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Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants

https://doi.org/10.1021/tx500145h
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What this work says

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

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Identification of nonmonotonic concentration-responses in Tox21 high-throughput screening estrogen receptor assays
inferred← citeshttps://doi.org/10.1016/j.taap.2023.116659openalex-snapshot
Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals
inferred← citeshttps://doi.org/10.1016/j.tips.2016.06.009openalex-snapshot
Transgenic Mouse Models Transferred into the Test Tube: New Perspectives for Developmental Toxicity Testing In Vitro ?
inferred← citeshttps://doi.org/10.1016/j.tiv.2012.08.015openalex-snapshot
Comparative analysis of eight cytotoxicity assays evaluated within the ACuteTox Project
inferred← citeshttps://doi.org/10.1016/j.tiv.2017.01.004openalex-snapshot
Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing
inferred← citeshttps://doi.org/10.1016/s1875-5364(26)61083-7openalex-snapshot
Network pharmacology and AI: illuminating the path to precision herbal medicine in Ganoderma spp.
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.0c00264openalex-snapshot
The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.2c00189openalex-snapshot
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.6c00143openalex-snapshot
Multitask Pretraining Framework for Improving Predictivity of Machine Learning Chemical Bioactivity Models for Low-Data Endpoints
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.8b00393openalex-snapshot
Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00227openalex-snapshot
An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00498openalex-snapshot
Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals
inferred← citeshttps://doi.org/10.1021/acs.est.0c07040openalex-snapshot
Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms
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.1c07413openalex-snapshot
Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications
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.est.3c02792openalex-snapshot
Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways
inferred← citeshttps://doi.org/10.1021/acs.est.5c09326openalex-snapshot
Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration–Response Data
inferred← citeshttps://doi.org/10.1021/acs.est.5c15424openalex-snapshot
Suspect List-Guided High-Throughput Screening (SLIG-HTS) Identifies Environmental Substances Associated with Advanced Embryonic Developmental Timing
inferred← citeshttps://doi.org/10.1021/acs.est.6c07797openalex-snapshot
A Safe and Just Earth by Design
inferred← citeshttps://doi.org/10.1021/acs.jchemed.5b00524openalex-snapshot
Big Data and Chemical Education
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00294openalex-snapshot
Data Quality in the Human and Environmental Health Sciences: Using Statistical Confidence Scoring to Improve QSAR/QSPR Modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01364openalex-snapshot
Chemical Space Exploration with Artificial “Mindless” Molecules
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00447openalex-snapshot
Optimal HTS Fingerprint Definitions by Using a Desirability Function and a Genetic Algorithm
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.0c01013openalex-snapshot
Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.7b00578openalex-snapshot
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00546openalex-snapshot
Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
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/acssuschemeng.7b03393openalex-snapshot
Better Metrics for “Sustainable by Design”: Toward an in Silico Green Toxicology for Green(er) Chemistry
inferred← citeshttps://doi.org/10.1021/ci500715eopenalex-snapshot
Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO
inferred← citeshttps://doi.org/10.1021/envhealth.4c00043openalex-snapshot
Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment
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/10.1038/s42004-024-01220-4openalex-snapshot
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
inferred← citeshttps://doi.org/10.1038/srep27897openalex-snapshot
Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy
inferred← citeshttps://doi.org/10.1039/9781782623656openalex-snapshot
Big Data in Predictive Toxicology
inferred← citeshttps://doi.org/10.1039/9781782623656-00001openalex-snapshot
Big Data in Predictive Toxicology: Challenges, Opportunities and Perspectives
inferred← citeshttps://doi.org/10.1039/9781782623656-00214openalex-snapshot
Toxicogenomics and Toxicoinformatics: Supporting Systems Biology in the Big Data Era
inferred← citeshttps://doi.org/10.1039/9781782623656-00264openalex-snapshot
Big Data Integration and Inference
inferred← citeshttps://doi.org/10.1039/9781782624059-00076openalex-snapshot
ToxicogenomicsIn vitro: Gene Expression Signatures for Differentiating Genotoxic Mechanisms
inferred← citeshttps://doi.org/10.1039/c8tx00051dopenalex-snapshot
Big-data and machine learning to revamp computational toxicology and its use in risk assessment
inferred← citeshttps://doi.org/10.1039/d1dd00046bopenalex-snapshot
Data management matters
inferred← citeshttps://doi.org/10.1080/10408398.2020.1756217openalex-snapshot
Ten years post-GAO assessment, FDA remains uninformed of potentially harmful GRAS substances in foods
inferred← citeshttps://doi.org/10.1080/10590501.2018.1537563openalex-snapshot
Deep learning for predicting toxicity of chemicals: a mini review
inferred← citeshttps://doi.org/10.1080/15287394.2021.1937418openalex-snapshot
Assessing how in vitro assay types predict in vivo toxicology data
inferred← citeshttps://doi.org/10.1080/17460441.2016.1216967openalex-snapshot
Getting the most out of PubChem for virtual screening
inferred← citeshttps://doi.org/10.1089/aivt.2017.0011openalex-snapshot
The AOP Concept: How Novel Technologies Can Support Development of Adverse Outcome Pathways
inferred← citeshttps://doi.org/10.1089/big.2014.1526openalex-snapshot
Big Data Literature Search
inferred← citeshttps://doi.org/10.1093/bioinformatics/btw640openalex-snapshot
CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data
inferred← citeshttps://doi.org/10.1093/nar/gkw1118openalex-snapshot
PubChem BioAssay: 2017 update
inferred← citeshttps://doi.org/10.1093/toxsci/kfad091openalex-snapshot
A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126
inferred← citeshttps://doi.org/10.1111/gcb.16689openalex-snapshot
Addressing chemical pollution in biodiversity research
inferred← citeshttps://doi.org/10.1128/msphere.00315-23openalex-snapshot
High-throughput functional trait testing for bacterial pathogens
inferred← citeshttps://doi.org/10.1146/annurev-pharmtox-010919-023324openalex-snapshot
Big Data and Artificial Intelligence Modeling for Drug Discovery
inferred← citeshttps://doi.org/10.1177/026119291504300506openalex-snapshot
Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of ‘Omics’ Data from Human Cell Cultures
inferred← citeshttps://doi.org/10.1177/0748233719893200openalex-snapshot
DNA damage and oogenesis anomalies in Pimelia latreillei (Coleoptera: Tenebrionidae) induced by heavy metals soil pollution
inferred← citeshttps://doi.org/10.1177/24723444241275987openalex-snapshot
Dermal Exposure to Alkylphenol Ethoxylates in As-Purchased Garments: Human Health Risk Assessment
inferred← citeshttps://doi.org/10.1177/2472555216685069openalex-snapshot
PubChem BioAssay: A Decade’s Development toward Open High-Throughput Screening Data Sharing
inferred← citeshttps://doi.org/10.1186/s12302-024-00919-4openalex-snapshot
Grouping strategies for assessing and managing persistent and mobile substances
inferred← citeshttps://doi.org/10.1186/s13040-025-00447-8openalex-snapshot
Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications
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/10.1186/s13321-017-0247-6openalex-snapshot
The CompTox Chemistry Dashboard: a community data resource for environmental chemistry
inferred← citeshttps://doi.org/10.1186/s41021-019-0139-2openalex-snapshot
Flow cytometric micronucleus assay and TGx-DDI transcriptomic biomarker analysis of ten genotoxic and non-genotoxic chemicals in human HepaRG™ cells
inferred← citeshttps://doi.org/10.12677/cc.2019.32002openalex-snapshot
Application Research and Development of Network Pharmacology
inferred← citeshttps://doi.org/10.1289/ehp.1509763openalex-snapshot
Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data
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/10.1371/journal.pcbi.1007025openalex-snapshot
Deep Learning-driven research for drug discovery: Tackling Malaria
inferred← citeshttps://doi.org/10.14573/altex.1601252openalex-snapshot
Supporting read-across using biological data
inferred← citeshttps://doi.org/10.14573/altex.2301061openalex-snapshot
A call for a Human Exposome Project
inferred← citeshttps://doi.org/10.1515/ci-2017-0304openalex-snapshot
The Future of Chemical Information Is Now
inferred← citeshttps://doi.org/10.21608/jesp.2020.131070openalex-snapshot
BEE VENOM DRUG POTENTIALITY ON THE MACROMOLECULES DAMAGE OF THE LARVAL GUT OF HERMETIA ILLUCENS (L.), (DIPTERA: STRATIOMYIDAE)
inferred← citeshttps://doi.org/10.24874/pes02.04.013openalex-snapshot
BIG DATA A PARADIGM SHIFT IN HRM RESEARCH FRONTIER REVIEW
inferred← citeshttps://doi.org/10.26434/chemrxiv.12781241openalex-snapshot
A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery
inferred← citeshttps://doi.org/10.26896/1028-6861-2019-85-2-73-78openalex-snapshot
Classification algorithms for quality control of industrially manufactured mineral fertilizers
inferred← citeshttps://doi.org/10.3389/fbioe.2019.00065openalex-snapshot
Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis
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/10.3389/fpls.2018.00235openalex-snapshot
Robust Microplate-Based Methods for Culturing and in Vivo Phenotypic Screening of Chlamydomonas reinhardtii
inferred← citeshttps://doi.org/10.3389/fpubh.2021.694834openalex-snapshot
A Modern Genotoxicity Testing Paradigm: Integration of the High-Throughput CometChip® and the TGx-DDI Transcriptomic Biomarker in Human HepaRG™ Cell Cultures
inferred← citeshttps://doi.org/10.3390/bdcc6040161openalex-snapshot
A Survey on Big Data in Pharmacology, Toxicology and Pharmaceutics
inferred← citeshttps://doi.org/10.3390/ijms19082358openalex-snapshot
Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
inferred← citeshttps://doi.org/10.3390/ijms21124380openalex-snapshot
Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement
inferred← citeshttps://doi.org/10.3390/ijms26073134openalex-snapshot
Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets
inferred← citeshttps://doi.org/10.3390/molecules26030766openalex-snapshot
In Silico Studies of Lamiaceae Diterpenes with Bioinsecticide Potential against Aphis gossypii and Drosophila melanogaster
inferred← citeshttps://doi.org/10.3390/ph14080790openalex-snapshot
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
inferred← citeshttps://doi.org/10.35248/2161-0983.20.9.221openalex-snapshot
Integration between Monitoring and Bio-monitoring Systems to Assessment the Impacts of Normal Levels of Environmental Pollutants
inferred← citeshttps://doi.org/10.70675/1172bdebzc150z4589z9e4bzd217b624a063openalex-snapshot
Bioinformatic and modelling approaches for a system-level understanding of oxidative stress toxicity
inferred← citeshttps://doi.org/10.70675/11ee966az2d84z47a4z99b1z77e79bf324ebopenalex-snapshot
Identification of biomarkers of effect following exposure to endocrine disrupting chemicals in Xenopus laevis and their relevance for human health
inferred← citeshttps://doi.org/10.70675/a6af77b4z321dz4ab5z8b89zb2a887c44ddcopenalex-snapshot
Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods
inferred← citeshttps://doi.org/10.70675/c9cacc57zdf38z489bzacf8z0d5ecf73c80copenalex-snapshot
Development of new virtual screening methods and novel unbiased benchmarking data sets
inferred← citeshttps://doi.org/10.7282/t3bz69ncopenalex-snapshot
From QSAR to QNAR, developing enhanced models for drug discovery
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