Iseult Lynch1, Antreas Afantitis2, Thomas Exner3, Martin Himly4, Vladimir Lobaskin5, Philip Doganis6, Dieter Maier7, Natasha Sanabria8, Anastasios G Papadiamantis1,2, Anna Rybinska-Fryca9, Maciej Gromelski9, Tomasz Puzyn9, Egon Willighagen10, Blair D Johnston11, Mary Gulumian8,12, Marianne Matzke13, Amaia Green Etxabe13, Nathan Bossa14, Angela Serra15, Irene Liampa6, Stacey Harper16, Kaido Tämm17, Alexander CØ Jensen18, Pekka Kohonen19, Luke Slater20, Andreas Tsoumanis2, Dario Greco15, David A Winkler21,22,23,24, Haralambos Sarimveis6, Georgia Melagraki2. 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. 2. Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus. 3. Edelweiss Connect GmbH, Hochbergerstrasse 60C, 4057 Basel, Switzerland. 4. Department Biosciences, Paris Lodron University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria. 5. School of Physics, University College Dublin, Belfield, Dublin 4, Ireland. 6. School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece. 7. Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany. 8. National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa. 9. QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. 10. Department of Bioinformatics-BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands. 11. Department Chemicals and Product Safety, Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, 10589 Berlin, Germany. 12. Haematology and Molecular Medicine, University of the Witwatersrand, 1 Jan Smuts Ave, Johannesburg 2000, South Africa. 13. UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford OX10 8BB, UK. 14. LEITAT Technological Center, Circular Economy Business Unit, C/de La Innovació 2, 08225 Terrassa, Barcelona, Spain. 15. Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland. 16. School of Chemical, Biological, and Environmental Engineering, Oregon State University, 116 Johnson Hall 105 SW 26th St., Corvallis, OR 97331, USA. 17. Institute of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia. 18. The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark. 19. Misvik Biology OY, Karjakatu 35 B, 20520 Turku, Finland. 20. Institute of Cancer and Genomics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. 21. Institute of Molecular Sciences, La Trobe University, Kingsbury Drive, Bundoora 3086, Australia. 22. Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia. 23. School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK. 24. CSIRO Data61, Pullenvale 4069, Australia.
Abstract
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (<n class="Chemical">span class="Disease">SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent <spn>an class="Chemical">polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.
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