Universal Chemical Synthesis and Discovery with ‘The Chemputer’

Abstract

Recent advances in chemical programming enable adoption and universal automation of chemical discovery and synthesis, combined with artificial intelligence, to efficiently perform laboratory tasks, including the closed-loop data exploration for new reactivity.Robots can perform chemical reactions and analysis much faster than can be done manually, utilizing trial and error, as well as feedback to make autonomous decisions.Chemists can actively seek out to explore chemical space, aiming for discovery of novel or new molecules and reactions using closed-loop robotic chemical search engines. There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code. There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code. Methodologies for the automation of chemical synthesis, optimization, and discovery have not generally been designed for the realities of laboratory-based research, tending instead to focus on engineering solutions to practical problems. We argue that the potential of rapidly developing technologies (e.g., machine learning and robotics) are more fully realized by operating seamlessly with the way that synthetic chemists currently work (Figure 1) [1Steiner S. et al.Organic synthesis in a modular robotic system driven by a chemical programming language.Science. 2019; 363eaav2211Crossref PubMed Scopus (194) Google Scholar]. This is because the organic chemist often works by thinking backwards as much as they do forwards when planning a synthetic procedure. To reproduce this fundamental mode of operation, a new universal approach to the automated exploration of chemical space is needed that combines an abstraction of chemical synthesis with robotic hardware and closed-loop programming [2Sans V. et al.A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy.Chem. Sci. 2015; 6: 1258-1264Crossref PubMed Google Scholar, 3Kitson P.J. et al.Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals.Science. 2018; 359: 314-319Crossref PubMed Scopus (112) Google Scholar]. However, this leads chemists to constantly test the reactions with different synthetic parameters and conditions. The alternative to this problem, as shown in this opinion article, is the development of an approach to universal chemistry using a programming language with automation in combination with ma