StructuredHTE Data and Integrated Bayesian OptimizationAccelerate Scalable Amidation Process Development

Abstract We describe the evolution of our early hit-identification workflow for reagent-mediated amidation reactions in support of commercial route development. Over the past decade, our strategy progressed from broad, exhaustive high-throughput experimentation (HTE) with limited data maturity to a modern, enterprise-scale digital platform that embeds data science tools directly into the experimental workflow. By tailoring Katalyst D2D─a commercial electronic laboratory notebook (ELN) for HTE developed by ACD/Laboratories─we established a structured, machine-readable data architecture and enabled seamless integration with the EDBO+ Bayesian optimization engine. Retrospective analysis of historical amidation data sets showed that, despite screening more than 60 reagents, only a small subset consistently translated to commercial processes. This insight motivated a streamlined workflow in which chemists begin with a focused survey of six reaction conditions rooted in empirical performance and commercial precedent, followed by Bayesian optimization within the ELN to identify optimal reagent/base/solvent combinations. This study highlights how treating reaction data as a digital asset─FAIR, interoperable, and directly linked to optimization tools─accelerates the transition from reaction screening to robust process development.