ArticleNanoImpact2020
Prediction of protein corona on nanomaterials by machine learning using novel descriptors.
Article in NanoImpact, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
- Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.Small methods · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- The protein corona at the nano-bio interface: the need for standardized methodology and opportunities for neurodegenerative disease intervention.RSC advances · 2026Review
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- The effects of ingested cellulose nanomaterials on DNA methylation in intestinal cells.Particle and fibre toxicology · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology.Beilstein journal of nanotechnology · 2026Article
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Emerging Approaches for the Discovery of Lipid-Based RNA Delivery Systems.Pharmaceutics · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Beyond Correlation: Establishing Causality in Protein Corona Formation for Nanomedicine.Molecular pharmaceutics · 2025Article
- The mucosal protein corona in local nanoparticle drug delivery.Cell biomaterials · 2025Article
- Article
- AI-based Prediction of Protein Corona Composition on DNA Nanostructures.bioRxiv : the preprint server for biology · 2024Article
- Article
- Unveiling protein corona composition: predicting with resampling embedding and machine learning.Regenerative biomaterials · 2024Article
- The Hybrid Nano-Biointerface between Proteins/Peptides and Two-Dimensional Nanomaterials.Molecules (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.
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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.