FAIR Data Model for Chemical Substances: Development Challenges, Management Strategies, and Applications

Abstract

Data models for representation of chemicals are at the core of cheminformatics processing workflows. The standard triple, (structure, properties, and descriptors), traditionally formalizes a molecule and has been the dominant paradigm for several decades. While this approach is useful and widely adopted from academia, the regulatory bodies and industry have complex use cases and impose the concept of chemical substances applied for multicomponent, advanced, and nanomaterials. Chemical substance data model is an extension of the molecule representation and takes into account the practical aspects of chemical data management, emerging research challenges and discussions within academia, industry, and regulators. The substance paradigm must handle a composition of multiple components. Mandatory metadata is packed together with the experimental and theoretical data. Data model elucidation poses challenges regarding metadata, ontology utilization, and adoption of FAIR principles. We illustrate the adoption of these good practices by means of the Ambit/eNanoMapper data model, which is applied for chemical substances originating from ECHA REACH dossiers and for largest nanosafety database in Europe. The Ambit/eNanoMapper model allows development of tools for data curation, FAIRification of large collections of nanosafety data, ontology annotation, data conversion to standards such as JSON, RDF, and HDF5, and emerging linear notations for chemical substances.