We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the platform to be used to study both thermal and photochemical reactions. We incorporate a Bayesian optimization algorithm into the control software to enable reaction optimization over both categorical and continuous variables. We demonstrate the capabilities of both the preliminary single-channel and parallelized versions of the platform using a series of model thermal and photochemical reactions. We conduct a series of reaction optimization campaigns and demonstrate rapid acquisition of the data necessary to determine reaction kinetics. The platform is flexible in terms of use case: it can be used either to investigate reaction kinetics or to perform reaction optimization over a wide range of chemical domains.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

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.

Research-scale fully automated flow platform for reaction self-optimization with solids handling facilitates identification of optimal conditions for continuous manufacturing of pharmaceuticals while reducing amounts of raw materials consumed.

The implementation of self-optimizing flow reactors has been mostly limited to model reactions or known synthesis routes. In this work, a self-optimizing flow photochemistry platform is used to develop an original synthesis of the bioactive fragment of Salbutamol and derivatives. The key photochemical steps for the construction of the aryl vicinyl amino alcohol moiety consist of a C-C bond forming reaction followed by an unprecedented, high yielding (>80 %), benzylic oxidative cyclization.

The experimental approach taken and challenges overcome in developing a high-purity production (>100 g) scale process for the telescoped synthesis of the antibiotic ciprofloxacin is outlined. The process was first optimized for each step sequentially with regard to purity and yield, with necessary process changes identified and implemented before scaling for longer runs. These changes included implementing a continuous liquid-liquid extraction (CLLE) step and eliminating and replacing the base 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) initially used in the ring-closure step due to DBU plausibly forming a decomposition side product that negatively impacted the final product purity. Process conditions were scaled 1.5-2-fold in order to enable the ultimate project goal of producing enough crude ciprofloxacin within 24 h to manufacture 1000 250 mg tablets. Working toward this goal, several production-scale runs were carried out to assess the reproducibility and robustness of the finalized process conditions, with the first three steps being run continuously up to 22 h and the last two steps being run continuously up to 10 h. The end result is a process with a throughput of similar to 29 g/h (similar to 700 g/24 h) with a crude product stream profile of 94 +/- 2% and 34 +/- 3 mg/ mL after five chemical transformations across four reactors and one continuous CLLE unit operation with each intermediate step maintaining a purity >95% by HPLC.

Traditional pharmaceutical manufacturing is based on a complex supply chain that is vulnerable to spikes in demand and interruptions. Continuous pharmaceutical production in compact modules is a potential solution that allows for drug manufacturing when and where it is needed with significantly shorter lead times. As part of the Pharmacy on Demand (PoD) initiative, we demonstrate the potential for end-to-end manufacturing of multiple drug substances in reconfigurable devices, under common industrial constraints, and within a challenging manufacturing time frame. A new set of refrigerator-sized modules was constructed for the synthesis, isolation, and formulation of several drugs, with focus on achieving high manufacturing throughputs, and allowing for the production of pharmaceutical tablets. Their operation is demonstrated with the synthesis and formulation of USP-compliant tablets of diazepam, diphenhydramine hydrochloride, and ciprofloxacin hydrochloride, as well as liquid formulations of lidocaine hydrochloride and atropine sulfate.

Flow chemistry devices have seen an increase as research tools in recent years. We present a modular system for benchtop continuous flow that allows user flexibility and reliability in the pursuit of challenging chemistry. An oscillating baffle reactor mold-formed in perfluoroalkoxy alkane enables handling reactions forming solids. A LabView control system with help from Omega temperature controllers allows for simple and precise regulation of reaction conditions with real-time monitoring of the temperature and pressure. A new pressurized sample collection vessel allows for inert and pressurized sampling into multiple vials. An array of case studies serves to validate the system by demonstrating the use of interchangeable reactors with in-line heating and cooling, along with the ability to handle highly corrosive and reactive chemical reagents such as white fuming HNO3.

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.