Automation and computer-assisted planning for chemical synthesis

The molecules of today — the medicines that cure diseases, the agrochemicals that protect our crops, the materials that make life convenient — are becoming increasingly sophisticated thanks to advancements in chemical synthesis. As tools for synthesis improve, molecular architects can be bold and creative in the way they design and produce molecules. Several emerging tools at the interface of chemical synthesis and data science have come to the forefront in recent years, including algorithms for retrosynthesis and reaction prediction, and robotics for autonomous or high-throughput synthesis. This Primer covers recent additions to the toolbox of the data-savvy organic chemist. There is a new movement in retrosynthetic logic, predictive models of reactivity and chemistry automata, with considerable recent engagement from contributors in diverse fields. The promise of chemical synthesis in the information age is to improve the quality of the molecules of tomorrow through data-harnessing and automation. This Primer is written for organic chemists and data scientists looking to understand the software, hardware, data sets and tactics that are commonly used as well as the capabilities and limitations of the field. The Primer is split into three main components covering retrosynthetic logic, reaction prediction and automated synthesis. The former of these topics is about distilling the strategy of multistep synthesis to a logic that can be taught to a computer. The section on reaction prediction details modern tools and models for developing reaction conditions, catalysts and even new transformations based on information-rich data sets and statistical tools such as machine learning. Finally, we cover recent advances in the use of liquid handling robotics and autonomous systems that can physically perform experiments in the chemistry laboratory. This Primer summarizes the most relevant aspects of chemical synthesis in this information age for those looking to understand the software, hardware and data and how these are used to enable retrosynthetic logic, reaction prediction and automation.