Controlling an organic synthesis robot with machine learning to search for new reactivity

The discovery of chemical reactions is an inherently unpredictable and time-consuming process1. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy2. Reaction prediction based on high-level quantum chemical methods is complex3, even for simple molecules. Although machine learning is powerful for data analysis4,5, its applications in chemistry are still being developed6. Inspired by strategies based on chemists’ intuition7, 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 expert8. 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

Organic synthesis in a modular robotic system driven by a chemical programming language

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

Electron density-based GPT for optimization and suggestion of host–guest binders

Here we present a machine learning model trained on electron density for the production of host–guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with >98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host–guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal–organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant Ka ranging from 13.5 M−1 to 5,470 M−1) and the discovery of 4 unreported guests for [Pd214]4+ (with Ka ranging from 44 M−1 to 529 M−1)

Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine

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

Sweet Anion Receptors: Recognition of Chiral Carboxylate Anions by d‑Glucuronic-Acid-Decorated Diindolylmethane

Anion receptors containing glucuronic acid were synthesized, and their anion binding ability studied. Chirality of anionic guests derived from mandelic acid and amino acids can be distinguished not only in terms of stability constants but also by significant differences in chemical shift changes for sugar moiety protons

Sweet Anion Receptors: Recognition of Chiral Carboxylate Anions by d‑Glucuronic-Acid-Decorated Diindolylmethane

Anion receptors containing glucuronic acid were synthesized, and their anion binding ability studied. Chirality of anionic guests derived from mandelic acid and amino acids can be distinguished not only in terms of stability constants but also by significant differences in chemical shift changes for sugar moiety protons

Controlling transport of ion pairs by a light-responsive heteroditopic azobenzene carrier

Precise and stimuli-controllable transport of charged and neutral guests is a hallmark of cellular processes. Although ion transport has been mimicked with artificial carriers, no such systems are known for electrically neutral ion pairs. We have engineered an artificial carrier (1) that demonstrates a regulated binding and transport of ion pairs dependant on the photo-controlled translocation of the cation and anion binding domains. The NMR and electrochemical experiments supported by DFT calculations show that UVA-generated V-shaped cis-1, featuring ion binding domains close to each other, exhibits an unprecedented 74-fold higher extraction rate of ion pairs relative to the native trans-1

How to explore chemical space using algorithms and automation

Although extending the reactivity of a given class of molecules is relatively straightforward, the discovery of genuinely new reactivity and the molecules that result is a wholly more challenging problem. If new reactions can be considered unpredictable using current chemical knowledge, then we suggest that they are not merely new but also novel. Such a classification, however, requires an expert judge to have access to all current chemical knowledge or risks a lack of information being interpreted as unpredictability. Here, we describe how searching chemical space using automation and algorithms improves the probability of discovery. The former enables routine chemical tasks to be performed more quickly and consistently, while the latter uses algorithms to facilitate the searching of chemical knowledge databases. Experimental systems can also be developed to discover novel molecules, reactions and mechanisms by augmenting the intuition of the human expert. In order to find new chemical laws, we must seek to question current assumptions and biases. Accomplishing that involves using two areas of algorithmic approaches: algorithms to perform searches, and more general machine learning and statistical modelling algorithms to predict the chemistry under investigation. We propose that such a chemical intelligence approach is already being used and that, in the not-too-distant future, the automated chemical reactor systems controlled by these algorithms and monitored by a sensor array will be capable of navigating and searching chemical space more quickly, efficiently and, importantly, without bias. This approach promises to yield not only new molecules but also unpredictable and thus novel reactivity

Synthesis, Structure, and Complexation Properties of a <i>C</i>₃‑Symmetrical Triptycene-Based Anion Receptor: Selectivity for Dihydrogen Phosphate

A new anion binding motif based on triptycene core has been synthesized from 2,7,14-trinitrotriptycene. Its well-defined binding pocket allowed for the selective recognition and sensing of dihydrogen phosphate in DMSO-<i>d</i>₆ + 0.5% H₂O