Structured HTE Data and Integrated Bayesian Optimization Accelerate Scalable Amidation Process Development

Year: 2026

Type: article

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.

Source: ACS Organic & Inorganic Au

DOI: 10.1021/acsorginorgau.6c00051

Institutions: Bristol-Myers Squibb (United States), Bristol-Myers Squibb (United Kingdom)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,500

OpenAlex ID: W7203687622