Volume 94, Issue 6

For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.

A set of guiding principles for the development of a standard for FAIR management of spectroscopic data are outlined and discussed. The principles form the basis for future recommendations of IUPAC Project 2019-031-1-024 specifying a detailed data model and metadata schema for describing the contents of an “IUPAC FAIRData Collection” and the organization of digital objects within that collection. Foremost among the recommendations will be a specification for an “IUPAC FAIRData Finding Aid” that describes the collection in such a way as to optimize the findability , accessibility , interoperability , and reusability of its contents. Results of an analysis of data provided by an American Chemical Society Publications pilot study are discussed in relation to potential workflows that might be used in implementing the “IUPAC FAIRSpec” standard based on these principles.

The advent of materials informatics (MI) with emerging global trends in digitalization, artificial intelligence, and automation has led to promising opportunities for transforming traditional scientific research workflows. However, new MI efforts rely critically on the establishment, management, and accessibility of high-quality thermophysical and chemical data, either by mining existing databases, labelling historical data in archives, or generating sufficient data sets as prerequisites to the creation of predictive machine learning models. For ambitious MI-driven projects, amassing systematic data can be a time-intensive and prohibitively costly endeavor in spaces where data is uncurated or scarce. Here, we describe a MI initiative that started in the 3M Corporate Research Laboratories (CRL), highlighting how we strategically applied MI tools and data-driven methodologies for industrial materials research and product development workflows. Robust web applications and cloud infrastructure were developed to structure, standardize, and aggregate materials data for specific CRL projects. This integrated approach leverages the diverse skills and deep technical expertise of subject-matter experts at 3M to build the foundations for MI through systematic data management in materials research and, ultimately, to advance core technology platforms with innovative, customer-driven product solutions. Key elements that have contributed to the ongoing implementation of this highly versatile MI program, as well as challenges encountered, are presented as lessons learned for the broader MI and cheminformatics communities.

The RInChI is a canonical identifier for reactions which is widely used in reaction databases. It can be used to handle large collections of reactions and to link information from diverse data sources. How much information can it handle? Studies of the SAVI database, which contains more than a billion reactions, demonstrate that the RInChI is useful in analysing such a large collection of molecular data, and the reduced form of the Web-RInChIKey contains enough information to be an effective differentiator of reactions. Issues of NH tautomerism and stereochemistry are handled effectively. The RInChI illustrates that some of the properties of the algorithmically-generated SAVI database differ from SPRESI, which is a collection of experimental data. The RInChI has different properties to Reaction SMILES and both approaches provide useful and distinct information. We recommend that the RInChI be included in data models for reactions.

There are numerous formats and data models for describing reaction-related data. However, each offers only a limited coverage of the multitude of information that can be of interest to a broad user base in the context of chemical reactions. Structured Product Labeling (SPL) is a robust yet fairly light public XML document standard. It uses a highly generic but usefully refinable data schema, which is, like a language, highly expressive. We are therefore presenting an extension of SPL to chemical reactions (“Reaction SPL”). This extension is designed to support chemical manufacturing processes, which include as a minimum the chemical reaction and the procedures and conditions to run it. We provide an overview of the SPL reaction specification structures followed by some examples of documents with reaction data: predicted single-step reactions, a two-step synthesis, an enzymatic reaction, an example how to represent a reaction center, a patent, and a fully annotated reaction with by-products. Special attention is given to a mechanism for atom-atom mapping of reactions as well as to the possibility to integrate Reaction SPL with laboratory automation equipment, in particular automated synthesis devices.

In November 2020 the Royal Society of Chemistry Chemical Information and Computer Applications interest group (RSC CICAG) ran a five-day meeting entitled Open Chemical Science (https://www.rsc.org/events/detail/42090/open-chemical-science). This event had three intertwined themes, Open Data, Open Access publishing and a series of workshops highlighting a variety of Open-Source tools for chemistry. The online event proved to be enormously popular, with attendees from 45 different countries. The challenges involved in converting what was planned as a three-day physical event into a five day virtual event with three intertwined strands was recognised by the RSC with the award of the “2021 Inspirational Committee Award” (https://www.rsc.org/prizes-funding/prizes/2021-winners/rsc-chemical-information-and-computer-applications-group/). The workshops in particular proved to be enormously popular and spawned a year long series of further workshops.

The UDM (Unified Data Model) is an open, extendable and freely available data format for the exchange of experimental information about compound synthesis and testing. The UDM had been initially developed in a collaborative project between Elsevier and Roche, where chemical reactions data from a variety of disparate data sources existing at Roche was consolidated and integrated into the Roche in-house version of the Reaxys database. Elsevier adapted the UDM model to its needs and finally donated its pre-4.0 release to the Pistoia Alliance for further development together with the five project founders (Elsevier, Roche, BIOVIA, GSK and Novartis, joined later by BMS), who contributed with funding and expertise to the Pistoia Alliance UDM project between 2017 and 2020. The latest UDM version 6.0 has been made freely available for the community under the MIT license in January 2021. The past, present, and future of the UDM exchange format are discussed in this article and factors that contribute to the successful adoption of the UDM format.

The JCAMP-DX format is the most widely used vendor-independent data standard in spectroscopy. Based on the ASCII character set, the first standard was published for infrared spectroscopy in 1988 and over the years additional standards have been published covering most of the major spectroscopic techniques. This paper provides an overview of the JCAMP-DX standards, including a brief historical review. This is followed by a basic description of the format, example applications and a discussion of its continuing evolution.

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.

We present a simple, biochemistry data model (BioChemUDM) to represent compounds and assays for the purpose of capturing, reporting, and sharing data, both biological and chemical. We describe an approach to register a compound based solely on a stereo-enhanced sketch, thereby replacing the need for additional user-specified “flags” at the time of compound registration. We describe a convention for string-based labels that enables inter-organizational compound and assay data sharing. By co-adopting the BioChemUDM, we have successfully enabled same-day exchange and utilization of chemical and biological information with various stakeholders.