ArticleScientific reports2019
Quantitative design rules for protein-resistant surface coatings using machine learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Protein adsorption at material interface: mechanistic design framework for engineering ceramic scaffolds for bone repair applications.Biomedical engineering online · 2026Review
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Exploring 2D Graphene-Based Nanomaterials for Biomedical Applications: A Theoretical Modeling Perspective.Small science · 2025Article
- Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Data-Driven Materials Research and Development for Functional Coatings.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Machine-Learning-Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes.ACS applied materials & interfaces · 2024Article
- Applied machine learning as a driver for polymeric biomaterials design.Nature communications · 2023Review
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Block Length-Dependent Protein Fouling on Poly(2-oxazoline)-Based Polymersomes: Influence on Macrophage Association and Circulation Behavior.Small (Weinheim an der Bergstrasse, Germany) · 2022Article
- Combinatorial Polyacrylamide Hydrogels for Preventing Biofouling on Implantable Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Biomaterials by design: Harnessing data for future development.Materials today. Bio · 2021Review
- Large scale model lipid membrane movement induced by a cation switch.Journal of colloid and interface science · 2021Article
- Affinity of small-molecule solutes to hydrophobic, hydrophilic, and chemically patterned interfaces in aqueous solution.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Antimicrobial Metal Nanomaterials: From Passive to Stimuli-Activated Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Review
- Methacrylate Coatings for Titanium Surfaces to Optimize Biocompatibility.Micromachines · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r
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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.