Microfluidic Squeezing Enables MHC Class I Antigen Presentation by Diverse Immune Cells to Elicit CD8+ T Cell Responses with Antitumor Activity.

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.

Machine-Learning-Guided Discovery of Electrochemical Reactions.

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.

Photochemical Synthesis of the Bioactive Fragment of Salbutamol and Derivatives in a Self-Optimizing Flow Chemistry Platform.

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.

Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach.

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.

Automated Chemical Reaction Extraction from Scientific Literature.

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.

In vitro and ex vivo strategies for intracellular delivery.

Intracellular delivery of materials has become a critical component of genome-editing approaches, ex vivo cell-based therapies, and a diversity of fundamental research applications. Limitations of current technologies motivate development of next-generation systems that can deliver a broad variety of cargo to diverse cell types. Here we review in vitro and ex vivo intracellular delivery approaches with a focus on mechanisms, challenges and opportunities. In particular, we emphasize membrane-disruption-based delivery methods and the transformative role of nanotechnology, microfluidics and laboratory-on-chip technology in advancing the field.

The Open Reaction Database.

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.

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries.

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.

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning.

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.

Intracellular Delivery by Membrane Disruption: Mechanisms, Strategies, and Concepts.

Intracellular delivery is a key step in biological research and has enabled decades of biomedical discoveries. It is also becoming increasingly important in industrial and medical applications ranging from biomanufacture to cell-based therapies. Here, we review techniques for membrane disruption-based intracellular delivery from 1911 until the present. These methods achieve rapid, direct, and universal delivery of almost any cargo molecule or material that can be dispersed in solution. We start by covering the motivations for intracellular delivery and the challenges associated with the different cargo types-small molecules, proteins/peptides, nucleic acids, synthetic nanomaterials, and large cargo. The review then presents a broad comparison of delivery strategies followed by an analysis of membrane disruption mechanisms and the biology of the cell response. We cover mechanical, electrical, thermal, optical, and chemical strategies of membrane disruption with a particular emphasis on their applications and challenges to implementation. Throughout, we highlight specific mechanisms of membrane disruption and suggest areas in need of further experimentation. We hope the concepts discussed in our review inspire scientists and engineers with further ideas to improve intracellular delivery.

Autonomous Discovery in the Chemical Sciences Part†II: Outlook.

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.

Autonomous Discovery in the Chemical Sciences Part†I: Progress.

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.

A Multifunctional Microfluidic Platform for High-Throughput Experimentation of Electroorganic 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.

Flow Toolkit for Measuring Gas Diffusivity in Liquids.

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.

Machine Learning in Computer-Aided Synthesis Planning.

Computer-aided synthesis planning (CASP) is focused on the goal of accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input and output a sorted list of detailed reaction schemes that each connect that target to purchasable starting materials via a series of chemically feasible reaction steps. Early work in this field relied on expert-crafted reaction rules and heuristics to describe possible retrosynthetic disconnections and selectivity rules but suffered from incompleteness, infeasible suggestions, and human bias. With the relatively recent availability of large reaction corpora (such as the United States Patent and Trademark Office (USPTO), Reaxys, and SciFinder databases), consisting of millions of tabulated reaction examples, it is now possible to construct and validate purely data-driven approaches to synthesis planning. As a result, synthesis planning has been opened to machine learning techniques, and the field is advancing rapidly. In this Account, we focus on two critical aspects of CASP and recent machine learning approaches to both challenges. First, we discuss the problem of retrosynthetic planning, which requires a recommender system to propose synthetic disconnections starting from a target molecule. We describe how the search strategy, necessary to overcome the exponential growth of the search space with increasing number of reaction steps, can be assisted through a learned synthetic complexity metric. We also describe how the recursive expansion can be performed by a straightforward nearest neighbor model that makes clever use of reaction data to generate high quality retrosynthetic disconnections. Second, we discuss the problem of anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan (i.e., reduce false positives) to increase the likelihood of experimental success. While we introduce this task in the context of reaction validation, its utility extends to the prediction of side products and impurities, among other applications. We describe neural network-based approaches that we and others have developed for this forward prediction task that can be trained on previously published experimental data. Machine learning and artificial intelligence have revolutionized a number of disciplines, not limited to image recognition, dictation, translation, content recommendation, advertising, and autonomous driving. While there is a rich history of using machine learning for structure-activity models in chemistry, it is only now that it is being successfully applied more broadly to organic synthesis and synthesis design. As reported in this Account, machine learning is rapidly transforming CASP, but there are several remaining challenges and opportunities, many pertaining to the availability and standardization of both data and evaluation metrics, which must be addressed by the community at large.

A fully automated flow-based approach for accelerated peptide synthesis.

Here we report a fully automated, flow-based approach to solid-phase polypeptide synthesis, with amide bond formation in 7 seconds and total synthesis times of 40 seconds per amino acid residue. Crude peptide purities and isolated yields were comparable to those for standard-batch solid-phase peptide synthesis. At full capacity, this approach can yield tens of thousands of individual 30-mer peptides per year.

Revealing the Formation Mechanism of Alloyed Pd-Ru Nanoparticles: A Conversion Measurement Approach Utilizing a Microflow Reactor.

The synthesis of alloyed nanoparticles has been studied extensively; however, the formation mechanisms involved remain unclear. Here, we reveal the detailed formation mechanism of alloyed nanoparticles in a Pd-Ru system, using a semibatch polyol method in which the simultaneous rapid reduction of both precursors was assumed to be the critical mechanism. We employed a microflow reactor to realize rapid heating and cooling. A significant difference in the reaction rate between the two precursors was observed. Pd was reduced within seconds, but the reduction of Ru was 2 orders of magnitude slower than that of Pd and was not as rapid as previously assumed. Further investigation of the semibatch method was performed to trace changes in the particle sizes and composition. Through quantitative and multilateral evidence, we concluded that the formation of low-crystallinity seeds, followed by solid-state diffusion, is the governing mechanism for the formation of alloyed Pd-Ru nanoparticles.

RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application.

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.

Continuous N-Hydroxyphthalimide (NHPI)-Mediated Electrochemical Aerobic Oxidation of Benzylic C-H Bonds.

Electroorganic chemistry has emerged as an environmentally benign tool for synthetic chemists to achieve efficient transformations that are challenging with traditional reagent-based methods. Continuous flow chemistry brings pharmaceutical industry numerous advantages, but implementing electroorganic synthesis in flow is challenging, especially for electroorganic reactions with coupled electrode reactions and slow chemical reactions. We present a continuous electrolysis system engineered for N-hydroxyphthalimide (NHPI) mediated electrochemical aerobic oxidation of benzylic C-H bonds. First, a cation-exchange membrane prevents the crossover of the NHPI anion from anolyte to catholyte avoiding reductive decomposition of NHPI at the cathode, and enables the usage of a cost-effective reticulated vitreous carbon (RVC) cathode instead of a platinum electrode. Second, running the electrochemical flow cell with recycle streams accommodates the inherently slow kinetics of the chemical reaction without phthalimide-N-oxyl (PINO) radical self-decomposition at the anode, and allows the usage of gaseous oxygen as co-oxidant.