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Representing and describing nanomaterials in predictive nanoinformatics

Nature Nanotechnology volume 17, pages 924–932 (2022)Cite this article

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Abstract

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique ‘representation’ of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure–activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

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Fig. 1: The spectrum of perspectives on nanomaterials descriptors.
Fig. 2: The concept of ENM representation.

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Acknowledgements

This work was funded via the European Union’s H2020 project NanoSolveIT (grant agreement number 814572), with additional support from the EU H2020 project NanoInformaTIX (grant agreement number 814426) and EU H2020 project PATROLS (grant agreement number 760813). V.L. and J.S. were supported by Science Foundation Ireland grant number 16/IA/4506.

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Author notes

  1. These authors contributed equally: Ewelina Wyrzykowska, Alicja Mikolajczyk.

Authors and Affiliations

  1. QSAR Lab Ltd, Gdańsk, Poland

    Ewelina Wyrzykowska, Alicja Mikolajczyk, Karolina Jagiello & Tomasz Puzyn

  2. Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland

    Alicja Mikolajczyk, Karolina Jagiello & Tomasz Puzyn

  3. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom

    Iseult Lynch & Eugenia Valsami-Jones

  4. Ideaconsult Ltd, Sofia, Bulgaria

    Nina Jeliazkova & Nikolay Kochev

  5. Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria

    Nikolay Kochev

  6. School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece

    Haralambos Sarimveis, Philip Doganis & Pantelis Karatzas

  7. NovaMechanics Ltd, Nicosia, Cyprus

    Antreas Afantitis

  8. Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece

    Georgia Melagraki

  9. FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

    Angela Serra & Dario Greco

  10. BioMediTech Institute, Tampere University, Tampere, Finland

    Angela Serra & Dario Greco

  11. Institute of Biotechnology, University of Helsinki, Helsinki, Finland

    Dario Greco

  12. School of Physics, University College Dublin, Belfield, Dublin, Ireland

    Julia Subbotina & Vladimir Lobaskin

  13. Instituto de Catálisis y Petroleoquimica, ICP CSIC, Madrid, Spain

    Miguel A. Bañares

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  1. Ewelina Wyrzykowska
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Wyrzykowska, E., Mikolajczyk, A., Lynch, I. et al. Representing and describing nanomaterials in predictive nanoinformatics. Nat. Nanotechnol. 17, 924–932 (2022). https://doi.org/10.1038/s41565-022-01173-6

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