ArticleEnvironmental health perspectives2020
Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles.
Article in Environmental health perspectives, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Engineered nanoparticles at the redox interface: Rewiring ROS signaling and stress responses in plants.Journal of integrative plant biology · 2026Review
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- Machine Learning-Driven Prediction of Reactive Oxygen Species Dynamics for Assessing Nanomaterials' Cytotoxicity.Biomimetics (Basel, Switzerland) · 2025Review
- Dynamic QSAR modeling for predicting in vivo genotoxicity and inflammation induced by nanoparticles and advanced materials: a time-dose-property/response approach.Journal of nanobiotechnology · 2025Article
- Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.Nature communications · 2025Article
- Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis.Particle and fibre toxicology · 2025Article
- AI-based nanotoxicity data extraction and prediction of nanotoxicity.Computational and structural biotechnology journal · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- "intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticles.Computational and structural biotechnology journal · 2025Article
- Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges.Environment & health (Washington, D.C.) · 2024Review
- Package delivered: folate receptor-mediated transporters in cancer therapy and diagnosis.Chemical science · 2024Review
- Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials.Nanomaterials (Basel, Switzerland) · 2024Review
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.Biomolecules · 2024Review
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors.Beilstein journal of nanotechnology · 2024Article
- A review on the structural characterization of nanomaterials for nano-QSAR models.Beilstein journal of nanotechnology · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.
objectivesBy employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.
methodsWe built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta (
resultsSeventeen out of 30 MeONPs induced excess
conclusions
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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.