ArticleNature communications2020
Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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
51 citing papers in PubMed.
- Effect of physical manipulation pulmonary rehabilitation on lung cancer patients after thoracoscopic lobectomy.Thoracic cancer · 2022Trial
- 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
- Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicityNanoscale advances · 2026Article
- Universal feature selection for simultaneous interpretability of multitask datasets.Journal of cheminformatics · 2026Article
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology.Beilstein journal of nanotechnology · 2026Article
- AI-Driven Nano-QSAR Framework for Predicting Carbon Nanotube Cytotoxicity: Overcoming High Dimensionality and Data Imbalance.International journal of nanomedicine · 2026Article
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- High-throughput design of bimetallic materialsChemical science · 2025Article
- Targeting and tracking mRNA lipid nanoparticles at the particle, transcript and protein level.Nature biomedical engineering · 2025Review
- Nanoplastics Toxicity Is a Subset of Nanotoxicology, Not a Separate Field.Environment & health (Washington, D.C.) · 2025Article
- Advances in medical devices using nanomaterials and nanotechnology: Innovation and regulatory science.Bioactive materials · 2025Review
- Violet Innovation Grade Index (VIGI): A New Survey-Based Metric for Evaluating Innovation in Analytical Methods.Analytical chemistry · 2025Review
- Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.Nature communications · 2025Article
- Advances in nanomaterial-mediated CRISPR/Cas delivery: from lipid nanoparticles to vesicle-derived systems.Frontiers in bioengineering and biotechnology · 2025Review
- Designing nanotheranostics with machine learning.Nature nanotechnology · 2024Review
- Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance.Computational and structural biotechnology journal · 2024Article
- An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.Nano letters · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Modern nanotechnology research has generated numerous experimental data for various nanomaterials. However, the few nanomaterial databases available are not suitable for modeling studies due to the way they are curated. Here, we report the construction of a large nanomaterial database containing annotated nanostructures suited for modeling research. The database, which is publicly available through http://www.pubvinas.com/, contains 705 unique nanomaterials covering 11 material types. Each nanomaterial has up to six physicochemical properties and/or bioactivities, resulting in more than ten endpoints in the database. All the nanostructures are annotated and transformed into protein data bank files, which are downloadable by researchers worldwide. Furthermore, the nanostructure annotation procedure generates 2142 nanodescriptors for all nanomaterials for machine learning purposes, which are also available through the portal. This database provides a public resource for data-driven nanoinformatics modeling research aimed at rational nanomaterial design and other areas of modern computational nanotechnology.
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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.