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.

Computational prediction of complex cationic rearrangement outcomes

Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule's carbon skeleton8-12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types1-7 but will help rationalize and discover new, mechanistically complex transformations.

Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis

Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets-but supplemented by informative structural-characterization data and coupled with closed-loop experimentation-can yield materials of outstanding performance. The model we develop not only reproduces several non-obvious and actively studied experimental trends but also identifies a composition of a perovskite oxide electrocatalyst exhibiting an intrinsic overpotential at 10 mA cm-2oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides.

AlphaFold2 Can Predict Single-Mutation Effects

AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that the localized structural deformation between protein pairs differing by only 1-3 mutations-as measured by the effective strain-is correlated across 3901 experimental and AF-predicted structures. Furthermore, analysis of ∼11 000 proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the range and magnitude of single-mutation effects on average, and we propose a method to improve precision of AF predictions and to indicate when predictions are unreliable.

Solid-body trajectoids shaped to roll along desired pathways

In everyday life, rolling motion is typically associated with cylindrical (for example, car wheels) or spherical (for example, billiard balls) bodies tracing linear paths. However, mathematicians have, for decades, been interested in more exotically shaped solids such as the famous oloids1, sphericons2, polycons3, platonicons4 and two-circle rollers5 that roll downhill in curvilinear paths (in contrast to cylinders or spheres) yet indefinitely (in contrast to cones, Supplementary Video 1). The trajectories traced by such bodies have been studied in detail6-9, and can be useful in the context of efficient mixing10,11 and robotics, for example, in magnetically actuated, millimetre-sized sphericon-shaped robots12,13, or larger sphericon- and oloid-shaped robots translocating by shifting their centre of mass14,15. However, the rolling paths of these shapes are all sinusoid-like and their diversity ends there. Accordingly, we were intrigued whether a more general problem is solvable: given an infinite periodic trajectory, find the shape that would trace this trajectory when rolling down a slope. Here, we develop an algorithm to design such bodies-which we call 'trajectoids'-and then validate these designs experimentally by three-dimensionally printing the computed shapes and tracking their rolling paths, including those that close onto themselves such that the body's centre of mass moves intermittently uphill (Supplementary Video 2). Our study is motivated largely by fundamental curiosity, but the existence of trajectoids for most paths has unexpected implications for quantum and classical optics, as the dynamics of qubits, spins and light polarization can be exactly mapped to trajectoids and their paths16.

On-nanoparticle monolayers as a solute-specific, solvent-like phase

In addition to modifying surface properties, self-assembled monolayers, SAMs, on nanoparticles can selectively incorporate small molecules from the surrounding solution. This selectivity has been used in the design of substrate-specific catalytic systems but its degree has not been quantified. This work uses catalytic centers embedded in on-nanoparticle hydrophobic SAMs to monitor and quantify the partitioning of molecules between the bulk solvent and these monolayers. A combination of experiments and theory allows us to relate the logarithm of the incorporation-into-SAM constant to the "bulk" log P values, characterizing the incoming substrates. These results are in line with classic, semi-empirical linear free energy relationships between partitioning solvent systems; in this way, they substantiate the view of nanoscopic on-particle SAMs acting akin to a bulk solvent phase.

One-Pot, Three-Phase Recycling of Metals from Li-Ion Batteries in Rotating, Concentric-Liquid Reactors

Efficient recycling of spent lithium-ion batteries (LIBs) is essential for making their numerous applications sustainable. Hydrometallurgy-based separation methods are an indispensable part of the recycling process but remain limited by the extraction efficiency and selectivity, and typically require numerous binary liquid-liquid extraction steps in which the capacity of the extracting organic phase or partition coefficient of extracted metals become an overall bottleneck. Herein, rotating reactors are described, in which the aqueous feed, organic extractant, and aqueous acceptor phases are all present in the same rotating vessel and can be vigorously stirred and emulsified without the coalescence of aqueous layers. In this arrangement, the extractant molecules are not equilibrated with the feed and, instead, "shuttle" between the feed/extractant and the extractant/acceptor interfaces multiple times, with each such molecule ultimately transferring approximately ten metal ions. This shuttling allows for using extractant concentrations much lower than in previous designs even for extremely concentrated feeds and, simultaneously, ensures unprecedented speed and selectivity of the one-pot processes. These experimental results are accompanied by theoretical considerations of the selectivity versus speed trends as well as discussion of parameters essential for system upscaling.

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

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

Proving Cooperativity of a Catalytic Reaction by Means of Nanoscale Geometry: The Case of Click Reaction

Establishing whether a reaction is catalyzed by a single-metal catalytic center or cooperatively by a fleeting complex encompassing two such centers may be an arduous pursuit requiring detailed kinetic, isotopic, and other types of studies─as illustrated, for instance, by over a decade-long work on single-copper versus di-copper mechanisms of the popular "click" reaction. This paper describes a method to interrogate such cooperative mechanisms by a nanoparticle-based platform in which the probabilities of catalytic units being proximal can be varied systematically and, more importantly, independently of their volume concentration. The method relies on geometrical considerations rather than a detailed knowledge of kinetic equations, yet the scaling trends it yield can distinguish between cooperative and non-cooperative mechanisms.

Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki–Miyaura coupling with heterocyclic building blocks—and a carefully selected database of >10,000 literature examples—we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of negative data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training.

Is Organic Chemistry Really Growing Exponentially?

In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well-known methods. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction-type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer-assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases.

An Electrocatalytic Reaction As a Basis for Chemical Computing in Water Droplets

Aqueous droplets covered with amphiphilic Janus Au/Fe₃O₄ nanoparticles and suspended in an organic phase serve as building blocks of droplet-based electronic circuitry. The electrocatalytic activity of these nanoparticles in a hydrogen evolution reaction (HER) underlies the droplet's ability to rectify currents with typical rectification ratios of ∼10. In effect, individual droplets act as low-frequency half-wave rectifiers, whereas several appropriately wired droplets enable full-wave rectification. When the HER-supporting droplets are combined with salt-containing "resistor" ones, the resulting ensembles can act as AND or OR gates or as inverters.

Stimuli-responsive granular crystals assembled by dipolar and multipolar interactions

This work describes granular crystals held together by unusual, multipolar interactions and, under the application of an external bias, undergoing reversible structural transitions between closed and open forms. The system comprises two types of polymeric beads agitated on one or between two conductive plates and gradually acquiring charges by contact electrification. The charges thus developed induce a series of electrostatic images in the conductive supports and, in effect, the beads interact via dipolar or multipolar interactions, enabling the stabilization of non-electroneutral crystals. Furthermore, under an applied bias, the beads become polarized and their complex interactions (due to the series of image charges as well as series of image dipoles) result in open-pore crystals which return to compact forms upon bias removal. These effects are rationalized by analytical calculations, and the crystal structures observed in the experiments are reproduced by molecular dynamics simulations.

Self-Assembling Films of Covalent Organic Frameworks Enable Long-Term, Efficient Cycling of Zinc-Ion Batteries

Despite their safety, nontoxicity, and cost-effectiveness, zinc aqueous batteries still suffer from limited rechargeability and poor cycle life, largely due to spontaneous surface corrosion and formation of large Zn dendrites by irregular and uneven plating and stripping. In this work, these untoward effects are minimized by covering Zn electrodes with ultrathin layers of covalent organic frameworks, COFs. These nanoporous and mechanically flexible films form by self-assembly-via the straightforward and scalable dip-coating technique-and permit efficient mass and charge transport while suppressing surface corrosion and growth of large Zn dendrites. The batteries demonstrated have excellent capacity retention and stable polarization voltage for over 420 h of cycling at 1 mA cm^-2 . The COF films essential for these improvements can be readily deposited over large areas and curvilinear supports, enabling, for example, foldable wire-type batteries.

Mixed-Charge Nanocarriers Allow for Selective Targeting of Mitochondria by Otherwise Nonselective Dyes

Targeted delivery of molecular cargos to specific organelles is of paramount importance for developing precise and effective therapeutics and imaging probes. This work describes a disulfide-based delivery method in which mixed-charged nanoparticles traveling through the endolysosomal tract deliver noncovalently bound dye molecules selectively into mitochondria. This system comprises three elements: (1) The nanoparticles deliver their payloads by a kiss-and-go mechanism - that is, they drop off their dye cargos proximate to mitochondria but do not localize therein; (2) the dye molecules are by themselves nonspecific to any cellular structures but become so with the help of mixed-charge nanocarriers; and (3) the dye is engineered in such a way as to remain in mitochondria for a long time, up to days, allowing for observing dynamic remodeling of mitochondrial networks and long-term tracking of mitochondria even in dividing cells. The selectivity of delivery and long-lasting staining derive from the ability to engineer charge-imbalanced, mixed [+/-] on-particle monolayers and from the structural features of the cargo. Regarding the former, the balance of [+] and [-] ligands can be adjusted to limit cytotoxicity and control the number of dye molecules adsorbed onto the particles' surfaces. Regarding the latter, comparative studies with multiple dye derivatives we synthesized rationalize the importance of polar groups, long alkyl chains, and disulfide moieties in the assembly of fluorescent nanoconstructs and long-lasting staining of mitochondria. Overall, this strategy could be useful for delivering hydrophilic and/or anionic small-molecule drugs difficult to target to mitochondria by classical approaches.

Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions

This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.

Chemist Ex Machina: Advanced Synthesis Planning by Computers

Teaching computers to plan multistep syntheses of arbitrary target molecules-including natural products-has been one of the oldest challenges in chemistry, dating back to the 1960s. This Account recapitulates two decades of our group's work on the software platform called Chematica, which very recently achieved this long-sought objective and has been shown capable of planning synthetic routes to complex natural products, several of which were validated in the laboratory.For the machine to plan syntheses at an expert level, it must know the rules describing chemical reactions and use these rules to expand and search the networks of synthetic options. The rules must be of high quality: They must delineate accurately the scope of admissible substituents, capture all relevant stereochemical information, detect potential reactivity conflicts, and protection requirements. They should yield only those synthons that are chemically stable and energetically allowed (e.g., not too strained) and should be able to extrapolate beyond examples already published in the literature. In parallel, the network-search algorithms must be able to assign meaningful scores to the sets of synthons they encounter, make judicious choices which of the network's branches to expand, and when to withdraw from unpromising ones. They must be able to strategize over multiple steps to resolve intermittent reactivity conflicts, exchange functional groups, or overcome local maxima of molecular complexity.Meeting all these requirements makes the problem of computer-driven retrosynthesis very multifaceted, combining expert and AI approaches further supplemented by quantum-mechanical and molecular-mechanics calculations. Development of Chematica has been a very long and gradual process because all these components are needed. Any shortcuts-for example, reliance on only expert or only data-based approaches-yield chemically naïve and often erroneous syntheses, especially for complex targets. On the bright side, once all the requisite algorithms are implemented-as they now are-they not only streamline conventional synthetic planning but also enable completely new modalities that would challenge any human chemist, for example, synthesis with multiple constraints imposed simultaneously or library-wide syntheses in which the machine constructs "global plans" leading to multiple targets and benefiting from the use of common intermediates. These types of analyses will have profound impact on the practice of chemical industry, designing more economical, more green, and less hazardous pathways.

Concentric liquid reactors for chemical synthesis and separation

Recent years have witnessed increased interest in systems that are capable of supporting multistep chemical processes without the need for manual handling of intermediates. These systems have been based either on collections of batch reactors¹ or on flow-chemistry designs^2-4, both of which require considerable engineering effort to set up and control. Here we develop an out-of-equilibrium system in which different reaction zones self-organize into a geometry that can dictate the progress of an entire process sequence. Multiple (routinely around 10, and in some cases more than 20) immiscible or pairwise-immiscible liquids of different densities are placed into a rotating container, in which they experience a centrifugal force that dominates over surface tension. As a result, the liquids organize into concentric layers, with thicknesses as low as 150 micrometres and theoretically reaching tens of micrometres. The layers are robust, yet can be internally mixed by accelerating or decelerating the rotation, and each layer can be individually addressed, enabling the addition, sampling or even withdrawal of entire layers during rotation. These features are combined in proof-of-concept experiments that demonstrate, for example, multistep syntheses of small molecules of medicinal interest, simultaneous acid-base extractions, and selective separations from complex mixtures mediated by chemical shuttles. We propose that 'wall-less' concentric liquid reactors could become a useful addition to the toolbox of process chemistry at small to medium scales and, in a broader context, illustrate the advantages of transplanting material and/or chemical systems from traditional, static settings into a rotating frame of reference.

Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry

The challenge of prebiotic chemistry is to trace the syntheses of life’s key building blocks from a handful of primordial substrates. Here we report a forward-synthesis algorithm that generates a full network of prebiotic chemical reactions accessible from these substrates under generally accepted conditions. This network contains both reported and previously unidentified routes to biotic targets, as well as plausible syntheses of abiotic molecules. It also exhibits three forms of nontrivial chemical emergence, as the molecules within the network can act as catalysts of downstream reaction types; form functional chemical systems, including self-regenerating cycles; and produce surfactants relevant to primitive forms of biological compartmentalization. To support these claims, computer-predicted, prebiotic syntheses of several biotic molecules as well as a multistep, self-regenerative cycle of iminodiacetic acid were validated by experiment.

Computer-generated "synthetic contingency" plans at times of logistics and supply problems: scenarios for hydroxychloroquine and remdesivir

A computer program for retrosynthetic planning helps develop multiple "synthetic contingency" plans for hydroxychloroquine and also routes leading to remdesivir, both promising but yet unproven medications against COVID-19. These plans are designed to navigate, as much as possible, around known and patented routes and to commence from inexpensive and diverse starting materials, so as to ensure supply in case of anticipated market shortages of commonly used substrates. Looking beyond the current COVID-19 pandemic, development of similar contingency syntheses is advocated for other already-approved medications, in case such medications become urgently needed in mass quantities to face other public-health emergencies.

Targeted crystallization of mixed-charge nanoparticles in lysosomes induces selective death of cancer cells

Lysosomes have become an important target for anticancer therapeutics because lysosomal cell death bypasses the classical caspase-dependent apoptosis pathway, enabling the targeting of apoptosis- and drug-resistant cancers. However, only a few small molecules-mostly repurposed drugs-have been tested so far, and these typically exhibit low cancer selectivity, making them suitable only for combination therapies. Here, we show that mixed-charge nanoparticles covered with certain ratios of positively and negatively charged ligands can selectively target lysosomes in cancerous cells while exhibiting only marginal cytotoxicity towards normal cells. This selectivity results from distinct pH-dependent aggregation events, starting from the formation of small, endocytosis-prone clusters at cell surfaces and ending with the formation of large and well-ordered nanoparticle assemblies and crystals inside cancer lysosomes. These assemblies cannot be cleared by exocytosis and cause lysosome swelling, which gradually disrupts the integrity of lysosomal membranes, ultimately impairing lysosomal functions and triggering cell death.

Targeted crystallization of mixed-charge nanoparticles in lysosomes induces selective death of cancer cells

Lysosomes have become an important target for anticancer therapeutics because lysosomal cell death bypasses the classical caspase-dependent apoptosis pathway, enabling the targeting of apoptosis- and drug-resistant cancers. However, only a few small molecules-mostly repurposed drugs-have been tested so far, and these typically exhibit low cancer selectivity, making them suitable only for combination therapies. Here, we show that mixed-charge nanoparticles covered with certain ratios of positively and negatively charged ligands can selectively target lysosomes in cancerous cells while exhibiting only marginal cytotoxicity towards normal cells. This selectivity results from distinct pH-dependent aggregation events, starting from the formation of small, endocytosis-prone clusters at cell surfaces and ending with the formation of large and well-ordered nanoparticle assemblies and crystals inside cancer lysosomes. These assemblies cannot be cleared by exocytosis and cause lysosome swelling, which gradually disrupts the integrity of lysosomal membranes, ultimately impairing lysosomal functions and triggering cell death.

Additive Contact Polarization of Nonferroelectric Polymers for Patterning of Multilevel Memory Elements

When a thin polymer film supported by a conductive substrate is contacted by and then separated from a micropatterned polymeric stamp, the so-called contact electrification creates electrical charges over the stamped regions. Simultaneously, image charges are induced in the conductive substrate. Together, the surface and image charges establish large fields within the film, in effect polarizing it. Upon consecutive stampings, the magnitudes of polarization add up, enabling imprinting of multilevel polarization patterns. Because the electric field is high only within the film but low across the Gaussian surface surrounding the film/substrate system, the discharge of surface charges is slow and the polarization patterns are relatively long-lived. These findings are significant since multilevel polarization states have, to date, been achieved only in ferroelectrics or some specialized polymers-the current method extends them to common polymers such as poly(methyl methacrylate), poly(vinyl pyrrolidone), or poly(vinyl acetate).

Charged Metal Nanoparticles for Chemoelectronic Circuits

Although metal nanoparticles (NPs) stabilized with self-assembled monolayers (SAMs) of various organic ligands have proven useful in applications ranging from chemical sensing, to bionanotechnology, to plasmonics and energy conversion, they have not been widely considered as suitable building blocks of electronic circuitry, largely because metals screen electric fields and prevent electrically tunable conductivity. However, when metal nanoparticles a few nanometers in size are stabilized by charged ligands and placed under bias, the counterions surrounding the NPs can redistribute and establish local electric fields that feed back into the electronic currents passing through the nanoparticles' metallic cores. Herein, the manner in which the interplay between counterion gradients and electron flows can be controlled by using different types of SAMs is discussed. This can give rise to a new class of nanoparticle-based "chemoelectronic" logic circuits capable of sensing, processing, and ultimately reporting various chemical signals.

Computational design of syntheses leading to compound libraries or isotopically labelled targets

Although computer programs for retrosynthetic planning have shown improved and in some cases quite satisfactory performance in designing routes leading to specific, individual targets, no algorithms capable of planning syntheses of entire target libraries - important in modern drug discovery - have yet been reported. This study describes how network-search routines underlying existing retrosynthetic programs can be adapted and extended to multi-target design operating on one common search graph, benefitting from the use of common intermediates and reducing the overall synthetic cost. Implementation in the Chematica platform illustrates the usefulness of such algorithms in the syntheses of either (i) all members of a user-defined library, or (ii) the most synthetically accessible members of this library. In the latter case, algorithms are also readily adapted to the identification of the most facile syntheses of isotopically labelled targets. These examples are industrially relevant in the context of hit-to-lead optimization and syntheses of isotopomers of various bioactive molecules.

Rapid and Accurate Prediction of pKa Values of C-H Acids Using Graph Convolutional Neural Networks

The ability to estimate the acidity of C-H groups within organic molecules in non-aqueous solvents is important in synthetic planning to correctly predict which protons will be abstracted in reactions such as alkylations, Michael additions, or aldol condensations. This Article describes the use of the so-called graph convolutional neural networks (GCNNs) to perform such predictions on the time scales of milliseconds and with accuracy comparing favorably with state-of-the-art solutions, including commercial ones. The crux of the method is to train GCNNs using descriptors that reflect not only topological but also chemical properties of atomic environments. The model is validated against adversarial controls, supplemented by the discussion of realistic synthetic problems (on which it correctly predicts the most acidic protons in >90% of cases), and accompanied by a Web application intended to aid the community in everyday synthetic planning.

Correction: Immature dendritic cells navigate microscopic mazes to find tumor cells

Correction for 'Immature dendritic cells navigate microscopic mazes to find tumor cells' by Eujin Um et al., Lab Chip, 2019, 19, 1665-1675.

Dynamic Assembly of Small Parts in Vortex-Vortex Traps Established within a Rotating Fluid

Stable, purely fluidic particle traps established by vortex flows induced within a rotating fluid are described. The traps can manipulate various types of small parts, dynamically assembling them into high-symmetry clusters, cages, interlocked architectures, jammed colloidal monoliths, or colloidal formations on gas bubbles. The strength and the shape of the trapping region can be controlled by the strengths of one or both vortices and/or by the system's global angular velocity. The system exhibits a range of interesting dynamical behaviors including a Hopf-bifurcation transition between equilibrium-point trapping and the so-called limit cycle in which the particles are confined to circular orbits. Theoretical considerations indicate that these vortex-vortex traps can be further miniaturized to manipulate objects with sizes down to ≈10 µm.

Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

As the programs for computer-aided retrosynthetic design come of age, they are no longer identifying just one or few synthetic routes but a multitude of chemically plausible syntheses, together forming large, directed graphs of solutions. An important problem then emerges: how to select from these graphs and present to the user manageable numbers of top-scoring pathways that are cost-effective, promote convergent vs. linear solutions, and are chemically diverse so that they do not repeat only minor variations in the same chemical theme. This paper describes a family of reaction network algorithms that address this problem by (i) using recursive formulae to assign realistic prices to individual pathways and (ii) applying penalties to chemically similar strategies so that they are not dominating the top-scoring routes. Synthetic examples are provided to illustrate how these algorithms can be implemented - on the timescales of ∼1 s even for large graphs - to rapidly query the space of synthetic solutions under the scenarios of different reaction yields and/or costs associated with performing reaction operations on different scales.

Immature dendritic cells navigate microscopic mazes to find tumor cells

Dendritic cells (DCs) are potent antigen-presenting cells with high sentinel ability to scan their neighborhood and to initiate an adaptive immune response. Whereas chemotactic migration of mature DCs (mDCs) towards lymph nodes is relatively well documented, the migratory behavior of immature DCs (imDCs) in tumor microenvironments is still poorly understood. Here, microfluidic systems of various geometries, including mazes, are used to investigate how the physical and chemical microenvironment influences the migration pattern of imDCs. Under proper degree of confinement, the imDCs are preferentially recruited towards cancer vs. normal cells, accompanied by increased cell speed and persistence. Furthermore, a systematic screen of cytokines, reveals that Gas6 is a major chemokine responsible for the chemotactic preference. These results and the accompanying theoretical model suggest that imDC migration in complex tissue environments is tuned by a proper balance between the strength of the chemical gradients and the degree of spatial confinement.

Propagation of Oscillating Chemical Signals through Reaction Networks

Akin to electronic systems that can tune to and process signals of select frequencies, systems/networks of chemical reactions also "propagate" time-varying concentration inputs in a frequency-dependent manner. Whereas signals of low frequencies are transmitted, higher frequency inputs are dampened and converted into steady-concentration outputs. Such behavior is observed in both idealized reaction chains as well as realistic signaling cascades, in the latter case explaining the experimentally observed responses of such cascades to input calcium oscillations. These and other results are supported by numerical simulations within the freely available Kinetix web application we developed to study chemical systems of arbitrary architectures, reaction kinetics, and boundary conditions.

Correction: Electrostatics at the nanoscale

Correction for 'Electrostatics at the nanoscale' by David A. Walker et al., Nanoscale, 2011, 3, 1316-1344.

Uniform and directional growth of centimeter-sized single crystals of cyclodextrin-based metal organic frameworks

Although macroscopically-sized MOF crystals have proven of interest for efficient chromatographic separations, information processing, or optoelectronic devices, growing really large crystals has proven problematic. A growth-and-reseeding method can now produce MOF monocrystals ca. 1 cm³ in volume vs. at most ca. 0.025 cm³ by prior methods.

The logic of translating chemical knowledge into machine-processable forms: a modern playground for physical-organic chemistry

With renewed interest and significant progress in computer-assisted synthetic planning, it is essential to codify the logic that should be followed when translating organic synthetic knowledge into reaction rules understandable to the machine.

Lévy-like movement patterns of metastatic cancer cells revealed in microfabricated systems and implicated in vivo

Metastatic cancer cells differ from their non-metastatic counterparts not only in terms of molecular composition and genetics, but also by the very strategy they employ for locomotion. Here, we analyzed large-scale statistics for cells migrating on linear microtracks to show that metastatic cancer cells follow a qualitatively different movement strategy than their non-invasive counterparts. The trajectories of metastatic cells display clusters of small steps that are interspersed with long "flights". Such movements are characterized by heavy-tailed, truncated power law distributions of persistence times and are consistent with the Lévy walks that are also often employed by animal predators searching for scarce prey or food sources. In contrast, non-metastatic cancerous cells perform simple diffusive movements. These findings are supported by preliminary experiments with cancer cells migrating away from primary tumors in vivo. The use of chemical inhibitors targeting actin-binding proteins allows for "reprogramming" the Lévy walks into either diffusive or ballistic movements.

Switchable counterion gradients around charged metallic nanoparticles enable reception of radio waves

Mechanically flexible, easy-to-process, and environmentally benign materials capable of current rectification are interesting alternatives to "hard" silicon-based devices. Among these materials are metallic/charged-organic nanoparticles in which electronic currents though metal cores are modulated by the gradients of counterions surrounding the organic ligands. Although layers of oppositely charged particles can respond to both electronic and chemical signals and can function even under significant mechanical deformation, the rectification ratios of these "chemoelectronic" elements have been, so far, low. This work shows that significantly steeper counterion gradients and significantly higher rectification ratios can be achieved with nanoparticles of only one polarity but in contact with a porous electrode serving as a counterion "sink." These composite structures act as rectifiers even at radio frequencies, providing a new means of interfacing counterions' dynamics with high-frequency electronic currents.