Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

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.

Toward the Specificity of Bare Nanomaterial Surfaces for Protein Corona Formation

“…The surface of a solid is a complex and dynamic entity, that can actively interact with the surrounding medium, e.g., undergoing protonation/deprotonation, adsorbing/desorbing solvent molecules, ions, and biomolecules, or assisting the precipitation of compounds [ 150 ]. In this view, and taking inspiration from chemo-informatics, the future possibility to encode multi-component structures of nanomaterials in a machine-readable format was foreseen [ 151 ]. Information on size, shape, internal structure, and surface characteristics, possibly including ligands and surface defects on uncoated nanomaterials, should be considered.…”

Using Machine Learning to Parse Chemical Mixture Descriptions

“…A longer-term goal of this machine learning project is to keep pace with developments in the InChI community for representing more complicated substances, which step outside of the realm of the well-defined small molecules that make up most fine chemicals. These include polymers, 13 nanomaterials, 14 biomaterials, 15 enumerative sets, 16 reaction products, 17 and many other scenarios where some facts are known about the nature of a component but for various reasons it is not possible to represent it as an atomic connection table. Establishment of a suitable data structure for describing these substances is a subject of active research, for which several domain-specific standards are expected to emerge in the near future.…”

The nanotopography of SiO2particles impacts the selectivity and 3D fold of bound allergens

“…45,46 A study with gold nanoparticles with different surface charges showed that when NPs are coated with serum proteins, the resulting zeta potential was the same for all the particles. 47 Surface charge and porosity are both intrinsic morphological properties describing the nanotopography of NPs, 48 which has previously been shown to greatly influence the cell interactions of nanomaterials. 30 The surface area and pore size were determined via nitrogen sorption using Brunauer-Emmett-Teller analysis, which revealed an area of 1259.8 m 2 g −1 and average pore size of 3.9 nm for MSNPs, while high-resolution TEM revealed a pore size of 4.1 ± 0.3 nm (Fig.…”