Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data

Abstract

The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers.

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

“…Addressing these challenges necessitates enhanced dataset quality and size, and standardized data reporting. 84,86,87 This comprehensive understanding highlights the pivotal role of data quality, underscores the of standardized reporting practices, and emphasizes the collective effort required to enhance dataset quality and modeling techniques, ultimately unlocking the full potential of ML in driving innovation within the OPV and broader chemistry domains.…”

“…Both domains face challenges involving two molecular structures as input features, processing variables, sparse data, non-smooth response surfaces, and biases in reported data, notably the underreporting of negative results. 69,[82][83][84][85] Acknowledging these parallels emphasizes the broader challenges in predictive modeling within chemistry-driven domains. Addressing these challenges necessitates enhanced dataset quality and size, and standardized data reporting.…”

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

“…Both domains face challenges involving two molecular structures as input features, processing variables, sparse data, non-smooth response surfaces, and biases in reported data, notably the underreporting of negative results. 69,[82][83][84][85] Acknowledging these parallels emphasizes the broader challenges in predictive modeling within chemistry-driven domains. Addressing these challenges necessitates enhanced dataset quality and size, and standardized data reporting.…”

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes

“…The size of the corpus of chemical reaction data − obtained from public, proprietary, and licensed sources has progressively grown over the years, matched by an increasing demand for extracting more value from chemical experiments. , As a result, scientists are increasingly relying on computational tools for effective data navigation and analysis. For example, the Network of Organic Chemistry (NOC) , approach converts individual chemical reactions into a graph-like object, enabling powerful graph-based searches and facilitating the discovery of novel synthetic routes. , Additionally, advancements in predictive modeling has led to the development of Computer-Aided Synthesis Planning (CASP) tools, − which provide actionable insight in the form of synthetic plans to molecular targets.…”