Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
Name
nambiar-et-al-2022-bayesian-optimization-of-computer-proposed-multistep-synthetic-routes-on-an-automated-robotic-flow (1).pdf
Description
Accepted version
Size
7.14 MB
Format
Adobe PDF
Checksum (MD5)
69240ec353147c100d3727e542323fa0
Author(s)
Nambiar, Anirudh MK
Breen, Christopher P
Hart, Travis
Kulesza, Timothy
Jamison, Timothy F
Jensen, Klavs F
Date Issued
June 10, 2022
Journal
ACS Central Science
Publisher
American Chemical Society
Citation
Anirudh M. K. Nambiar, Christopher P. Breen, Travis Hart, Timothy Kulesza, Timothy F. Jamison, and Klavs F. Jensen. ACS Central Science 2022 8 (6), 825-836.
Version
Final published version
Abstract
Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the synthesis of organic compounds, but the limited availability of context-specific data currently necessitates experimental development to fully specify process details. We plan and optimize a CASP-proposed and human-refined multistep synthesis route toward an exemplary small molecule, sonidegib, on a modular, robotic flow synthesis platform with integrated process analytical technology (PAT) for data-rich experimentation. Human insights address catalyst deactivation and improve yield by strategic choices of order of addition. Multi-objective Bayesian optimization identifies optimal values for categorical and continuous process variables in the multistep route involving 3 reactions (including heterogeneous hydrogenation) and 1 separation. The platform's modularity, robotic reconfigurability, and flexibility for convergent synthesis are shown to be essential for allowing variation of downstream residence time in multistep flow processes and controlling the order of addition to minimize undesired reactivity. Overall, the work demonstrates how automation, machine learning, and robotics enhance manual experimentation through assistance with idea generation, experimental design, execution, and optimization.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acscentsci.2c00207
