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SUMMARY:Reaction Battleship:  Searching Chemical Space for the Optimal Reac
 tion with Bayesian Methods
LOCATION:Chemistry A101
TZID:America/Denver
DTSTART:20211111T160000
UID:2026-04-29-03-30-40@natsci.colostate.edu
DTSTAMP:20260429T033040
Description:Literature Seminar:\n\nThe discovery of new chemical reactions 
 and the optimization of conditions\, reagents\, solvents\, and catalysts a
 re long-standing challenges for synthetic chemistry. From a different\, mo
 re playful perspective\, reaction optimization can be perceived as a game 
 where the chemist tries to find the best combination of reagents and react
 ion conditions to achieve the highest yield. Since several variables influ
 ence the observed yield\, many thousands of possible combinations can be t
 ried.  To find the optimal combination in such a high-dimensional chemica
 l space\, chemists attempt to gain knowledge from mechanistic studies\, al
 though these are generally performed one reaction at a time. Alternatively
 \, High Throughput Experimentation (HTE) can be envisaged to exhaustively 
 sample all combinations of reactions. However\, in practice\, this is inef
 ficient\, costly\, and unsuited to large chemical spaces.\n\nTo carry out 
 targeted sampling of reaction parameters\, Adams and Doyle have developed 
 a Bayesian Optimization framework. This algorithmic approach iteratively s
 uggests new reaction combinations to try from within a predefined chemical
  space. The Bayesian optimizer uses knowledge of high and low yielding rea
 ctions to estimate which reaction combinations the synthetic chemist shoul
 d try next. These predictions are updated during the optimization campaign
 . Several synthetic organic reactions were subjected to Bayesian Optimizat
 ion to identify high-yielding reaction conditions\, catalysts\, and other 
 parameters. This approach outperforms chemists in optimizing a direct hete
 rocycle arylation reaction in less time than human efforts. Additionally\,
  the optimizer has demonstrated its general applicability by identifying r
 elatively high-yielding reactions for several industrially-relevant Pd-cat
 alyzed cross-coupling reactions.\n\nBayesian Optimization is introduced as
  a tool that avoids full-factorial sampling of a reaction space by leverag
 ing knowledge of prior results. These predictions are iteratively improved
  as more knowledge is collected. This approach enables chemists to arrive 
 more efficiently at optimal reaction combinations by reducing the number o
 f experiments\, time\, and cost involved.\n\nShields\, B. J.\; Stevens\, J
 .\; Li\, J.\; Parasram\, M.\; Damani\, F.\; Alvarado\, J. I. M.\; Janey\, 
 J. M.\; Adams\, R. P.\; Doyle\, A. G. Bayesian Reaction Optimization as a 
 Tool for Chemical Synthesis. Nature 2021\, 590 (7844)\, 89–96. https://d
 oi.org/10.1038/s41586-021-03213-y.\n\n&nbsp\; 4:00 pm
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