Simple User-Friendly Reaction Format

Abstract

Leveraging the increasing volume of chemical reaction data can enhance synthesis planning and improve suc- cess rates. However, machine learning applications for retrosynthesis planning and forward reaction prediction tools depend on having readily available, high-quality data in a structured format. While some public and licensed reaction databases are available, they frequently lack essential information about reaction condi- tions. To address this issue and promote the principles of findable, accessible, interoperable, and reusable (FAIR) data reporting and sharing, we introduce the Simple User-Friendly Reaction Format (SURF). SURF standardizes the documentation of reaction data through a structured tabular format, requiring only a basic understanding of spreadsheets. This format enables chemists to record the synthesis of molecules in a format that is both human- and machine-readable, making it easier to share and integrate directly into machine- learning pipelines. SURF files are designed to be interoperable, easily imported into relational databases, and convertible into other formats. This complements existing initiatives like the Open Reaction Database (ORD) and Unified Data Model (UDM). At Roche, SURF plays a crucial role in democratizing FAIR reaction data sharing and expediting the chemical synthesis process.

Fine-tuning large language models for chemical text mining

“…Apart from the five mentioned extraction tasks, it can be easily extended to tasks related to extracting information from scientific literature and transforming data into a simple user-friendly reaction format 22 that is both human- and machine-readable. This approach will significantly contribute to the development of extensive databases like the Open Reaction Database, 23,24 SciFinder 25 and Reaxys, 26 which gather comprehensive synthesis data through automated curation and expert verification, to make data more findable, accessible, interoperable, and reusable (FAIR).…”

HTE OS: A High-Throughput Experimentation Workflow Built from the Ground Up

“…In order to support the workflow but also store the data in a way that allows conversion into files amenable to machine learning, for example, the simple user-friendly reaction format (SURF), we developed a hierarchical schema to organize our data (Figure ). We clustered the data fields around the topics of Chemistry, Analytics, and Experiments.…”

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry

“…20,21 In addition, HTE generates large and consistent data sets covering both positive and, importantly, negative reaction outcomes for chemical transformations. 22,23 In line with the large amount of data generated, addressing the challenge of data sharing, adopting standardized formats and employing both human-and machine-readable reaction data formats 24 has become crucial. Repositories such as the Open Reaction Database (ORD) 25 and the Unified Data Model (UDM) 26 have already improved access to diverse data sets of reactions.…”