Open AccessBayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform
Author(s) -
Anirudh M. K. Nambiar,
Christopher Breen,
Travis Hart,
Timothy Kulesza,
Timothy F. Jamison,
Klavs F. Jensen
Publication year - 2022
Publication title -
acs central science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.893
H-Index - 76
eISSN - 2374-7951
pISSN - 2374-7943
DOI - 10.1021/acscentsci.2c00207
Subject(s) - reconfigurability , computer science , modular design , automation , context (archaeology) , modularity (biology) , flexibility (engineering) , robotics , bayesian optimization , process (computing) , artificial intelligence , robot , machine learning , programming language , engineering , mechanical engineering , telecommunications , paleontology , statistics , genetics , mathematics , biology
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.