ReviewNanomedicine (London, England)2026
Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.
Review in Nanomedicine (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Assembly of Multilevel Nanoconstructs with Negatively Charged Lipid Envelope and Features of Its Interaction with Protein Corona.Nanomaterials (Basel, Switzerland) · 2026Article
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The protein corona (PC) has widely been demonstrated to impact the pharmacokinetics, safety, and efficiency of nanoparticle (NP) systems for clinical applications such as drug or vaccine delivery. However, a comprehensive understanding of PC formation and its overall impact on biological behavior remains challenging due to the wide range of possible physicochemical and experimental parameters that may impact NP-PC formation and interactions, leaving important patterns difficult to identify. Machine learning (ML) algorithms have increasingly been used to analyze datasets and identify patterns that govern NP-PC interactions, showing great potential to enhance NP-PC experimental and pharmacological analysis. In this review, we conducted a systemic literature review for ML-based NP-PC analyses in PubMed and Web of Science between 1 January 2000 to 1 November 2025. We discuss key developments in the identified ML workflows for the prediction of NP-PC interactions, including PC composition and formation dynamics as well as pharmacological and toxicologically relevant endpoints like NP biodistribution and cellular uptake. We also highlight future perspectives in the field, such as improving dataset diversity, analytical protocol harmonization, and model transparency. We aim to guide future research toward more robust and informative ML approaches for optimizing NP design and predicting nano-bio interactions.
Indexed as
Identifiers
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.