ReviewBeilstein journal of nanotechnology2024
A review on the structural characterization of nanomaterials for nano-QSAR models.
Review in Beilstein journal of nanotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- Nanoinformatics: spanning scales, systems and solutions.Beilstein journal of nanotechnology · 2026Article
- SmartLipids in Drug Delivery: Redefining the Third Generation of Lipid Nanocarriers for Clinical and Industrial Translation.International journal of nanomedicine · 2026Review
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- Informatics-Based Design of Virtual Libraries of Polymer Nano-Composites.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.
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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.