Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform.

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.

Continuous Flow Synthesis of ACE Inhibitors From N-Substituted l-Alanine Derivatives.

A strategy for the continuous flow synthesis of angiotensin converting enzyme (ACE) inhibitors is described. An optimization effort guided by in situ IR analysis resulted in a general amide coupling approach facilitated by N-carboxyanhydride (NCA) activation that was further characterized by reaction kinetics analysis in batch. The three-step continuous process was demonstrated by synthesizing 8 different ACE inhibitors in up to 88 % yield with throughputs in the range of ≈0.5 g h-1 , all while avoiding both isolation of reactive intermediates and process intensive reaction conditions. The process was further developed by preparing enalapril, a World Health Organization (WHO) essential medicine, in an industrially relevant flow platform that scaled throughput to ≈1 g h-1 .

A robotic platform for flow synthesis of organic compounds informed by AI planning.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.

Ni-Catalyzed Electrochemical Decarboxylative C-C Couplings in Batch and Continuous Flow.

An electrochemically driven, nickel-catalyzed reductive coupling of N-hydroxyphthalimide esters with aryl halides is reported. The reaction proceeds under mild conditions in a divided electrochemical cell and employs a tertiary amine as the reductant. This decarboxylative C(sp3)-C(sp2) bond-forming transformation exhibits excellent substrate generality and functional group compatibility. An operationally simple continuous-flow version of this transformation using a commercial electrochemical flow reactor represents a robust and scalable synthesis of value added coupling process.