# Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
## 2 Brief Description of the Databases
NIL is a web-based nanoparticle information library. It was developed by the National Institute for Occupational Safety and Health (NIOSH) [53]. NIL provides a tool for sharing and searching health and safety-associated properties of nanoparticles. It contains information on composition, method of production, particle size, surface area, morphology (include scanning, transmission, and other electron micrographic images), availability for research or commercial applications, and associated or relevant publications of nanoparticles. This database can be browsed and searched with a set of functions, including origin search, structure search, element search, and size search. Currently, it only has 88 unique nanomaterials. NKB provides an openly accessible and sustainable nano-informatics framework for the assessments of the risks of nanomaterials. It was developed by Biomax Informatics AG, a bioinformatics software company. This knowledge base contains physicochemical properties such as size, size distribution, shape, coating, dynamic light scattering, polydispersity index, zeta potential, electrophoretic mobility, energy band gap, and geometric surface area of 598 unique nanomaterials. Two types of toxicity data, no observed adverse effect level and toxicity, are included in NKB. The data can be exported in Excel or a tab delimited text file. This database has search, analysis (e.g., RNA-Seq analysis, corona analysis, and image analysis), ontology browsing, data export, and data upload functions. The data curated in the above-mentioned seven nanomaterial databases are not ideal for in silico modeling. For instance, some nanomaterial entities in the databases, such as structure, physicochemical properties, and biological endpoints, exist in text outputs without nanostructure annotations, which limited the application of supervised in silico modeling in predicting structure and toxicity correlation of nanomaterials. PubVINAS was developed to overcome the challenges in facilitating modeling of nanomaterials by providing the annotated nanostructures [54]. This database contains 12 material types (gold nanoparticles, silver nanoparticles, platinum nanoparticles, palladium nanoparticles, metal oxide nanoparticles, quantum dot nanoparticles, carbon nanotubes, peptide nanotubes, dendrimers, DNA origami, C60, and carbon nanoparticles), 725 unique nanomaterials, and 2142 nanodescriptors. The data in PubVINAS, including the physicochemical properties (e.g., size, shape, ligands number, logP, and zeta potential) and biological activities (e.g., cytotoxicity, cell uptake, cell viability, cell association, nonspecific/specific binding with AChE enzyme, protein adsorption, and oxidative stress) of nanomaterials, are extracted from thousands of scientific papers. These data were annotated and stored in the protein data bank (PDB) format files, which could be accessed from their web portal. Experimental protocols associated with the data are included in the database. Some machine learning models for predicting the properties (e.g., zeta potential, logP, and cellular uptake) of nanomaterials were also established based on those descriptors.