Probing the chemical 'reactome' with high-throughput experimentation data.

Autor: King-Smith E; Cavendish Laboratory, University of Cambridge, Cambridge, UK., Berritt S; Pfizer Research and Development, Groton, CT, USA., Bernier L; Pfizer Research and Development, La Jolla, CA, USA., Hou X; Pfizer Research and Development, Cambridge, MA, USA., Klug-McLeod JL; Pfizer Research and Development, Groton, CT, USA., Mustakis J; Pfizer Research and Development, Groton, CT, USA., Sach NW; Pfizer Research and Development, La Jolla, CA, USA., Tucker JW; Pfizer Research and Development, Groton, CT, USA., Yang Q; Pfizer Research and Development, Cambridge, MA, USA., Howard RM; Pfizer Research and Development, Groton, CT, USA. roger.howard@pfizer.com., Lee AA; Cavendish Laboratory, University of Cambridge, Cambridge, UK. aal44@cam.ac.uk.
Jazyk: angličtina
Zdroj: Nature chemistry [Nat Chem] 2024 Apr; Vol. 16 (4), pp. 633-643. Date of Electronic Publication: 2024 Jan 02.
DOI: 10.1038/s41557-023-01393-w
Abstrakt: 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. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.
(© 2024. The Author(s).)
Databáze: MEDLINE
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