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The mechanism of Pd-catalyzed amination of five-membered heteroaryl halides was investigated by integrating experimental kinetic analysis with kinetic modeling through predictive testing and likelihood ratio analysis, revealing an atypical productive coupling pathway and multiple off-cycle events. The GPhos-supported Pd catalyst, along with the moderate-strength base NaOTMS, was previously found to promote efficient coupling between five-membered heteroaryl halides and secondary amines. However, slight deviations from the optimal concentration, temperature, and/or solvent resulted in significantly lower yields, contrary to typical reaction optimization trends. We found that the coupling of 4-bromothiazole with piperidine proceeds through an uncommon mechanism in which the NaOTMS base, rather than the amine, binds first to the oxidative addition complex; the resulting OTMS-bound Pd species is the resting state. Formation of the Pd-amido complex via base/amine exchange was identified as the turnover-limiting step, unlike other reported catalyst systems for which reductive elimination is turnover-limiting. We determined that the amine-bound Pd complex, usually an on-cycle intermediate, is instead a reversibly generated off-cycle species, and that base-mediated decomposition of 4-bromothiazole is the primary irreversible catalyst deactivation pathway. Predictive testing and kinetic modeling were key to the identification of these off-cycle processes, providing insight into minor mechanistic pathways that are difficult to observe experimentally. Collectively, this report reveals the unique enabling features of the Pd-GPhos/NaOTMS system, implementing mechanistic insights to improve the yields of particularly challenging coupling reactions. Moreover, these findings highlight the utility of applying predictive tests to kinetic models for the rapid evaluation of mechanistic possibilities in small-molecule catalytic systems.

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine learning and large language models to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models for reactivity prediction achieved high accuracy (>90 %) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were prompted to generate code that iteratively improved yields. This human-AI collaboration proved effective, efficiently identifying high-yield conditions for 8 drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 12 different LLMs-including LLaMA series, Claude series, OpenAI o1, and GPT-4-on code generation and function calling related to ML based on natural language prompts given by chemists to showcase potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions., (© 2024 The Author(s). Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

The US EPA's Toxicity Forecaster (ToxCast) is a suite of high-throughput in vitro assays to screen environmental toxicants and predict potential toxicity of uncharacterized chemicals. This work examines the relevance of ToxCast assay intended gene targets to putative molecular initiating events (MIEs) of neurotoxicants. This effort is needed as there is growing interest in the regulatory and scientific communities about developing new approach methodologies (NAMs) to screen large numbers of chemicals for neurotoxicity and developmental neurotoxicity. Assay gene function (GeneCards, NCBI-PUBMED) was used to categorize gene target neural relevance (1 = neural, 2 = neural development, 3 = general cellular process, 3 A = cellular process critical during neural development, 4 = unlikely significance). Of 481 unique gene targets, 80 = category 1 (16.6 %); 16 = category 2 (3.3 %); 303 = category 3 (63.0 %); 97 = category 3 A (20.2 %); 82 = category 4 (17.0 %). A representative list of neurotoxicants (548) was researched (ex. PUBMED, PubChem) for neurotoxicity associated MIEs/Key Events (KEs). MIEs were identified for 375 compounds, whereas only KEs for 173. ToxCast gene targets associated with MIEs were primarily neurotransmitter (ex. dopaminergic, GABA)receptors and ion channels (calcium, sodium, potassium). Conversely, numerous MIEs associated with neurotoxicity were absent. Oxidative stress (OS) mechanisms were 79.1 % of KEs. In summary, 40 % of ToxCast assay gene targets are relevant to neurotoxicity mechanisms. Additional receptor and ion channel subtypes and increased OS pathway coverage are identified for potential future assay inclusion to provide more complete coverage of neural and developmental neural targets in assessing neurotoxicity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

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., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Functionalization of lead compounds to create analogs is a challenging step in discovering new molecules with desired properties and it is conducted throughout the chemical industry, including pharmaceuticals and agrochemicals. The process can be time-consuming and expensive, requiring expert intuition and experience. To help address synthesis planning challenges in late-stage functionalization, we have developed a molecular similarity approach that proposes single-step functionalization reactions based on analogy to precedent reactions. The developed approach mimics reaction strategies and suggests co-reactants defined implicitly by a corpus of known reactions. Using ca. 348 k reactions from the patent literature as a knowledge base, the recorded products or close analogs are among the top 20 proposed products in 74% of ∼44 k test reactions. The combinatorial growth inherent in recursive applications of the tool allows the enumeration of chemical libraries surrounding a target compound of interest. Moreover, each step of the resulting library synthesis leverages common chemical transformations reported in the literature accessible to most chemists., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the platform to be used to study both thermal and photochemical reactions. We incorporate a Bayesian optimization algorithm into the control software to enable reaction optimization over both categorical and continuous variables. We demonstrate the capabilities of both the preliminary single-channel and parallelized versions of the platform using a series of model thermal and photochemical reactions. We conduct a series of reaction optimization campaigns and demonstrate rapid acquisition of the data necessary to determine reaction kinetics. The platform is flexible in terms of use case: it can be used either to investigate reaction kinetics or to perform reaction optimization over a wide range of chemical domains., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Chemoenzymatic synthesis methods use organic and enzyme chemistry to synthesize a desired small molecule. Complementing organic synthesis with enzyme-catalyzed selective transformations under mild conditions enables more sustainable and synthetically efficient chemical manufacturing. Here, we present a multistep retrosynthesis search algorithm to facilitate chemoenzymatic synthesis of pharmaceutical compounds, specialty chemicals, commodity chemicals, and monomers. First, we employ the synthesis planner ASKCOS to plan multistep syntheses starting from commercially available materials. Then, we identify transformations that can be catalyzed by enzymes using a small database of biocatalytic reaction rules previously curated for RetroBioCat, a computer-aided synthesis planning tool for biocatalytic cascades. Enzymatic suggestions captured by the approach include ones capable of reducing the number of synthetic steps. We successfully plan chemoenzymatic routes for active pharmaceutical ingredients or their intermediates ( e.g. , Sitagliptin, Rivastigmine, and Ephedrine), commodity chemicals ( e.g. , acrylamide and glycolic acid), and specialty chemicals ( e.g. , S -Metalochlor and Vanillin), in a retrospective fashion. In addition to recovering published routes, the algorithm proposes many sensible alternative pathways. Our approach provides a chemoenzymatic synthesis planning strategy by identifying synthetic transformations that could be candidates for enzyme catalysis., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The presence of solids as starting reagents/reactants or products in flow photochemical reactions can lead to reactor clogging and yield reduction from side reactions. We address this limitation with a new ultrasonic microreactor for continuous solid-laden photochemical reactions. The ultrasonic photochemical microreactor is characterized by the liquid and solid residence time distribution (RTD) and the absorbed photon flux in the reactor via chemical actinometry. The solid-handling capability of the ultrasonic photochemical microreactor is demonstrated with a silyl radical-mediated metallaphotoredox cross-electrophile coupling with a solid base as a reagent., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

For many experimentally measured chemical properties that cannot be directly computed from first-principles, the existing physics-based models do not extrapolate well to out-of-sample molecules, and experimental datasets themselves are too small for traditional machine learning (ML) approaches. To overcome these limitations, we apply a transfer learning approach, whereby we simultaneously train a multi-target regression model on a small number of molecules with experimentally measured values and a large number of molecules with related computed properties. We demonstrate this methodology on predicting the experimentally measured impact sensitivity of energetic crystals, finding that both characteristics of the computed dataset and model architecture are important to prediction accuracy of the small experimental dataset. Our directed-message passing neural network (D-MPNN) ML model using transfer learning outperforms direct-ML and physics-based models on a diverse test set, and the new methods described here are widely applicable to modeling many other structure-property relationships.

The implementation of self-optimizing flow reactors has been mostly limited to model reactions or known synthesis routes. In this work, a self-optimizing flow photochemistry platform is used to develop an original synthesis of the bioactive fragment of Salbutamol and derivatives. The key photochemical steps for the construction of the aryl vicinyl amino alcohol moiety consist of a C-C bond forming reaction followed by an unprecedented, high yielding (>80 %), benzylic oxidative cyclization., (© 2022 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)

Automation and microfluidic tools potentially enable efficient, fast, and focused reaction development of complex chemistries, while minimizing resource- and material consumption. The introduction of automation-assisted workflows will contribute to the more sustainable development and scale-up of new and improved catalytic technologies. Herein, the application of automation and microfluidics to the development of a complex asymmetric hydrogenation reaction is described. Screening and optimization experiments were performed using an automated microfluidic platform, which enabled a drastic reduction in the material consumption compared to conventional laboratory practices. A suitable catalytic system was identified from a library of Ru II -diamino precatalysts. In situ precatalyst activation was studied with 1 H/ 31 P nuclear magnetic resonance (NMR), and the reaction was scaled up to multigram quantities in a batch autoclave. These reactions were monitored using an automated liquid-phase sampling system. Ultimately, in less than a week of total experimental time, multigram quantities of the target enantiopure alcohol product were provided by this automation-assisted approach., (© 2022 The Authors. ChemSusChem published by Wiley-VCH GmbH.)

Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the synthesis of organic compounds, but the limited availability of context-specific data currently necessitates experimental development to fully specify process details. We plan and optimize a CASP-proposed and human-refined multistep synthesis route toward an exemplary small molecule, sonidegib, on a modular, robotic flow synthesis platform with integrated process analytical technology (PAT) for data-rich experimentation. Human insights address catalyst deactivation and improve yield by strategic choices of order of addition. Multi-objective Bayesian optimization identifies optimal values for categorical and continuous process variables in the multistep route involving 3 reactions (including heterogeneous hydrogenation) and 1 separation. The platform's modularity, robotic reconfigurability, and flexibility for convergent synthesis are shown to be essential for allowing variation of downstream residence time in multistep flow processes and controlling the order of addition to minimize undesired reactivity. Overall, the work demonstrates how automation, machine learning, and robotics enhance manual experimentation through assistance with idea generation, experimental design, execution, and optimization., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Background: Information and knowledge about cost of illness and labour productivity in patients with HER2-positive early-stage and metastatic breast cancer treated with trastuzumab is limited. The aim of this study was to estimate the direct and indirect costs associated with treatment of HER2-positive breast cancer among patients with early-stage and metastatic breast cancer, treated with trastuzumab, in a 10-year period after diagnosis., Materials and Methods: This study included all Danish HER2-positive breast cancer patients (≥ 18 years) treated with trastuzumab between 2005 and 2016 identified in The Danish Patient Register and the Danish Cancer Register. Furthermore, we identified patients experiencing metastatic or recurrent breast cancer. For the study populations, we estimated total direct costs and indirect costs for one year prior to the breast cancer diagnosis and up to 10 years after diagnosis compared with a group of matched controls free of breast cancer. In addition to The Danish Patient Register and Cancer Register, we applied patient level data from The Civil Registration System, The National Pathology Register, National Health Service Register for Primary Care, Register of Medicinal Product Statistics, Register of Municipal Services, The DREAM database, and Population's Education Register., Results: We identified 4,153 HER2-positive breast cancer patients, whereof 27% were identified with metastatic or recurrent breast cancer. During the follow-up period of 10 years, we estimated excess direct costs of EUR 115,000 among the total study population compared to controls; EUR 211,000 among patients with metastases or recurrence; and EUR 89,000 among patients without metastases or recurrence. Direct costs were found to be highest in the first year after diagnosis and also peaked in the year after recurrence. Labour productivity was significantly lower among patients with recurrence 10 years after breast cancer diagnosis compared with controls., Conclusions: In this study, we estimated the direct and indirect cost associated with HER2-positive breast cancer. The costs were significantly higher during the 10 years after diagnosis compared to the control group, specifically among patients experiencing metastases or recurrence of breast cancer., (© 2022. The Author(s).)

Access to structured chemical reaction data is of key importance for chemists in performing bench experiments and in modern applications like computer-aided drug design. Existing reaction databases are generally populated by human curators through manual abstraction from published literature (e.g., patents and journals), which is time consuming and labor intensive, especially with the exponential growth of chemical literature in recent years. In this study, we focus on developing automated methods for extracting reactions from chemical literature. We consider journal publications as the target source of information, which are more comprehensive and better represent the latest developments in chemistry compared to patents; however, they are less formulaic in their descriptions of reactions. To implement the reaction extraction system, we first devised a chemical reaction schema, primarily including a central product , and a set of associated reaction roles such as reactants , catalyst , solvent , and so on. We formulate the task as a structure prediction problem and solve it with a two-stage deep learning framework consisting of product extraction and reaction role labeling . Both models are built upon Transformer-based encoders, which are adaptively pretrained using domain and task-relevant unlabeled data. Our models are shown to be both effective and data efficient, achieving an F1 score of 76.2% in product extraction and 78.7% in role extraction, with only hundreds of annotated reactions.

Enzymes synthesize complex natural products effortlessly by catalyzing chemo-, regio-, and enantio-selective transformations. Further, biocatalytic processes are increasingly replacing conventional organic synthesis steps because they use mild solvents, avoid the use of metals, and reduce overall non-biodegradable waste. Here, we present a single-step retrosynthesis search algorithm to facilitate enzymatic synthesis of natural product analogs. First, we develop a tool, RDEnzyme, capable of extracting and applying stereochemically consistent enzymatic reaction templates, i.e. , subgraph patterns that describe the changes in connectivity between a product molecule and its corresponding reactant(s). Using RDEnzyme, we demonstrate that molecular similarity is an effective metric to propose retrosynthetic disconnections based on analogy to precedent enzymatic reactions in UniProt/RHEA. Using ∼5500 reactions from RHEA as a knowledge base, the recorded reactants to the product are among the top 10 proposed suggestions in 71% of ∼700 test reactions. Second, we trained a statistical model capable of discriminating between reaction pairs belonging to homologous enzymes and evolutionarily distant enzymes using ∼30 000 reaction pairs from SwissProt as a knowledge base. This model is capable of understanding patterns in enzyme promiscuity to evaluate the likelihood of experimental evolution success. By recursively applying the similarity-based single-step retrosynthesis and evolution prediction workflow, we successfully plan the enzymatic synthesis routes for both active pharmaceutical ingredients ( e.g. Islatravir, Molnupiravir) and commodity chemicals ( e.g. 1,4-butanediol, branched-chain higher alcohols/biofuels), in a retrospective fashion. Through the development and demonstration of the single-step enzymatic retrosynthesis strategy using natural transformations, our approach provides a first step towards solving the challenging problem of incorporating both enzyme- and organic-chemistry based transformations into a computer aided synthesis planning workflow., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

CD8 + T cell responses are the foundation of the recent clinical success of immunotherapy in oncologic indications. Although checkpoint inhibitors have enhanced the activity of existing CD8 + T cell responses, therapeutic approaches to generate Ag-specific CD8 + T cell responses have had limited success. Here, we demonstrate that cytosolic delivery of Ag through microfluidic squeezing enables MHC class I presentation to CD8 + T cells by diverse cell types. In murine dendritic cells (DCs), squeezed DCs were ∼1000-fold more potent at eliciting CD8 + T cell responses than DCs cross-presenting the same amount of protein Ag. The approach also enabled engineering of less conventional APCs, such as T cells, for effective priming of CD8 + T cells in vitro and in vivo. Mixtures of immune cells, such as murine splenocytes, also elicited CD8 + T cell responses in vivo when squeezed with Ag. We demonstrate that squeezing enables effective MHC class I presentation by human DCs, T cells, B cells, and PBMCs and that, in clinical scale formats, the system can squeeze up to 2 billion cells per minute. Using the human papillomavirus 16 (HPV16) murine model, TC-1, we demonstrate that squeezed B cells, T cells, and unfractionated splenocytes elicit antitumor immunity and correlate with an influx of HPV-specific CD8 + T cells such that >80% of CD8s in the tumor were HPV specific. Together, these findings demonstrate the potential of cytosolic Ag delivery to drive robust CD8 + T cell responses and illustrate the potential for an autologous cell-based vaccine with minimal turnaround time for patients., (Copyright © 2022 by The American Association of Immunologists, Inc.)

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. The data, schema, supporting code, and web-based user interfaces are all publicly available on GitHub. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

The synthesis of thousands of candidate compounds in drug discovery and development offers opportunities for computer-aided synthesis planning to simplify the synthesis of molecule libraries by leveraging common starting materials and reaction conditions. We develop an optimization-based method to analyze large organic chemical reaction networks and design overlapping synthesis plans for entire molecule libraries so as to minimize the overall number of unique chemical compounds needed as either starting materials or reaction conditions. We consider multiple objectives, including the number of starting materials, the number of catalysts/solvents/reagents, and the likelihood of success of the overall syntheses plan, to select an optimal reaction network to access the target molecules. The library synthesis planning task was formulated as a network flow optimization problem, and we design an efficient decomposition scheme that reduces solution time by a factor of 5 and scales to instance with 48 target molecules and nearly 8000 intermediate reactions within hours. In four case studies of pharmaceutical compounds, the approach reduces the number of starting materials and catalysts/solvents/reagents needed by 32.2 and 66.0% on average and up to 63.2 and 80.0% in the best cases. The code implementation can be found at https://github.com/Coughy1991/Molecule_library_synthesis.

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C-H functionalization, aromatic C-X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of a holistic pathway evaluation mechanism makes it challenging to systematically prioritize strategic pathways except for using some simple heuristics. Herein, we introduce a data-driven approach to evaluate the relative strategic levels of retrosynthesis pathways using a dynamic tree-structured long short-term memory (tree-LSTM) model. We first curated a retrosynthesis pathway database, containing 238k patent-extracted pathways along with ∼55 M artificial pathways generated from an open-source CASP program, ASKCOS. The tree-LSTM model was trained to differentiate patent-extracted and artificial pathways with the same target molecule in order to learn the strategic relationship among single-step reactions within the patent-extracted pathways. The model achieved a top-1 ranking accuracy of 79.1% to recognize patent-extracted pathways. In addition, the trained tree-LSTM model learned to encode pathway-level information into a representative latent vector, which can facilitate clustering similar pathways to help illustrate strategically diverse pathways generated from CASP programs., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Electroorganic synthesis is a promising tool to design sustainable transformations and discover new reactivities. However, the added setup complexity caused by electrodes in the system impedes efficient screening of reaction conditions. Herein, we present a microfluidic platform that enables automated high-throughput experimentation (HTE) for electroorganic synthesis at a 15-microliter scale. Two HTE modules are demonstrated: 1) the rapid electrochemical reaction condition screening for a radical-radical cross-coupling reaction on micro-fabricated interdigitated electrodes, and 2) measurements of kinetics for mediated anodic oxidations using the microliter-scale cyclic voltammetry. The presented modular approach could be deployed for a range of other electroorganic chemistry applications beyond the demonstrated functionalities., (© 2020 Wiley-VCH GmbH.)

Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google's Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry considerations in computer aided synthesis planning with potential applications in process development for fine chemicals or pharmaceuticals., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high-accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access data sets of organic reactions with explicitly calculated template applicability and pretraining a template-relevance neural network on this augmented applicability data set, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small data set of well-curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating that these strategies can be very useful for small data sets.

Electrochemistry offers opportunities to promote single-electron transfer (SET) redox-neutral chemistries similar to those recently discovered using visible-light photocatalysis but without the use of an expensive photocatalyst. Herein, we introduce a microfluidic redox-neutral electrochemistry (μRN-eChem) platform that has broad applicability to SET chemistry, including radical-radical cross-coupling, Minisci-type reactions, and nickel-catalyzed C(sp 2 )-O cross-coupling. The cathode and anode simultaneously generate the corresponding reactive intermediates, and selective transformation is facilitated by the rapid molecular diffusion across a microfluidic channel that outpaces the decomposition of the intermediates. μRN-eChem was shown to enable a two-step gram-scale electrosynthesis of a nematic liquid crystal compound, demonstrating its practicality., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The rational design of semiconductor nanocrystal populations requires control of their emission linewidths, which are dictated by interparticle inhomogeneities and single-nanocrystal spectral linewidths. To date, research efforts have concentrated on minimizing the ensemble emission linewidths, however there is little knowledge about the synthetic parameters dictating single-nanocrystal linewidths. In this direction, we present a flow-based system coupled with an optical interferometry setup for the extraction of single nanocrystal properties. The platform has the ability to synthesize nanocrystals at high temperature <300 °C, adjust the particle concentration after synthesis and extract ensemble-averaged single nanocrystal emission linewidths using flow photon-correlation Fourier spectroscopy.

The controlled and continuous production of nanoparticles (NPs) with functionalized surfaces remains a technological challenge. We present a multistage synthetic platform, consisting of 3D-printed miniature continuous stirred-tank reactor (CSTR) cascades, for the continuous synthesis and functionalization of SiO 2 NPs. The use of the CSTR platform provides ideal and rapid mixing of precursor solutions, precise injection of additional reagents for multistep reactions, and facile operation when using viscous solutions and handling of syntheses with longer reaction times. To exemplify the use of such custom-designed CSTR cascades, amine- and carbohydrate-functionalized SiO 2 NPs are chosen as model reaction systems. In particular, the intensified flow reactor units allowed for the reproducible formation of SiO 2 NPs with diameters less than 100 nm and narrow size distributions (3-8%). Most importantly, by assembling various 3D-printed CSTR cascades, we synthesized gluconolactone-capped polyethylenimine-modified silica NPs in a fully continuous manner. The inherent control over NP surface charge, reactor scalability, and the significant shortening of processing times (less than 10 min) compared to batch methodologies (several days) strongly indicate the ability of the reactor technology to accelerate continuous nanomanufacturing. In general, it provides a simple route for the reproducible preparation of functionalized NPs, thus expanding the gamut of flow reactors for material synthesis.

Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular "simplified molecular-input line-entry system" (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community., Competing Interests: The authors declare no competing financial interest., (Copyright © 2019 American Chemical Society.)

Intensive research of hybrid metal-halide perovskite materials for use as photoactive materials has resulted in an unmatched increase in the power conversion efficiency of perovskite photovoltaics (PVs) over the last couple of years. Now that lab-fabricated perovskite devices rival the efficiency of silicon PVs, the next challenge of scalable mass manufacturing of large perovskite PV panels remains to be solved. For that purpose, it is still unclear which manufacturing method will provide the lowest processing cost and highest quality solar cells. Vapor deposition has been proven to work well for perovskites as a controllable and repeatable thin-film deposition technique but with processing speeds currently too slow to adequately lower the production costs. Addressing this challenge, in the present work, we demonstrate a high-speed vapor transport processing technique in a custom-built reactor that produces high-quality perovskite films with unprecedented deposition speed exceeding 1 nm/s, over 10× faster than previous vapor deposition demonstrations. We show that the semiconducting perovskite films produced with this method have excellent crystallinity and optoelectronic properties with 10 ns charge carrier lifetime, enabling us to fabricate the first photovoltaic devices made by perovskite vapor transport deposition. Our experiments are guided by computational fluid dynamics simulations that also predict that this technique could lead to deposition rates on the order of micrometers per second. This, in turn, could enable cost-effective scalable manufacturing of the perovskite-based solar technologies.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

There is a renewed interest in computer-aided synthesis planning, where the vast majority of approaches require the application of retrosynthetic reaction templates. Here we introduce RDChiral, an open-source Python wrapper for RDKit designed to provide consistent handling of stereochemical information in applying retrosynthetic transformations encoded as SMARTS strings. RDChiral is designed to enforce the introduction, destruction, retention, and inversion of chiral tetrahedral centers as well as the cis/trans configuration of double bonds. We also introduce an open-source implementation of a retrosynthetic template extraction algorithm to generate SMARTS patterns from atom-mapped reaction SMILES strings. In this application note, we describe the implementation of these two pieces of code and illustrate their use through many examples.

Precise knowledge of gas diffusivity in liquids is critical for describing complex multiphase reaction systems. Here we present a high-throughput flow concept to measure gas diffusivity in liquids. This strategy takes advantage of the tube-in-tube reactor design whereby semipermeable Teflon AF-2400 tubes facilitate fast mass transfer between gas and liquid without directly contacting the two fluids. Coupled pseudosteady-state flux balances over the gas and liquid describe the gas dissolution rate and corresponding diffusivity with the aid of a single gas flow meter and a continuously ramped liquid flow rate. This in situ method demonstrates excellent accuracy in diffusion coefficient measurements, with less than 5% deviation from established techniques.

Functional amyloid (FA) proteins have evolved to assemble into fibrils with a characteristic cross-β structure, which stabilizes biofilms and contributes to bacterial virulence. Some of the most studied bacterial FAs are the curli protein CsgA, expressed in a wide range of bacteria, and FapC, produced mainly by members of the Pseudomonas genus. Though unrelated, both CsgA and FapC contain imperfect repeats believed to drive the formation of amyloid fibrils. While much is known about CsgA biogenesis and fibrillation, the mechanism of FapC fibrillation remains less explored. Here, we show that removing the three imperfect repeats of FapC (FapC ΔR1R2R3) slows down the fibrillation but does not prevent it. The increased lag phase seen for FapC ΔR1R2R3 allows for disulfide bond formation, which further delays fibrillation. Remarkably, these disulfide-bonded species of FapC ΔR1R2R3 also significantly delay the fibrillation of human α-synuclein, a key protein in Parkinson's disease pathology. This attenuation of α-synuclein fibrillation was not seen for the reduced form of FapC ΔR1R2R3. The results presented here shed light on the FapC fibrillation mechanism and emphasize how unrelated fibrillation systems may share such common fibril formation mechanisms, allowing inhibitors of one fibrillating protein to affect a completely different protein., Competing Interests: The authors declare no competing financial interest.