Automation isn't automatic

Abstract

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chemical systems. Careful assessment of these critical procedural requirements and identification of the tools suitable for effective experimental execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.

Fig. 1. Flow chart for decision-making around whether to buy or build an automation module.

Fig. 2. Types of synthetic chemistry automation modules with select examples.

Fig. 3. Axelsemrau Chronect outfitted with a Mettler-Toledo Auto Chem Quantos solid dispense module (left). Chemspeed Technologies GDU-S SWILE with a positive-displacement module (right). Reprinted with permission from ref. . Copyright 2020 American Chemical Society.

Fig. 4. Liquid handling modules arranged in two configurations: a syringe pump, selector valve, and needle tip (top) and a peristaltic pump, 6-port, 2-position valve, and pipette tip (bottom).

Fig. 5. Custom filtration apparatus developed for an Unchained Labs platform for polymorph screening. Reprinted with permission from ref. . Copyright 2016 American Chemical Society.

Fig. 6. Magnetic and mechanical stirring modules impart different agitation homogeneity across a reaction plate, efficiency, and scalability.

Fig. 7. Comparison of simple spectroscopic (top) and chromatographic (bottom) analytical methods.

Fig. 8. The use of a webcam for computer-vision based turbidity measurement. Reproduced with permission from ref. . Copyright 2021 Elsevier.

Fig. 9. Robotic modules for translocation.

Fig. 10. (a) HTE to discover optimal conditions for a Suzuki cross-coupling reaction (b) HTE to discover optimal conditions for an asymmetric hydrogenation reaction. Reprinted with permission from ref. . Copyright 2016 American Chemical Society.

Fig. 11. (a) Time profile for the main effects on pyridone formation. (b) Time profile for the main effects on impurity B formation. Adapted with permission from ref. . Copyright 2021 American Chemical Society.

Fig. 12. Qualitative factors that contribute to the selection of high-throughput analysis techniques. Red = least favorable; yellow = moderately favorable, green = most favorable. Reproduced with permission from ref. . Copyright 2021 American Chemical Society.

Fig. 13. Suzuki–Miyaura reaction screening across different scales. Figure reproduced with permission form the authors of ref. .

Fig. 14. Strong base screening workflow involving liquid stock mixture dispensing. Reproduced from ref. with permission from the Royal Society of Chemistry.

Fig. 15. The weighing step of the acetoin dimer is moved to a glove box to account for hygroscopicity. Reproduced from ref. with permission from the Royal Society of Chemistry.

Fig. 16. HTE in the optimization of a tandem Heck–Suzuki reaction. Reproduced with permission from ref. . Copyright 2017 American Chemical Society.

Fig. 17. DoE time course studies enabled by an acidic quench in the Vilsmeier Haack bromination of γ-cyclodextrin. Adapted with permission from ref. . Copyright 2021 American Chemical Society.

Fig. 18. Reaction monitoring with online HPLC analysis enables a more accurate reaction snapshot. Reproduced from ref. with permission from the Royal Society of Chemistry.

Fig. 19. DoE with time course sampling to optimize a diastereoselective oxazolidine synthesis through Unchained Labs OSR sampling at elevated temperature. Reprinted with permission from ref. . Copyright 2018 American Chemical Society.

Fig. 20. Automated closed-loop for reaction optimization. Reproduced with permission from ref. .