Probing the Chemical "Reactome" with High Throughput Experimentation Data

06 September 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Herein we report the development of a High Throughput Experimentation Analyzer (HiTEA), a robust and statistically rigorous framework which is applicable to any HTE dataset regardless of size, scope, or target reaction outcome. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions. HiTEA is validated on this dataset, showcasing the elucidation of hidden relationships between reaction components and outcomes as well as highlighting reaction space that necessitates further investigation.

Keywords

HTE
High Throughput Experimentation
Dataset Analysis
Machine Learning
Buchwald
Buchwald-Hartwig
Ullmann
Hydrogenation

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