The discovery of chemical reactions is an inherently unpredictable and time-consuming process. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy. Reaction prediction based on high-level quantum chemical methods is complex, even for simple molecules. Although machine learning is powerful for data analysis, its applications in chemistry are still being developed. Inspired by strategies based on chemists' intuition, we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chemical reactions quickly, especially if trained by an expert. Here we present an organic synthesis robot that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space. By using machine learning for decision making, enabled by binary encoding of the chemical inputs, the reactions can be assessed in real time using nuclear magnetic resonance and infrared spectroscopy. The machine learning system was able to predict the reactivity of about 1,000 reaction combinations with accuracy greater than 80 per cent after considering the outcomes of slightly over 10 per cent of the dataset. This approach was also used to calculate the reactivity of published datasets. Further, by using real-time data from our robot, these predictions were followed up manually by a chemist, leading to the discovery of four reactions.

One Sentence Summary: A modular platform for synthesis is demonstrated that makes purified organic compounds autonomously without physical reconfiguration and is driven using a chemical programming language.Abstract: The synthesis of complex organic compounds is largely a manual process that is often incompletely documented. To address these shortcomings, we developed an abstraction that maps commonly reported methodological instructions into discrete steps amenable to automation. These unit operations were implemented in a modular robotic platform using a chemical programming language which formalizes and controls the assembly of the molecules.We validated the concept by directing the automated system to synthesize three pharmaceutical compounds, Nytol, Rufinamide, and Sildenafil, without any human intervention. Yields and purities of products and intermediates were comparable to or better than those achieved manually. The syntheses are captured as digital code that can be published, versioned, and transferred flexibly between platforms with no modification, thereby greatly enhancing reproducibility and reliable access to complex molecules.The automation of chemical synthesis is currently expanding, and this is driven by the availability of digital labware. The field currently encompasses areas as diverse as the design of new reactions (1), chemistry in reactionware (2), reaction monitoring and optimization (3,4), flow chemistry (5) for reaction optimization and scale up, to full automation of the synthesis

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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. Automation in Chemical SynthesisMethodologies 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) [1]. 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 [2,3]. 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 machine learning and artificial intelligence (AI).Chemists already benefit from algorithms in the field of chemometrics and, therefore, automation is one step forward that might help chemists to navigate and search chemical space more quickly, efficiently, and importantly, without bias. Chemometrics is a field that employs a broad range of algorithms to solve chemistry-related problems and has been well established over the past 50 years [4]. Figure 2 presents a standard chemometrics workflow for processing data. The process begins with data that may be of various formats that depend upon the experiment type and/or posed question. The next step is data preprocessing, which covers a variety of procedures depending on the type of data analyzed (e.g., peak detection, input of missing data, and/or normalization). This process is followed by statistical modeling, which is divided into supervised and unsupervised approaches. Probably one of the most well-known unsupervised approaches is principal component analysis (s...

Although the automatic synthesis of molecules has been established, each reaction class uses bespoke hardware. This means that the connection of multi-step syntheses in a single machine to run many different protocols and reactions is not possible, as manual intervention is required. Here we show how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity. Developing universal and modular hardware that can be automated using one software system makes a wide variety of batch chemistry accessible. This is shown by our system, which performed around 8,500 operations while reusing only 22 distinct steps in 10 unique modules, with the code able to access 17 different reactions. We also demonstrate a complex convergent robotic synthesis of a peptide reacted with a diazirine-a process requiring 12 synthetic steps.The synthesis of organic small molecules is still largely performed by hand in the laboratory, a paradigm that has barely changed in decades, despite predictions of imminent change 1,2 . The issue is that organic synthesis is not only labour intensive but also highly specialized, requiring years of training. Given these characteristics of organic synthesis, the automation of small-molecule synthesis has the potential to improve reproducibility and accessibility. However, the lack of a universal approach means that current technologies are highly specialized and focus on specific niches, for example, for the automated synthesis of oligopeptides 3 , oligonucleotides 4 , oligosaccharides 5 and, recently, with MIDA boronate building blocks 6 . All these approaches are based on the successive iteration of a small number of robust reactions; hence, they are not generally programmable nor are the unit operations reusable. Such systems are ideal for process intensification but require extensive method development and specific hardware 7 . These limitations have been partly addressed by the development of reconfigurable flow systems addressing a wider range of chemistries 8,9 . What is needed is a paradigm that not only captures the expertise and numerous hours spent discovering and optimizing batch reactions but also is amenable to the development of an overarching ontology that allows universality. Previously, the concept of the Chemputer, a programmable batch synthesis robot, was designed and developed to demonstrate the proof of principle for a general approach to the synthesis of any organic molecule, whereby a range of different molecules could be automatically synthesized on the same hardware 10 . However, the ability to