ArticleAdvanced materials (Deerfield Beach, Fla.)2024
GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.
Article in Advanced materials (Deerfield Beach, Fla.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Multiscale chiral zeros in biomolecules.Communications chemistry · 2025Article
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.Advanced materials (Deerfield Beach, Fla.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano-bio interface. This work introduces and validates a strategy to predict atom-scale interactions between water-soluble gold nanoclusters (AuNCs) and a set of blood proteins (albumin, apolipoprotein, immunoglobulin, and fibrinogen). Graph theory and neural networks are utilized to predict the strengths of interactions in AuNC-protein complexes on a coarse-grained level, which are then optimized in Monte Carlo-based structure search and refined to atomic-scale structures. The training data is based on extensive molecular dynamics (MD) simulations of AuNC-protein complexes, and the validating MD simulations show the robustness of the predictions. This strategy can be generalized to any complexes of inorganic nanostructures and biomolecules provided that one generates enough data about the interactions, and the bioactive parts of the nanostructure can be coarse-grained rationally.
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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.