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.

中文翻译：

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自动化机器人流程平台上计算机提出的多步合成路线的贝叶斯优化

计算机辅助合成规划（CASP）工具可以为有机化合物的合成提出逆向合成途径和正向反应条件，但目前上下文特定数据的可用性有限，需要进行实验开发以充分指定过程细节。我们在模块化机器人流程合成平台上规划和优化了 CASP 提出的、经过人工改进的多步合成路线，以实现示例性小分子 sonidegib，该平台具有集成过程分析技术 (PAT)，可进行数据丰富的实验。人类洞察力通过添加顺序的战略选择来解决催化剂失活问题并提高产量。多目标贝叶斯优化可确定涉及 3 个反应（包括多相氢化）和 1 个分离的多步骤路线中分类和连续过程变量的最佳值。该平台的模块化、机器人可重构性和聚合合成的灵活性对于允许多步流程中下游停留时间的变化以及控制添加顺序以最大限度地减少不需要的反应性至关重要。总体而言，这项工作展示了自动化、机器学习和机器人技术如何通过创意生成、实验设计、执行和优化的帮助来增强手动实验。