ArticleACS nano2017
Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.
Article in ACS nano, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 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
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicityNanoscale advances · 2026Article
- Machine Learning-Driven Prediction of Reactive Oxygen Species Dynamics for Assessing Nanomaterials' Cytotoxicity.Biomimetics (Basel, Switzerland) · 2025Review
- Advances in medical devices using nanomaterials and nanotechnology: Innovation and regulatory science.Bioactive materials · 2025Review
- An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.Nano letters · 2024Article
- Ligand Lipophilicity Determines Molecular Mechanisms of Nanoparticle Adsorption to Lipid Bilayers.ACS nano · 2024Article
- Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials.Nanomaterials (Basel, Switzerland) · 2024Review
- Development potential of nanoenabled agriculture projected using machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery.Pharmaceutics · 2023Review
- Integrating structure annotation and machine learning approaches to develop graphene toxicity models.Carbon · 2023Article
- Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management.Current medicinal chemistry · 2023Review
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
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- Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles.Science advances · 2021Article
- Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design.Frontiers in chemistry · 2021Article
- Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging.Frontiers in immunology · 2021Review
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
- Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles.Environmental health perspectives · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.
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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.