The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

Abstract

The eighteenth and nineteenth centuries marked a sweeping transition from manual to automated manufacturing on the macroscopic scale. This enabled an unmatched period of human innovation that helped drive the Industrial Revolution. The impact on society was transformative, ultimately yielding substantial improvements in living conditions and lifespan in many parts of the world. During the same time period, the first manual syntheses of organic molecules was achieved. Now, two centuries later, we are poised for an analogous transition from highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of many different types of molecules with the push of a button. Automation of customized small molecule synthesis pathways is already enabling safer, more reproducible, and readily scalable production of specific targets, and general machines now exist for the synthesis of a wide range of different peptides, oligonucleotides, and oligosaccharides. Creating general machines that are similarly capable of making many different types of small molecules on-demand, akin to that which has been achieved on the macroscopic scale with 3D printers, has proven to be substantially more challenging. Yet important progress is being made toward this potentially transformative objective with two complementary approaches: (1) automation of customized synthesis routes to different targets via machines that enable use of many different reactions and starting materials, and (2) automation of generalized platforms that make many different targets using common coupling chemistry and building blocks. Continued progress in these exciting directions has the potential to shift the bottleneck in molecular innovation from synthesis to imagination, and thereby help drive a new industrial revolution on the molecular scale.

Toward Design of Novel Materials for Organic Electronics

“…Examples can be given for, e.g., feature detection, [207] bioactivity prediction, [208] or drug target prediction, [209] and others. [211,212] Automated computational reaction planning can be combined with in silico materials design (be it with physical models and design rules, [39,40] computational screening approaches, [186] or machine learning methods [41] ) and automated in situ synthesis and characterization, [201,213] (b,c) and the power conversion efficiency distribution (d) for a subset of the materials screened in the Harvard Clean Energy project. This would be a big step towards the democratization of chemistry.…”

“…This would be a big step towards the democratization of chemistry. [211,212] Automated computational reaction planning can be combined with in silico materials design (be it with physical models and design rules, [39,40] computational screening approaches, [186] or machine learning methods [41] ) and automated in situ synthesis and characterization, [201,213] (b,c) and the power conversion efficiency distribution (d) for a subset of the materials screened in the Harvard Clean Energy project. The region that allows for power conversion efficiency (with respect to a PCBM acceptor according to the Scharber model) of more than 10% is highlighted in (c).…”

Miniaturized and Automated Synthesis of Biomolecules—Overview and Perspectives

“…Their concept of a materials acceleration platform (MAP) aims for the establishment of self‐driving laboratories that may solve key questions in materials design by combining the most important steps for new developments. As not all developments can be highlighted here, we refer to additional reviews for further reading …”

Hard-threshold neural network-based prediction of organic synthetic outcomes

“…Recently, chemists enthusiastically applied advanced machine learning and artificial intelligence (AI) technologies toward the synthesis of drug molecules. For example, the AI-driven discovery of drug molecules [4][5][6], automated planning of synthetic routes [7][8][9], machine learning-driven optimization of reaction conditions [10][11][12] and autonomous assembly of synthetic processes [13][14][15].…”