The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

Harnessing alcohols as sustainable reagents for late-stage functionalisation: synthesis of drugs and bio-inspired compounds

Alcohol is ubiquitous with unparalleled structural diversity and thus has wide applications as a native functional group in organic synthesis. It is highly prevalent among biomolecules and offers promising opportunities for the development of chemical libraries. Over the last decade, alcohol has been extensively used as an environmentally friendly chemical for numerous organic transformations. In this review, we collectively discuss the utilisation of alcohol from 2015 to 2023 in various organic transformations and their application toward intermediates of drugs, drug derivatives and natural product-like molecules. Notable features discussed are as follows: (i) sustainable approaches for C-X alkylation (X = C, N, or O) including O-phosphorylation of alcohols, (ii) newer strategies using methanol as a methylating reagent, (iii) allylation of alkenes and alkynes including allylic trifluoromethylations, (iv) alkenylation of N-heterocycles, ketones, sulfones, and ylides towards the synthesis of drug-like molecules, (v) cyclisation and annulation to pharmaceutically active molecules, and (vi) coupling of alcohols with aryl halides or triflates, aryl cyanide and olefins to access drug-like molecules. We summarise the synthesis of over 100 drugs via several approaches, where alcohol was used as one of the potential coupling partners. Additionally, a library of molecules consisting over 60 fatty acids or steroid motifs is documented for late-stage functionalisation including the challenges and opportunities for harnessing alcohols as renewable resources.

Iterative SuFEx approach for sequence-regulated oligosulfates and its extension to periodic copolymers

The synthesis of sequence-regulated oligosulfates has not yet been established due to the difficulties in precise reactivity control. In this work, we report an example of a multi-directional divergent iterative method to furnish oligosulfates based on a chain homologation approach, in which the fluorosulfate unit is regenerated. The oligosulfate sequences are determined by high resolution mass spectrometry of the hydrolyzed fragments, and polysulfate periodic copolymers are synthesized by using oligomeric bisfluorosulfates in a bi-directional fashion. The synthetic utility of this iterative ligation is demonstrated by preparing crosslinked network polymers as synthetic adhesive materials.

Augmenting DMTA using predictive AI modelling at AstraZeneca

Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery.

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.

Oxazaborolidinones: Steric Coverage Effect of Lewis Acidic Boron Center in Suzuki-Miyaura Coupling Reactions

It was demonstrated that α-hydroxycarboxamide is an excellent boron-protecting group. The reaction between α-hydroxycarboxamide and organoboronic acids produced stable oxazaborolidinones (OxBs), in which thesp 2 ${{_{{\rm sp}{^{2}}}}}$ -hybridized boron atom was sterically protected by α-hydroxycarboxamide. The alkyl groups of the α-hydroxycarboxamide moiety can dynamically cover the p-orbital of thesp 2 ${{_{{\rm sp}{^{2}}}}}$ -hybridized boron center, creating a small space around the boron atom, allowing for smooth transmetalation by a Pd catalyst and easy deprotection by water. This protecting phenomenon is effective for readily purification, Suzuki-Miyaura coupling reactions with unstable boronic acids and iterative cross-couplings.

Automation to Enable High-Throughput Chemical Proteomics

Chemical proteomics utilizes small-molecule probes to covalently engage with their interacting proteins. Since chemical probes are tagged to the active or binding sites of functional proteins, chemical proteomics can be used to profile protein targets, reveal precise binding sites/mechanisms, and screen inhibitors competing with probes in a biological context. These capabilities of chemical proteomics have great potential to enable discoveries of both drug targets and lead compounds. However, chemical proteomics is limited by the time-consuming bottleneck of sample preparations, which are processed manually. With the advancement of robotics and artificial intelligence, it is now possible to automate workflows to make chemical proteomics sample preparation a high-throughput process. An automated robotic system represents a major technological opportunity to speed up advances in proteomics, open new frontiers in drug target discovery, and broaden the future of chemical biology.

A Versatile Route to Acyl (MIDA)Boronates

A modular approach to highly functional acyl (MIDA)boronates is described. It involves the generation of the hitherto unknown radical derived from acetyl (MIDA)boronate and its capture by various alkenes, including electronically unbiased, unactivated alkenes. In contrast to the anion of acetyl (MIDA)boronate, which has not so far been employed in synthesis, the corresponding radical is well behaved and readily produced from the novel α-xanthyl acetyl (MIDA)boronate. This shelf-stable, easily prepared solid is a convenient acyl (MIDA)boronate transfer agent that provides a direct entry to numerous otherwise inaccessible structures, including latent 1,4-dicarbonyl derivatives that can be transformed into B(MIDA) substituted pyrroles and furans. A competition experiment indicated the acyl (MIDA)boronate substituted radical to be more stable than the all-carbon acetonyl radical but somewhat less reactive in additions to alkenes.

MIDA- and TIDA-Boronates Stabilize α-Radicals Through B-N Hyperconjugation

Multifunctional organoboron compounds increasingly enable the simple generation of complex, Csp3 -rich small molecules. The ability of boron-containing functional groups to modify the reactivity of α-radicals has also enabled a myriad of chemical reactions. Boronic esters with vacant p-orbitals have a significant stabilizing effect on α-radicals due to delocalization of spin density into the empty orbital. The effect of coordinatively saturated derivatives, such as N-methyliminodiacetic acid (MIDA) boronates and counterparts, remains less clear. Herein, we demonstrate that coordinatively saturated MIDA and TIDA boronates stabilize secondary alkyl α-radicals via σB-N hyperconjugation in a manner that allows site-selective C-H bromination. DFT calculated radical stabilization energies and spin density maps as well as LED NMR kinetic analysis of photochemical bromination rates of different boronic esters further these findings. This work clarifies that the α-radical stabilizing effect of boronic esters does not only proceed via delocalization of radical character into vacant boron p-orbitals, but that hyperconjugation of tetrahedral boron-containing functional groups and their ligand electron delocalizing ability also play a critical role. These findings establish boron ligands as a useful dial for tuning reactivity at the α-carbon.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening

Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

Photo‐Induced Homologation of Carbonyl Compounds for Iterative Syntheses

We describe a photo-induced reaction for the in situ generation of highly reactive alkyl diazo species from carbonyl precursors via photo-excitation of N-tosylhydrazone anions. The diazo intermediates undergo efficient C-H insertion of aldehydes, leading to the productive synthesis of aldehydes and ketones. The method is applicable to the iterative synthesis of densely functionalized carbonyl compounds through sequential trapping of the diazo species with various aldehydes. The reaction proceeds without the need of any catalyst by light irradiation and features high functional group tolerance. More than 70 examples, some performed on a gram-scale, demonstrate the broad applicability of this reaction sequence in synthesis.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging and consequential problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches. This study provides a practical road map for solving multidimensional chemical optimization problems with large search spaces.

Synthesis and Glycosidation of Anomeric Halides: Evolution from Early Studies to Modern Methods of the 21st Century

Advances in synthetic carbohydrate chemistry have dramatically improved access to common glycans. However, many novel methods still fail to adequately address challenges associated with chemical glycosylation and glycan synthesis. Since a challenge of glycosylation has remained, scientists have been frequently returning to the traditional glycosyl donors. This review is dedicated to glycosyl halides that have played crucial roles in shaping the field of glycosciences and continue to pave the way toward our understanding of chemical glycosylation.

Digitization and validation of a chemical synthesis literature database in the ChemPU

Despite huge potential, automation of synthetic chemistry has only made incremental progress over the past few decades. We present an automatically executable chemical reaction database of 100 molecules representative of the range of reactions found in contemporary organic synthesis. These reactions include transition metal–catalyzed coupling reactions, heterocycle formations, functional group interconversions, and multicomponent reactions. The chemical reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular ChemPU’s with yields and purities comparable to those achieved by an expert chemist. We also demonstrate the automatic purification of a range of compounds using a chromatography module seamlessly coupled to the platform and programmed with the same language.

Digitizing Chemical Synthesis in 3D Printed Reactionware

Chemistry digitization requires an unambiguous link between experiments and the code used to generate the experimental conditions and outcomes, yet this process is not standardized, limiting the portability of any chemical code. What is needed is a universal approach to aid this process using a well-defined standard that is composed of syntheses that are employed in modular hardware. Herein we present a new approach to the digitization of organic synthesis that combines process chemistry principles with 3D printed reactionware. This approach outlines the process for transforming unit operations into digitized hardware and well-defined instructions that ensure effective synthesis. To demonstrate this, we outline the process for digitizing 3 MIDA boronate building blocks, an ester hydrolysis, a Wittig olefination, a Suzuki-Miyaura coupling reaction, and synthesis of the drug sulfanilamide.

Ketones as strategic building blocks for the synthesis of natural product-inspired compounds

Natural product-inspired compound collections serve as excellent sources for the identification of new bioactive compounds to treat disease. However, such compounds must necessarily be more structurally-enriched than traditional screening compounds, therefore inventive synthetic strategies and reliable methods are needed to prepare them. Amongst the various possible starting materials that could be considered for the synthesis of natural product-inspired compounds, ketones can be especially valuable due to the vast variety of complexity-building synthetic transformations that they can take part in, their high prevalence as commercial building blocks, and relative ease of synthesis. With a view towards developing a unified synthetic strategy for the preparation of next generation bioactive compound collections, this review considers whether ketones could serve as general precursors in this regard, and summarises the opulence of synthetic transformations available for the annulation of natural product ring-systems to ketone starting materials.

Using automated synthesis to understand the role of side chains on molecular charge transport

The development of next-generation organic electronic materials critically relies on understanding structure-function relationships in conjugated polymers. However, unlocking the full potential of organic materials requires access to their vast chemical space while efficiently managing the large synthetic workload to survey new materials. In this work, we use automated synthesis to prepare a library of conjugated oligomers with systematically varied side chain composition followed by single-molecule characterization of charge transport. Our results show that molecular junctions with long alkyl side chains exhibit a concentration-dependent bimodal conductance with an unexpectedly high conductance state that arises due to surface adsorption and backbone planarization, which is supported by a series of control experiments using asymmetric, planarized, and sterically hindered molecules. Density functional theory simulations and experiments using different anchors and alkoxy side chains highlight the role of side chain chemistry on charge transport. Overall, this work opens new avenues for using automated synthesis for the development and understanding of organic electronic materials.

Development of organic electronic materials relies on understanding structure-function relationships in conjugated polymers but the synthetic workload to make large numbers of new compounds presents a practical barrier to properly survey conjugated organic derivatives. Here, the authors use automated synthesis to prepare a library of conjugated oligomers with systematically varied side chain composition followed by single-molecule characterization of charge transport.

Automated iterative Csp3-C bond formation

Fully automated synthetic chemistry would substantially change the field by providing broad on-demand access to small molecules. However, the reactions that can be run autonomously are still limited. Automating the stereospecific assembly of Csp3-C bonds would expand access to many important types of functional organic molecules1. Previously, methyliminodiacetic acid (MIDA) boronates were used to orchestrate the formation of Csp2-Csp2 bonds and were effective building blocks for automating the synthesis of many small molecules2, but they are incompatible with stereospecific Csp3-Csp2 and Csp3-Csp3 bond-forming reactions3-10. Here we report that hyperconjugative and steric tuning provide a new class of tetramethyl N-methyliminodiacetic acid (TIDA) boronates that are stable to these conditions. Charge density analysis11-13 revealed that redistribution of electron density increases covalency of the N-B bond and thereby attenuates its hydrolysis. Complementary steric shielding of carbonyl π-faces decreases reactivity towards nucleophilic reagents. The unique features of the iminodiacetic acid cage2, which are essential for generalized automated synthesis, are retained by TIDA boronates. This enabled Csp3 boronate building blocks to be assembled using automated synthesis, including the preparation of natural products through automated stereospecific Csp3-Csp2 and Csp3-Csp3 bond formation. These findings will enable increasingly complex Csp3-rich small molecules to be accessed via automated assembly.

Automated Flow and Real-Time Analytics Approach for Screening Functional Group Tolerance in Heterogeneous Catalytic Reactions

Heterogeneous hydrogenation reactions are widely used in synthesis, and performing them using continuous flow technologies addresses many of the safety, scalability and sustainability issues. However, one of the main potential...

Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...

Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization

The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied...

Is Aromaticity a Driving Force in Catalytic Cycles? A Case from the Cycloisomerization of Enynes Catalyzed by All-Metal Aromatic Pd3+ Clusters and Carboxylic Acids

The work details a mechanistic study based on density functional theory modeling on the cycloisomerization of polyunsaturated substrates catalyzed by all-metal aromatic tripalladium complexes and carboxylic acids. These clusters are an emerging class of catalysts for a variety of relevant transformations, including C-C forming processes that occur under mild conditions and display synthetic features complementary to those of established mononuclear complexes. This study is the first computational one devoted to the comprehension of the series of elementary steps involved in a synthetic transformation catalyzed by an all-metal aromatic complex. Present results confirm previous experimental hints on the striking mechanistic differences exerted by these clusters with respect to the usual cyclization pathways of related substrates. Moreover, the catalytic cycle involving present all-metal aromatic clusters closely parallels the mechanism of the aromatic substitution of regular arenes.

Current Topics in Ionic Liquid Crystals

Ionic liquid crystals (ILCs), that is, ionic liquids exhibiting mesomorphism, liquid crystalline phases, and anisotropic properties, have received intense attention in the past years. Among others, this is due to their special properties arising from the combination of properties stemming from ionic liquids and from liquid crystalline arrangements. Besides interesting fundamental aspects, ILCs have been claimed to have tremendous application potential that again arises from the combination of properties and architectures that are not accessible otherwise, or at least not accessible easily by other strategies. The current review highlights recent developments in ILC research, starting with some key fundamental aspects. Further subjects covered include the synthesis and variations of modern ILCs, including the specific tuning of their mesomorphic behavior. The review concludes with reflections on some applications that may be within reach for ILCs and finally highlights a few key challenges that must be overcome prior and during true commercialization of ILCs.

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

Chemputation and the Standardization of Chemical Informatics

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR

A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. "Chemputer" which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.

Organic Sonochemistry: A Chemist's Timely Perspective on Mechanisms and Reactivity

Sonochemistry, the use of sound waves, usually within the ultrasonic range (>20 kHz), to boost or alter chemical properties and reactivity constitutes a long-standing and sustainable technique that has, however, received less attention than other activation protocols despite affordable setups. Even if unnecessary to underline the impact of ultrasound-based strategies in a broad range of chemical and biological applications, there is considerable misunderstanding and pitfalls regarding the interpretation of cavitational effects and the actual role played by the acoustic field. In this Perspective, with an eye on mechanisms in particular, we discuss the potentiality of sonochemistry in synthetic organic chemistry through selected examples of past and recent developments. Such examples illustrate specific controlling effects and working rules. Looking back at the past while looking forward to advancing the field, some essentials of sonochemical activation will be distilled.

Insight into the recent advances of microwave pretreatment technologies for the conversion of lignocellulosic biomass into sustainable biofuel

The utilization of renewable lignocellulosic biomasses for bioenergy synthesis is believed to facilitate competitive commercialization and realize affordable clean energy sources in the future. Among the pathways for biomass pretreatment methods that enhance the efficiency of the whole biofuel production process, the combined microwave irradiation and physicochemical approach is found to provide many economic and environmental benefits. Several studies on microwave-based pretreatment technologies for biomass conversion have been conducted in recent years. Although some reviews are available, most did not comprehensively analyze microwave-physicochemical pretreatment techniques for biomass conversion. The study of these techniques is crucial for sustainable biofuel generation. Therefore, the biomass pretreatment process that combines the physicochemical method with microwave-assisted irradiation is reviewed in this paper. The effects of this pretreatment process on lignocellulosic structure and the ratio of achieved components were also discussed in detail. Pretreatment processes for biomass conversion were substantially affected by temperature, irradiation time, initial feedstock components, catalyst loading, and microwave power. Consequently, neoteric technologies utilizing high efficiency-based green and sustainable solutions should receive further focus. In addition, methodologies for quantifying and evaluating effects and relevant trade-offs should be develop to facilitate the take-off of the biofuel industry with clean and sustainable goals.

Opportunities and challenges in translational science

The mission of translational science is to bring predictivity and efficiency to the development and dissemination of interventions that improve human health. Ten years ago this year, the National Center for Advancing Translational Sciences was founded to embody, conduct, and support this new discipline. The Center's first decade has brought substantial progress across a broad range of translational areas, from diagnostic and drug development to clinical trials to implementation science to education. The origins of the translational science and advances to this point are reviewed here and allow the establishment of an ambitious future research agenda for the field.

Determining usefulness of machine learning in materials discovery using simulated research landscapes

When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this context, the following two conditions are highly common: (i) constructing new unbiased data sets is too expensive and the global knowledge effectively does not change by performing a limited number of novel measurements; (ii) the performance of the material depends on a limited number of physical parameters, much smaller than the range of variables that can be changed, albeit such parameters are unknown or not measurable. To determine the usefulness of ML under these conditions, we introduce the concept of simulated research landscapes, which describe how datasets of arbitrary complexity evolve over time. Simulated research landscapes allow us to use different discovery strategies to compare standard materials exploration with ML-guided explorations, i.e. we can measure quantitatively the benefit of using a specific ML model. We show that there is a window of opportunity to obtain a significant benefit from ML-guided strategies. The adoption of ML can take place too soon (not enough information to find patterns) or too late (dense datasets only allow for negligible ML benefit), and the adoption of ML can even slow down the discovery process in some cases. We offer a qualitative guide on when ML can accelerate the discovery of new best-performing materials in a field under specific conditions. The answer in each case depends on factors like data dimensionality, corrugation and data collection strategy. We consider how these factors may affect the ML prediction capabilities and discuss some general trends.

An integrated console for capsule-based, automated organic synthesis

The current laboratory practices of organic synthesis are labor intensive, impose safety and environmental hazards, and hamper the implementation of artificial intelligence guided drug discovery. Using a combination of reagent design, hardware engineering, and a simple operating system we provide an instrument capable of executing complex organic reactions with prepacked capsules. The machine conducts coupling reactions and delivers the purified products with minimal user involvement. Two desirable reaction classes – the synthesis of saturated N-heterocycles and reductive amination – were implemented, along with multi-step sequences that provide drug-like organic molecules in a fully automated manner. We envision that this system will serve as a console for developers to provide synthetic methods as integrated, user-friendly packages for conducting organic synthesis in a safe and convenient fashion.

Using a combination of reagent design, hardware engineering, and a simple operating system we provide an instrument capable of executing complex organic reactions using prepacked capsules with minimal user involvement.

C-N Bond Formation by Consecutive Continuous-Flow Reductions towards A Medicinally Relevant Piperazine Derivative

A new, continuous-flow consecutive reduction method was developed for the C-N bond formation in the synthesis of the key intermediate of the antipsychotic drug cariprazine. The two-step procedure consists of a DIBAL-H mediated selective ester reduction conducted in a novel, miniature alternating diameter reactor, followed by reductive amination using catalytic hydrogenation on 5% Pt/C. The connection of the optimized modules was accomplished using an at-line extraction to prevent precipitation of the aluminum salt byproducts.

Direct and Divergent Solid-Phase Synthesis of Azobenzene and Spiropyran Derivatives

Here, we report a solid-phase approach to synthesize azobenzene and spiropyran derivatives. The divergent synthesis process requires no purification steps to obtain the desired product with a 28-55% yield, depending on the specific compound. For the spiropyran compounds, solid-phase resin cleavage is performed under mild conditions to minimize spiropyran ring opening. The solid-phase method enables the synthesis of a library of azobenzene and spiropyran derivatives without the need to develop purification strategies for each derivative.

Automated Experimentation Powers Data Science in Chemistry

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery

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.

ChemOS: Orchestrating autonomous experimentation

ChemOS aims to catalyze the expansion of autonomous laboratories and to disrupt the conventional approach to experimentation.