346. Digitizing Chemical Discovery with a Bayesian Explorer for Interpreting Reactivity Data

S. Hessam M. Mehr, Dario Caramelli and Leroy Cronin; ChemRXiv; (2022); DOI: 10.26434/chemrxiv-2022-t5qqx (open access)

Interpretating the outcome of chemistry experiments consistently is slow and often 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 standardise the discovery process and accelerate the interpretation of high dimensional data aided by the expert chemist’s intuition. We demonstrate a digital Oracle that reasons about chemical reactivity using probability. By doing >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 new 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.