapshot
Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing
inferred← citeshttps://doi.org/10.3390/pharmaceutics17020156openalex-snapshot
Pesticides’ Cornea Permeability—How Serious Is This Problem?
inferred← citeshttps://doi.org/10.7282/t3bz69ncopenalex-snapshot
From QSAR to QNAR, developing enhanced models for drug discovery
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 and goes through ORCID: icsacinstitute.org/submit.