A Brief Introduction to Chemical Reaction Optimization

Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms

Exploring catalytic reaction mechanisms is crucial for understanding chemical processes, optimizing reaction conditions, and developing more effective catalysts. We present a reaction-agnostic framework based on high-throughput deep reinforcement learning with first principles (HDRL-FP) that offers excellent generalizability for investigating catalytic reactions. HDRL-FP introduces a generalizable reinforcement learning representation of catalytic reactions constructed solely from atomic positions, which are subsequently mapped to first-principles-derived potential energy landscapes. By leveraging thousands of simultaneous simulations on a single GPU, HDRL-FP enables rapid convergence to the optimal reaction path at a low cost. Its effectiveness is demonstrated through the studies of hydrogen and nitrogen migration in Haber-Bosch ammonia synthesis on the Fe(111) surface. Our findings reveal that the Langmuir-Hinshelwood mechanism shares the same transition state as the Eley-Rideal mechanism for H migration to NH2, forming ammonia. Furthermore, the reaction path identified herein exhibits a lower energy barrier compared to that through nudged elastic band calculation.

New Opportunities for Organic Synthesis with Superheated Flow Chemistry

ConspectusFlow chemistry has brought a fresh breeze with great promises for chemical manufacturing, yet critical deterrents persist. To remain economically viable at production scales, flow processes demand quick reactions, which are actually not that common. Superheated flow technology stands out as a promising alternative poised to confront modern chemistry challenges. While continuous micro- and mesofluidic reactors offer uniform heating and rapid cooling across different scales, operating above solvent boiling points (i.e., operating under superheated conditions) significantly enhances reaction rates. Despite the energy costs associated with high temperatures, superheated flow chemistry aligns with sustainability goals by improving productivity (process intensification), offering solvent flexibility, and enhancing safety.However, navigating the unconventional chemical space of superheated flow chemistry can be cumbersome, particularly for neophytes. Expanding the temperature/pressure process window beyond the conventional boiling point under the atmospheric pressure limit vastly increases the optimization space. When associated with conventional trial-and-error approaches, this can become exceedingly wasteful, resource-intensive, and discouraging. Over the years, flow chemists have developed various tools to mitigate these challenges, with an increased reliance on statistical models, artificial intelligence, and experimental (kinetics, preliminary test reactions under microwave irradiation) or theoretical (quantum mechanics) a priori knowledge. Yet, the rationale for using superheated conditions has been slow to emerge, despite the growing emphasis on predictive methodologies.To fill this gap, this Account provides a concise yet comprehensive overview of superheated flow chemistry. Key concepts are illustrated with examples from our laboratory's research, as well as other relevant examples from the literature. These examples have been thoroughly studied to answer the main questions Why? At what cost? How? For what? The answers we provide will encourage educated and widespread adoption. The discussion begins with a demonstration of the various advantages arising from superheated flow chemistry. Different reactor alternatives suitable for high temperatures and pressures are then presented. Next, a clear workflow toward strategic adoption of superheated conditions is resorted either using Design of Experiments (DoE), microwave test chemistry, kinetics data, or Quantum Mechanics (QM). We provide rationalization for chemistries that are well suited for superheated conditions (e.g., additions to carbonyl functions, aromatic substitutions, as well as C-Y [Y = N, O, S, C, Br, Cl] heterolytic cleavages). Lastly, we bring the reader to a rational decision analysis toward superheated flow conditions. We believe this Account will become a reference guide for exploring extended chemical spaces, accelerating organic synthesis, and advancing molecular sciences.

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction

Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Deep Kernel learning for reaction outcome prediction and optimization

Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the feature learning ability of neural networks (NNs) with uncertainty quantification of GPs in a deep kernel learning (DKL) framework to predict the reaction outcome. The DKL model is observed to obtain very good predictive performance across different input representations. It significantly outperforms standard GPs and provides comparable performance to graph neural networks, but with uncertainty estimation. Additionally, the uncertainty estimates on predictions provided by the DKL model facilitated its incorporation as a surrogate model for Bayesian optimization (BO). The proposed method, therefore, has a great potential towards accelerating reaction discovery by integrating accurate predictive models that provide reliable uncertainty estimates with BO.

Generation and Application of Aryl Radicals Under Photoinduced Conditions

Photoinduced aryl radical generation is a powerful strategy in organic synthesis that facilitates the formation of diverse carbon-carbon and carbon-heteroatom bonds. The synthetic applications of photoinduced aryl radical formation in the synthesis of complex organic compounds, including natural products, physiologically significant molecules, and functional materials, have received immense attention. An overview of current developments in photoinduced aryl radical production methods and their uses in organic synthesis is given in this article. A generalized idea of how to choose the reagents and approach for the generation of aryl radicals is described, along with photoinduced techniques and associated mechanistic insights. Overall, this article offers a critical assessment of the mechanistic results as well as the selection of reaction parameters for specific reagents in the context of radical cascades, cross-coupling reactions, aryl radical functionalization, and selective C-H functionalization of aryl substrates.

Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.

Flow-based bioconjugation of coumarin phosphatidylethanolamine probes: Optimised synthesis and membrane molecular dynamics studies

A series of phosphatidylethanolamine fluorescent probes head-labelled with 3-carboxycoumarin was prepared by an improved bioconjugation approach through continuous flow synthesis. The established procedure, supported by a design of experiment (DoE) set-up, resulted in a significant reduction in the reaction time compared to the conventional batch method, in addition to a minor yield increase. The characterization of these probes was enhanced by an in-depth molecular dynamics (MD) study of the behaviour of a representative probe of this family, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine labelled with 3-carboxycoumarin (POPE-COUM), in bilayers of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine (SLPC) 2:1, mimicking the composition of the egg yolk lecithin membranes recently used experimentally by our group to study POPE-COUM as a biomarker of the oxidation state and integrity of large unilamellar vesicles (LUVs). The MD simulations revealed that the coumarin group is oriented towards the bilayer interior, leading to a relatively internal location, in agreement with what is observed in the nitrobenzoxadiazole fluorophore of commercial head-labelled NBD-PE probes. This behaviour is consistent with the previously stated hypothesis that POPE-COUM is entirely located within the LUVs structure. Hence, the delay on the oxidation of the probe in the oxygen radical absorbance capacity (ORAC) assays performed is related with the inaccessibility of the probe until alteration of the LUV structure occurs. Furthermore, our simulations show that POPE-COUM exerts very little global and local perturbation on the host bilayer, as evaluated by key properties of the unlabelled lipids. Together, our findings establish PE-COUM as suitable fluorescent lipid analogue probes.

CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space

Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model's prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R-an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. SCIENTIFIC CONTRIBUTION: To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

The Role of Base in Reaction Performance of Photochemical Synthesis of Thiazoles: An Integrated Theoretical and Experimental Study

Artificial intelligence (AI)/machine learning (ML) is emerging as pivotal in synthetic chemistry, offering revolutionary potential in retrosynthetic analysis, reaction conditions and reaction prediction. We have combined chemical descriptors, primarily based on Density Functional Theory (DFT) calculations, with various AI/ML tools such as Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict the synthesis of 2-arylbenzothiazole in photoredox reactions. Significantly, our models underscore the critical role of the molecular structure and physicochemical characteristics of the base, especially the total atomic polarizabilities, in the rate-determining steps involving cyclohexyl and phenethyl moieties of the substrate. Moreover, we validated our findings in articles through experimental studies. It showcases the power of AI/ML and quantum chemistry in shaping the future of organic chemistry.

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies for radiolabeling, the optimization of these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be a powerful approach for optimizing reactions in many areas of chemical synthesis. However, to date, HTE has rarely been applied to radiochemistry. This is largely because of the short lifetime of common radioisotopes, which presents major challenges for efficient parallel reaction setup and analysis using standard equipment and workflows. Herein, we demonstrate an effective HTE workflow and apply it to the optimization of copper-mediated radiofluorination of pharmaceutically relevant boronate ester substrates. The workflow utilizes commercial equipment and allows for rapid analysis of reactions for optimizing reactions, exploring chemical space using pharmaceutically relevant aryl boronates for radiofluorinations, and constructing large radiochemistry data sets.

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time-consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on-line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron-demand Diels-Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer-readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning.

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Enhancing Precatalyst Performance and Robustness through Aromaticity: Insights from Iridaheteroaromatics

Despite the inherent stability-enhancing benefits of dπ-pπ conjugation-induced aromaticity, metallaaromatic catalysts remain underutilized in this context, despite their reactivity with organic functionalities in stoichiometric reactions. We present a strategy for synthesizing a diverse range of iridaheteroaromatics, (L^L)IrIII(Cp*)I, including iridapyridylidene-indole, iridapyridene-indole, and iridaimidazole, via in situ deprotonation/metalation reactions utilizing [Cp*IrCl2]2 and the respective ligands. These catalysts exhibit enhanced σ-donor and π-acceptor properties, intrinsic σ-π continuum attributes, and versatile binding sites, contributing to stability through enhanced dπ-pπ conjugation-induced aromaticity. Spectroscopic data, X-ray crystallographic data, and density functional theory calculations confirm their aromaticity. These iridaheteroaromatics exhibit formidable catalytic ability across a spectrum of transformations under industrially viable conditions, notably excelling in highly selective cross alkylation and β-alkylation of alcohols and an eco-friendly avenue for quinolone synthesis, achieving remarkably high turnover frequencies (TOFs). Additionally, this method extends to the self-condensation of bioalcohols like ethanol, n-butanol, and n-hexanol in water, replicating conditions frequently encountered in primary fermentation solutions. These iridaheteroaromatics exhibit strong catalytic activity with fast reaction rates, high TOFs, broad substrate compatibility, and remarkable selectivity, displaying their potential as robust catalysts in large-scale applications and emphasizing their practical significance beyond their structural and theoretical importance.

Identifying general reaction conditions by bandit optimization

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production II: Model-based multiobjective optimization

The inherent complexity of coupled biocatalytic reactions presents a major challenge for process development with one-pot multienzyme cascade transformations. Kinetic models are powerful engineering tools to guide the optimization of cascade reactions towards a performance suitable for scale up to an actual production. Here, we report kinetic model-based window of operation analysis for cellobiose production (≥100 g/L) from sucrose and glucose by indirect transglycosylation via glucose 1-phosphate as intermediate. The two-step cascade transformation is catalyzed by sucrose and cellobiose phosphorylase in the presence of substoichiometric amounts of phosphate (≤27 mol% of substrate). Kinetic modeling was instrumental to uncover the hidden effect of bulk microviscosity due to high sugar concentrations on decreasing the rate of cellobiose phosphorylase specifically. The mechanistic-empirical hybrid model thus developed gives a comprehensive description of the cascade reaction at industrially relevant substrate conditions. Model simulations serve to unravel opposed relationships between efficient utilization of the enzymes and maximized concentration (or yield) of the product within a given process time, in dependence of the initial concentrations of substrate and phosphate used. Optimum balance of these competing key metrics of process performance is suggested from the model-calculated window of operation and is verified experimentally. The evidence shown highlights the important use of kinetic modeling for the characterization and optimization of cascade reactions in ways that appear to be inaccessible to purely data-driven approaches.

Ultrasound and sonochemistry enhance education outcomes: From fundamentals and applied research to entrepreneurial potential

With this manuscript we aim to initiate a discussion specific to educational actions around ultrasonics sonochemistry. The importance of these actions does not just derive from a mere pedagogical significance, but they can be an exceptional tool for illustrating various concepts in other disciplines, such as process intensification and microfluidics. Sonochemistry is currently a far-reaching discipline extending across different scales of applicability, from the fundamental physics of tiny bubbles and molecules, up to process plants. This review is part of a special issue in Ultrasonics Sonochemistry, where several scholars have shared their experiences and highlighted opportunities regarding ultrasound as an education tool. The main outcome of our work is that teaching and mentorship in sonochemistry are highly needed, with a balanced technical and scientific knowledge to foster skills and implement safe protocols. Applied research typically features the use of ultrasound as ancillary, to merely enhance a given process and often leading to poorly conceived experiments and misunderstanding of the actual effects. Thus, our scientific community must build a consistent culture and monitor reproducible practices to rigorously generate new knowledge on sonochemistry. These practices can be implemented in teaching sonochemistry in classrooms and research laboratories. We highlight ways to collectively provide a potentially better training for scientists, invigorating academic and industry-oriented careers. A salient benefit for education efforts is that sonochemistry-based projects can serve multidisciplinary training, potentially gathering students from different disciplines, such as physics, chemistry and bioengineering. Herein, we discuss challenges, opportunities, and future avenues to assist in designing courses and research programs based on sonochemistry. Additionally, we suggest simple experiments suitable for teaching basic physicochemical principles at the undergraduatelevel. We also provide arguments and recommendations oriented towards graduate and postdoctoral students, in academia or industry to be more entrepreneurial. We have identified that sonochemistry is consistently seen as a 'green' or sustainable tool, which particular appeal to process intensification approaches, including microfluidics and materials science. We conclude that a globally aligned pedagogical initiative and constantly updated educational tools will help to sustain a virtuous cycle in STEM and industrial applications of sonochemistry.

Automated self-optimization, intensification, and scale-up of photocatalysis in flow

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.

Copper-Mediated N-Trifluoromethylation of O-Benzoylhydroxylamines

The use of trifluoromethyl containing compounds is well established within medicinal chemistry, with a range of approved drugs containing C-CF3 and O-CF3 moieties. However, the utilisation of the N-CF3 functional group remains relatively unexplored. This may be attributed to the challenging synthesis of this unit, with many current methods employing harsh conditions or less accessible reagents. A robust methodology for the N-trifluoromethylation of secondary amines has been developed, which employs an umpolung strategy in the form of a copper-catalysed electrophilic amination. The method is operationally simple, uses mild, inexpensive reagents, and has been used to synthesise a range of novel, structurally complex N-CF3 containing compounds.

A dynamic knowledge graph approach to distributed self-driving laboratories

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Direct Heterocycle C-H Alkenylation via Dual Catalysis Using a Palladacycle Precatalyst: Multifactor Optimization and Scope Exploration Enabled by High-Throughput Experimentation

One of the major challenges in developing catalytic methods for C-C bond formation is the identification of generally applicable reaction conditions, particularly if multiple substrate structural classes are involved. Pd-catalyzed direct arylation reactions are powerful transformations that enable direct functionalization of C-H bonds; however, the corresponding direct alkenylation reactions, using vinyl (pseudo) halide electrophiles, are less well developed. Inspired by process development efforts toward GSK3368715, an investigational active pharmaceutical ingredient, we report that a Pd(II) palladacycle derived from tri-tert-butylphosphine and Pd(OAc)2 is an effective single-component precatalyst for a variety of direct alkenylation reactions. High-throughput experimentation identified optimal solvent/base combinations for a variety of HetAr-H substrate classes undergoing C-H activation without the need for cocatalysts or stoichiometric silver bases (e.g., Ag2CO3). We propose this reaction proceeds via a dual cooperative catalytic mechanism, where in situ-generated Pd(0) supports a canonical Pd(0)/(II) cross-coupling cycle and the palladacycle effects C-H activation via CMD in a redox-neutral cycle. In all, 192 substrate combinations were tested with a hit rate of approximately 40% and 24 isolated examples. Importantly, this method was applied to prepare a key intermediate in the synthesis of GSK3368715 on multigram scale.

Design of Experiment for Optimizing Microencapsulation by the Solvent Evaporation Technique

We employed microemulsion combined with the solvent evaporation technique to produce biodegradable polycaprolactone (PCL) MCs, containing encapsulated isophorone diisocyanate (IPDI), to act as crosslinkers in high-performance adhesive formulations. The MC production process was optimized by applying a design of experiment (DoE) statistical approach, aimed at decreasing the MCs' average size. For that, three different factors were considered, namely the concentration of two emulsifiers, polyvinyl alcohol (PVA) and gum arabic (GA); and the oil-to-water phase ratio of the emulsion. The significance of each factor was evaluated, and a predictive model was developed. We were able to decrease the average MC size from 326 μm to 70 µm, maintaining a high encapsulation yield of approximately 60% of the MCs' weight, and a very satisfactory shelf life. The MCs' average size optimization enabled us to obtain an improved distributive and dispersive mixture of isocyanate-loaded MCs at the adhesive bond. The MCs' suitability as crosslinkers for footwear adhesives was assessed following industry standards. Peel tests revealed peel strength values above the minimum required for casual footwear, while the creep test results indicated an effective crosslinking of the adhesive. These results confirm the ability of the MCs to release IPDI during the adhesion process and act as crosslinkers for new adhesive formulations.

Optimization of a 1,4,7-Triazacyclononane-1,4-diacetic acid (NODA) Derivative Radiofluorination by Al18 F Complexation Using a Design of Experiments Approach

Fluorine-18 (18 F) is the most favorable positron emitter for radiolabeling Positron Emission Tomography (PET) probes. However, conventional 18 F labeling through covalent C-F bond formation is challenging, involving multiple steps and stringent conditions unsuitable for sensitive biomolecular probes whose integrity may be altered. Over the past decade, an elegant new approach has been developed involving the coordination of an aluminum fluoride {Al18 F} species in aqueous media at a late-stage of the synthetic process. The objective of this study was to implement this method and to optimize radiolabeling efficiency using a Design of Experiments (DoE). To assess the impact of various experimental parameters on {Al18 F} incorporation, a pentadentate chelating agent NODA-MP-C4 was prepared as a model compound. This model carried a thiourea function present in the final conjugates resulting from the grafting of the chelating agent onto the probe. The formation of the radioactive complex Al18 F-NODA-MP-C4 was studied to achieve the highest radiochemical conversion. A complementary "cold" series study using the natural isotope 19 F was also conducted to guide the radiochemical operating conditions. Ultimately, Al18 F-NODA-MP-C4 was obtained with a reproducible and satisfactory radiochemical conversion of 79±3.5 % (n=5).

Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions

Miniaturised high-throughput experimentation (HTE) is widely employed in industrial and academic laboratories for rapid reaction optimisation using material-limited, multifactorial reaction condition screening. In fragment-based drug discovery (FBDD), common toolbox reactions such as the Suzuki-Miyaura and Buchwald-Hartwig cross couplings can be hampered by the fragment's intrinsic heteroatom-rich pharmacophore which is required for ligand-protein binding. At Astex, we are using microscale HTE to speed up reaction optimisation and prevent target down-prioritisation. By identifying catalyst/base/solvent combinations which tolerate unprotected heteroatoms we can rapidly optimise key cross-couplings and expedite route design by avoiding superfluous protecting group manipulations. However, HTE requires extensive upfront training, and this modern automated synthesis technique largely differs to the way organic chemists are traditionally trained. To make HTE accessible to all our synthetic chemists we have developed a semi-automated workflow enabled by pre-made 96-well screening kits, rapid analytical methods and in-house software development, which is empowering chemists at Astex to run HTE screens independently with minimal training.

In vitro analysis of iron oxide (Fe3O4) nanoparticle mediated degradation of polycyclic aromatic hydrocarbons (PAHs) and their antimicrobial activity

To degrade anthracene, magnetite nanoparticles were produced using a simple co-precipitation process. The fabricated nanoparticles have been analyzed for structural and optical properties. XRD examination revealed that the produced Fe3O4 nanoparticles were cubic phase, having a mean crystallite dimension of 18.84 nm. DLS determined the hydrodynamic diameter of Fe3O4 nanoparticles to be 182 nm. UV-Vis research revealed that Fe3O4 nanoparticles absorb at 390 nm. A peak at 895 cm-1 in the FT-IR study indicated the metal-oxygen connection. The synthesized Fe3O4 nanoparticles demonstrated an effective photocatalytic performance towards anthracene degradation and was found to be 86.55%. Furthermore, Fe3O4 nanoparticles showed the highest antimicrobial activity against Bacillus subtilis was 19.43 mm. The present study is the first and foremost study determining the dual role of Fe3O4 nanoparticles towards bioremediation and biomedical applications.

High-Throughput Liquid Chromatographic Analysis Using a Segmented Flow Injector with a 1 s Cycle Time

High-throughput screening (HTS) workflows are revolutionizing many fields, including drug discovery, reaction discovery and optimization, diagnostics, sensing, and enzyme engineering. Liquid chromatography (LC) is commonly deployed during HTS to reduce matrix effects, distinguish isomers, and preconcentrate prior to detection, but LC separation time often limits throughput. Although subsecond LC separations have been demonstrated, they are rarely utilized during HTS due to limitations associated with the speed of common autosamplers. In this work, these limits are overcome by utilizing droplet microfluidics for sample introduction. In the method, a train of samples segmented by air are continuously pumped into the inlet of an LC injection valve that is actuated once each sample fills the sample loop. Coupled with 2.1 mm diameter × 5 mm long columns packed with 2.7 μm superficially porous C18 particles operated at 5 mL/min, the injector enabled separation of 3 components at 1 s/sample and analysis of a 96-well plate in 1.6 min with <2% peak area relative standard deviation. Analyte-dependent carryover was minimized by including wash droplets composed of organic solvent in between sample droplets. High-throughput LC coupled with mass spectrometric detection using the segmented flow injector was applied to a screen of inhibitors of a cytochrome P450-catalyzed hydroxylation reaction. Measurements of the reaction substrate and product concentrations made using fast LC with the segmented flow injector correlated well with measurements made using a more conventional, 3 min LC method. These results demonstrate the potential for droplet microfluidics to be used for sample introduction during high-throughput LC analysis.

Quantum chemical calculations for reaction prediction in the development of synthetic methodologies

Quantum chemical calculations have been used in the development of synthetic methodologies to analyze the reaction mechanisms of the developed reactions. Their ability to estimate chemical reaction pathways, including transition state energies and connected equilibria, has led researchers to embrace their use in predicting unknown reactions. This perspective highlights strategies that leverage quantum chemical calculations for the prediction of reactions in the discovery of new methodologies. Selected examples demonstrate how computation has driven the development of unknown reactions, catalyst design, and the exploration of synthetic routes to complex molecules prior to often laborious, costly, and time-consuming experimental investigations.

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

Sequential analysis for identification of byproduct from N-benzylation reaction: wound healing and anti-inflammatory potential of the byproduct 4-chlorobenzyl 2-((4-chlorobenzyl)amino)benzoate

A very common reaction, N-benzylation of isatoic anhydride in the presence of sodium hydride base, produces byproducts. The yield of one of the byproducts was greater than that of the desired product; therefore, we identified the anonymous undisclosed structure of the byproduct using sequential spectroscopy methods and SC-XRD. This byproduct was found to be effective as a wound-healing and anti-inflammatory agent. The 10% formulation of byproduct and standard (nitrofurazone) showed complete wound closure with a large number of cell migrations within 16 days. Hydroxyproline contents of 5% and 10% formulations were found to be slightly increased as compared with that of the standard. The byproduct also had anti-inflammatory potential. It was effective in inhibiting COX-2, heat-induced albumin denaturation, and formalin-induced paw edema.

ResNet14Attention network for identifying the titration end-point of potassium dichromate

With the rapid development of industry, the increasing discharge of sewage causes the detection of water quality to be of increasing importance. Potassium dichromate titration is one of the most important testing methods in water quality detection; the ability to accurately identify the titration end-point of potassium dichromate is currently a research challenge. To identify titration end-point quickly and accurately, this study proposes a ResNet14Attention network, which utilizes residual modules that focus on original image information and an attention mechanism that focuses highly on classification targets. The proposed ResNet14Attention network is compared with 12 convolutional neural networks such as ResNet series networks, VGG, and GoogLeNet. The results of comparison experiments reveal that only the proposed ResNet14Attention network has the highest training and testing accuracy of 100% among all convolutional neural networks in the comparison experiment; the proposed ResNet14Attention network has the highest training speed compared to all the networks that over 90% accuracy.

Efficient degassing and ppm-level oxygen monitoring flow chemistry system

Low oxygen levels are critical for a long range of chemical transformations carried out in both flow and batch chemistry. Here, we present an inline continuous flow degassing system based on a gas-permeable membrane inside a vacuum chamber for achieving and monitoring ppm-level oxygen concentrations in solutions. The oxygen presence was monitored with a molecular oxygen probe and a continuously running UV-vis spectrometer. An automated setup for discovering optimal reaction conditions for minimal oxygen presence was devised. The parameters tested were: flow rate, vacuum pressure, solvent back-pressure, tube material, tube length and solvent oxygen solubility. The inline degassing system was proven to be effective in removing up to 99.9% of ambient oxygen from solvents at a flow rate of 300 μl min^-1 and 4 mbar vacuum pressure inside the degassing chamber. Reaching lower oxygen concentrations was limited by gas permeation in the tubing following the degassing unit, which could be addressed by purging large volume flow reactors with an inert gas after degassing or by using tubing with lower gas permeability, such as stainless steel tubing. Among all factors, oxygen solubility in solvents was found to play a significant role in achieving efficient degassing of solvents. The data presented here can be used to choose optimal experimental parameters for oxygen-sensitive reactions in flow chemistry reaction setups. The data were also fitted to an analytically derived model from simple differential equations in physical context of the experiment.

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.

Circularity in polymers: addressing performance and sustainability challenges using dynamic covalent chemistries

The circularity of current and future polymeric materials is a major focus of fundamental and applied research, as undesirable end-of-life outcomes and waste accumulation are global problems that impact our society. The recycling or repurposing of thermoplastics and thermosets is an attractive solution to these issues, yet both options are encumbered by poor property retention upon reuse, along with heterogeneities in common waste streams that limit property optimization. Dynamic covalent chemistry, when applied to polymeric materials, enables the targeted design of reversible bonds that can be tailored to specific reprocessing conditions to help address conventional recycling challenges. In this review, we highlight the key features of several dynamic covalent chemistries that can promote closed-loop recyclability and we discuss recent synthetic progress towards incorporating these chemistries into new polymers and existing commodity plastics. Next, we outline how dynamic covalent bonds and polymer network structure influence thermomechanical properties related to application and recyclability, with a focus on predictive physical models that describe network rearrangement. Finally, we examine the potential economic and environmental impacts of dynamic covalent polymeric materials in closed-loop processing using elements derived from techno-economic analysis and life-cycle assessment, including minimum selling prices and greenhouse gas emissions. Throughout each section, we discuss interdisciplinary obstacles that hinder the widespread adoption of dynamic polymers and present opportunities and new directions toward the realization of circularity in polymeric materials.