A universal system for digitization and automatic execution of the chemical synthesis literature

Robotic systems for chemical synthesis are growing in popularity but can be difficult to run and maintain because of the lack of a standard operating system or capacity for direct access to the literature through natural language processing. Here we show an extendable chemical execution architecture that can be populated by automatically reading the literature, leading to a universal autonomous workflow. The robotic synthesis code can be corrected in natural language without any programming knowledge and, because of the standard, is hardware independent. This chemical code can then be combined with a graph describing the hardware modules and compiled into platform-specific, low-level robotic instructions for execution. We showcase automated syntheses of 12 compounds from the literature, including the analgesic lidocaine, the Dess-Martin periodinane oxidation reagent, and the fluorinating agent AlkylFluor

Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data

Interpreting the outcome of chemistry experiments consistently is slow and frequently introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardize the discovery process and accelerate the interpretation of high-dimensional data aided by the expert chemist’s intuition. We demonstrate a digital Oracle that interprets chemical reactivity using probability. By carrying out >500 reactions covering a large space and retaining both the positive and negative results, the Oracle was able to rediscover eight historically important reactions including the aldol condensation, Buchwald–Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig, and Wittig–Horner reactions. This paradigm for decoding reactivity validates and formalizes the expert chemist’s experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data

Intuition-enabled machine learning beats the competition when joint human-robot teams perform inorganic chemical experiments

Traditionally, chemists have relied on years of training and accumulated experience in order to discov-er new molecules. But the space of possible molecules so vast, only a limited exploration with the tra-ditional methods can be ever possible. This means that many opportunities for the discovery of inter-esting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the de-velopment of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by compar-ing collaboration between human experimenters with an algorithm-based search, against their perfor-mance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone

Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network

We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (p-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a “multistep, single-substrate” cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C–C bonds involving sp–sp2 and sp–sp3 carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input

Evidence of selection in mineral mediated polymerization reactions executed in a robotic Chemputer system

It has long been thought that abiogenesis requires a process of selection and evolution at the molecular level, but this process is hard to explore experimentally. One solution could be the use of automation in experiments which could allow for traceability and the ability to explore a larger reaction space. We report a fully programmable and automated platform to explore the reactions of amino acids in the presence of mineral environments. The robotic system is based upon the Chemputer system which has well defined modules, software, and a chemical programming language to orchestrate the chemical processes, including analysis. The reaction mixtures were analysed with tandem mass spectrometry and a peptide sequencing algorithm. Each experiment was screened for 1,398,100 possible unique sequences, and more than 550 specifically defined sequences were confirmed experimentally. This work aimed to develop a new understanding of selection in repeated cycles of polymerisation reactions to explore the emergence of well-defined amino acid sequences. We found that the outcome of oligomerisation was significantly influenced by the presence of different minerals, and that a serpentine environment selects glycine and phenylalanine rich fragments that enable the formation of longer oligomers with well-defined sequences as a function of cycle number

An integrated self-optimizing programmable chemical synthesis and reaction engine

Robotic platforms for chemistry are developing rapidly but most systems are not currently able to adapt to changing circumstances in real-time. We present a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, we demonstrate the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures. We also show how the use of in-line spectroscopy such as HPLC, Raman, and NMR can be used for closed-loop optimization of reactions, exemplified using Van Leusen oxazole synthesis, a four-component Ugi condensation and manganese-catalysed epoxidation reactions, as well as two previously unreported reactions, discovered from a selected chemical space, providing up to 50% yield improvement over 25–50 iterations. Finally, we demonstrate an experimental pipeline to explore a trifluoromethylations reaction space, that discovers new molecules

Investigating and quantifying molecular complexity using assembly theory and spectroscopy

Current approaches to evaluate molecular complexity use algorithmic complexity, rooted in computer science, and thus are not experimentally measurable. Directly evaluating molecular complexity could be used to study directed vs undirected processes in the creation of molecules, with potential applications in drug discovery, the origin of life, and artificial life. Assembly theory has been developed to quantify the complexity of a molecule by finding the shortest path to construct the molecule from building blocks, revealing its molecular assembly index (MA). In this study, we present an approach to rapidly infer the MA of molecules from spectroscopic measurements. We demonstrate that the MA can be experimentally measured by using three independent techniques: nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS), and infrared spectroscopy (IR). By identifying and analyzing the number of absorbances in IR spectra, carbon resonances in NMR, or molecular fragments in tandem MS, the MA of an unknown molecule can be reliably estimated. This represents the first experimentally quantifiable approach to determining molecular assembly. This paves the way to use experimental techniques to explore the evolution of complex molecules as well as a unique marker of where an evolutionary process has been operating

Detection of Micron-Sized Chemical Droplets Using a Commodity Digital Camera Setup

The availability, portability, and low cost of electronic devices have made them a prime candidate for the rapid detection of chemical particles. Here we designed a chemical particle detection system based on a Raspberry Pi camera to detect micron droplets generated by ultrasonic atomizers. Through the analysis of sample photos and droplet size, we found that the detection system could clearly image micron-sized particles and accurately measure the particle size. The devices used in this system were all low-cost widely accessible digital cameras, so this detection technique could meet the requirements of low-cost and rapid detection technology, and widely deployed as a practical readout method in chemistry experiments

Digitizing chemistry using the chemical processing unit: from synthesis to discovery

Conspectus The digitization of chemistry is not simply about using machine learning or artificial intelligence systems to process chemical data, or about the development of ever more capable automation hardware; instead, it is the creation of a hard link between an abstracted process ontology of chemistry and bespoke hardware for performing reactions or exploring reactivity. Chemical digitization is therefore about the unambiguous development of an architecture, a chemical state machine, that uses this ontology to connect precise instruction sets to hardware that performs chemical transformations. This approach enables a universal standard for describing chemistry procedures via a chemical programming language and facilitates unambiguous dissemination of these procedures. We predict that this standard will revolutionize the ability of chemists to collaborate, increase reproducibility and safety, as we all as optimize for cost and efficiency. Most importantly, the digitization of chemistry will dramatically reduce the labor needed to make new compounds and broaden accessible chemical space. In recent years, the developments of automation in chemistry have gone beyond flow chemistry alone, with many bespoke workflows being developed not only for automating chemical synthesis but also for materials, nanomaterials, and formulation production. Indeed, the leap from fixed-configuration synthesis machines like peptide, nucleic acid, or dedicated cross-coupling engines is important for developing a truly universal approach to "dial-a-molecule". In this case, a key conceptual leap is the use of a batch system that can encode the chemical reagents, solvent, and products as packets which can be moved around the system, and a graph-based approach for the description of hardware modules that allows the compilation of chemical code that runs on, in principle, any hardware. Further, the integration of sensor systems for monitoring and controlling the state of the chemical synthesis machine, as well as high resolution spectroscopic tools, is vital if these systems are to facilitate closed-loop autonomous experiments. Systems that not only make molecules and materials, but also optimize their function, and use algorithms to assist with the development of new synthetic pathways and process optimization are also possible. Here, we discuss how the digitization of chemistry is happening, building on the plethora of technological developments in hardware and software. Importantly, digital-chemical robot systems need to integrate feedback from simple sensors, e.g., conductivity or temperature, as well as online analytics in order to navigate process space autonomously. This will open the door to accessing known molecules (synthesis), exploring whether known compounds/reactions are possible under new conditions (optimization), and searching chemical space for unknown and unexpected new molecules, reactions, and modes of reactivity (discovery). We will also discuss the role of chemical knowledge and how this can be used to challenge bias, as well as define and expand synthetically accessible chemical space using programmable robotic chemical state machines

Stabilization of a strained heteroradialene by peripheral electron delocalization

Dimethylamine and 2,4,6-triformylphloroglucinol react to form a product with a highly contorted nonplanar geometry due to favorable electron delocalization. This new heteroradialene compound has been studied by X-ray diffraction, variable-temperature multinuclear NMR spectroscopy, IR spectroscopy, UV–vis spectroscopy, and ab initio calculations. Electron delocalization throughout the periphery of the central ring leads to a structure that retains very little of the aromatic characteristics of the starting material