ArticleACS nano2010
Quantitative nanostructure-activity relationship modeling.
Article in ACS nano, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 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
85 citing papers in PubMed.
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- Artificial Intelligence in Nanopharmaceutical Development: From Predictive Design to Clinical Translation.Pharmaceutics · 2026Review
- Exploring Lamiaceae diterpenoids as potential multitarget therapeutics for leishmaniasis and chagas disease.Molecular diversity · 2026Article
- AI-Driven Nano-QSAR Framework for Predicting Carbon Nanotube Cytotoxicity: Overcoming High Dimensionality and Data Imbalance.International journal of nanomedicine · 2026Article
- NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles.ACS nanoscience Au · 2025Article
- Data-Driven Prediction of Nanoparticle Biodistribution from Physicochemical Descriptors.ACS nano · 2025Article
- Dynamic QSAR modeling for predicting in vivo genotoxicity and inflammation induced by nanoparticles and advanced materials: a time-dose-property/response approach.Journal of nanobiotechnology · 2025Article
- Designing nanotheranostics with machine learning.Nature nanotechnology · 2024Review
- Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines.Discover nano · 2024Review
- Development of quantitative structure activity relationships (QSARs) for predicting the aggregation of TiOHeliyon · 2024Article
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- A review on the structural characterization of nanomaterials for nano-QSAR models.Beilstein journal of nanotechnology · 2024Review
- Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification.Beilstein journal of nanotechnology · 2024Article
- Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives asMolecules (Basel, Switzerland) · 2023Article
- New Opportunities and Old Challenges in the Clinical translation of Nanotheranostics.Nature reviews. Materials · 2023Article
- The protein corona from nanomedicine to environmental science.Nature reviews. Materials · 2023Review
- Integrating structure annotation and machine learning approaches to develop graphene toxicity models.Carbon · 2023Article
- In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES.International journal of molecular sciences · 2023Article
- Prediction of cytotoxicity of heavy metals adsorbed on nano-TiOBeilstein journal of nanotechnology · 2023Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Evaluation of biological effects, both desired and undesired, caused by manufactured nanoparticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as quantitative structure-activity relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach quantitative nanostructure-activity relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (Proc. Natl. Acad. Sci. 2008, 105, 7387-7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol. 2005, 23, 1418-1423). We have generated QNAR models using machine learning approaches such as support vector machine (SVM)-based classification and k nearest neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and having an R(2) of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials toward better and safer products.
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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.