Conspectus 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.
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 typessmall 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.
The pharmaceutical industry is investing in continuous flow and high-throughput experimentation as tools for rapid process development accelerated scale-up. Coupled with automation, these ...technologies offer the potential for comprehensive reaction characterization and optimization, but with the cost of conducting exhaustive multifactor screens. Automated feedback in flow offers researchers an alternative strategy for efficient characterization of reactions based on the use of continuous technology to control chemical reaction conditions and optimize in lieu of screening. Optimization with feedback allows experiments to be conducted where the most information can be gained from the chemistry, enabling product yields to be maximized and kinetic models to be generated while the total number of experiments is minimized. This Account opens by reviewing select examples of feedback optimization in flow and applications to chemical research. Systems in the literature are classified into (i) deterministic “black box” optimization systems that do not model the reaction system and are therefore limited in the utility of results for scale-up, (ii) deterministic model-based optimization systems from which reaction kinetics and/or mechanisms can be automatically evaluated, and (iii) stochastic systems. Though diverse in application, flow feedback systems have predominantly focused upon the optimization of continuous variables, i.e., variables such as time, temperature, and concentration that can be ramped from one experiment to the next. Unfortunately, this implies that the screening of discrete variables such as catalyst, ligand, or solvent generally does not factor into automated flow optimization, resulting in incomplete process knowledge. Herein, we present a system and strategy developed for optimizing discrete and continuous variables of a chemical reaction simultaneously. The approach couples automated feedback with high-throughput reaction screening in droplet flow microfluidics. This Account details the system configuration for on-demand creation of sub-20 μL droplets with interchangeable reagents and catalysts. These droplets are reacted in a fully automated microfluidic system and analyzed online by LC/MS. Feeding back from the online analytical results, a design of experiments (DoE)-based adaptive response surface algorithm is employed that deductively removes candidate reagents from the optimization as optimal reaction conditions are refined, leading to rapid convergence. Using the automated optimization platform, case studies are presented for solvent selection in a competitive alkylation chemistry and for catalyst-ligand selection in heteroaromatic Suzuki–Miyaura cross-coupling chemistries. For the monoalkylation of trans-1,2-diaminocyclohexane, polar aprotic solvents at moderate temperatures are shown to be favorable, with optimality accurately identified with dimethyl sulfoxide as the solvent in 67 experiments. For Suzuki–Miyaura cross-couplings, the optimality of precatalysts and continuous variable conditions are observed to change in accordance with the coupling reagents, providing insights into catalyst behavior in the context of the reaction mechanism. Future opportunities in automated reaction development include the incorporation of chemoinformatics for faster analysis and machine-learning algorithms to guide and optimize the synthesis. Adoption of this technology stands to reduce graduate student and postdoc time on routine tasks in the laboratory, while feeding back knowledge used to guide new research directions. Moreover, the application of this technology in industry promises to lessen the cost and time associated with advancing pharmaceutical molecules through development and scale-up.
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.
The contribution of computer assistance and automation to various aspects of discovery in the chemical sciences is discussed in this two‐part Review. This first part describes how discoveries of physical matter, processes, and models are unified as search problems; what to consider when assessing the extent of autonomy in a discovery; and many examples of discoveries in synthetic chemistry, drug discovery, inorganic chemistry, and materials science.
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.
Exploring the possibilities: This two‐part Review evaluates the contribution of computer assistance and automation to various aspects of discovery in the chemical sciences. This second part reflects on exemplary case studies introduced in the first part. A set of open research directions in terms of both practical and methodological challenges are defined; addressing these would advance the extent to which these systems can make meaningful discoveries.
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.
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to ...manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions
the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.
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