Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 37068251.
- Also identified by DOI 10.1073/pnas.2220045120 and PMC identifier 10151610.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Interpreting the outcome of chemistry experiments consistently is slow and frequently introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardize the discovery process and accelerate the interpretation of high-dimensional data aided by the expert chemist's intuition. We demonstrate a digital Oracle that interprets chemical reactivity using probability. By carrying out >500 reactions covering a large space and retaining both the positive and negative results, the Oracle was able to rediscover eight historically important reactions including the aldol condensation, Buchwald-Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig, and Wittig-Horner reactions. This paradigm for decoding reactivity validates and formalizes the expert chemist's experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data.