Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles
Abstract
Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structure and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure - Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle's descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.
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- Authors
- Fourches, D., Pu, D., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Combinatorial Chemistry & High Throughput Screening
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2011-03-01
- Volume
- 14
- Issue
- 3
- Pages
- 217-225
- ISSN
- 1386-2073 (ISSN-L)
- OpenAlex
- W2111455508
- Semantic Scholar
- c562f52957311fa23335be4063ab4dc26cb02449
- Cited by
- 92
- References
- 53
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 8 · 2019: 2 · 2018: 5 · 2017: 15 · 2016: 7 · 2015: 13 · 2014: 11 · 2013: 8 · 2012: 5
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Combinatorial Chemistry & High Throughput Screening (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM066940)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Nanoparticles: synthesis and applications
- Keywords
- engineered nanoparticles, QSAR, cheminformatics, carbon nanotubes, model validation
- MeSH terms
- Biological Assay, Computer Simulation, Humans, Combinatorial Chemistry Techniques, Quantitative Structure-Activity Relationship, Nanotechnology, Cell Line, Tumor, Nanostructures
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