Christensen Melodie, Yunker Lars P E, Shiri Parisa, Zepel Tara, Prieto Paloma L, Grunert Shad, Bork Finn, Hein Jason E
Department of Chemistry, University of British Columbia Vancouver British Columbia V6T 1Z1 Canada
Department of Process Research and Development, Merck & Co., Inc. Rahway NJ 07065 USA.
Chem Sci. 2021 Oct 27;12(47):15473-15490. doi: 10.1039/d1sc04588a. eCollection 2021 Dec 8.
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.
在过去十年中，自动化已成为合成化学家越来越常用的工具。机器人技术和计算机科学的最新进展催生了自动化系统，这些系统可执行常见的实验室程序，包括平行合成、反应发现、反应优化、时间进程研究和结晶开发。虽然此类系统具有诸多潜在优势，但由于合成程序的高度专业性，其实施过程很少是自动的。每个反应类别都需要仔细执行特定的步骤顺序，具体步骤会因不同条件和化学体系而有所变化。仔细评估这些关键程序要求并确定适合有效实验执行的工具，是开发有效自动化工作流程的关键。即便如此，要使自动化系统的所有组件集成并运行通常也很困难。数据流和专用设备带来了另一层面的挑战。不幸的是，实施自动化系统的痛点和过程往往没有得到分享，或者仍深藏在补充信息中。本视角概述了台式规模合成化学自动化的当前状态，特别强调了核心考量因素以及部署系统随之而来的挑战。重要的是，我们旨在将自动化重新定义为绝非自动的过程，而是一个涉及一系列仔细决策（包括人为和计算决策）以及持续调整的迭代过程。
