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Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

Development of scalable processes for C­(sp3)–H oxidative carbonylation of alkylamines is reported to provide convenient access to the β-lactam scaffol d. A study of the kinetics of the process revealed that the reaction is CO-limited even at elevated pressures and that there is an optimal CO concentration for the most effective outcomethis understanding led to an increase in the turnover number from 7 to 420 in the optimized process. Two scalable processes were then developed: a batch process, characterized by a very low catalyst loading, and a continuous process for an oxidative C–H carbonylation reaction that uses a copper-tube-flow reactor as a heterogeneous source of Cu2+ oxidant. The continuous process was tested on oxidative carbonylation of several alkylamines, yielding very good results with virtually no optimization required. This study thereby builds upon the utility of flow chemistry applications to oxidative carbonylations and scalable metal-catalyzed processes more generally.

We identify many larger organics as products of electrochemical CO2 reduction in high-current GDEs – most interestingly, C4 and C5 isomers. The analysis of selectivity trends allows for deduction of general hypotheses to build a detailed mechanism.

The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

Designing a simple, yet representative reaction network for subsequent micro-kinetic analysis is important for limiting the cost of evaluation and ensuring model solvability. This is currently achieved by employing sensitivity analysis over a comprehensive reaction network (CRN) to screen reaction species. However, as a reaction network is being simplified for a particular catalyst composition, it loses its transferability to other compositions. Therefore, in this study, a two-way approach is presented to circumvent this problem. Firstly, a generalizable model outcome is identified, i.e. minimum reactant conversions (xR), based on a mass-flow analysis. Then, a stepwise workflow is developed for constructing a partial reaction network (PRN) to insure transferability of min (xR) for a range of varying catalyst energetics, in the absence of experimental data for validation. Lastly, the transferability of this approach is demonstrated for CH4 dry reforming by developing a PRN using Ni(1 1 1) as the initial catalyst and testing it over Ru(0 0 1).

Optimisation-based design is an established methodology that aims to achieve a globally optimal solution to a complex process design task by representing it as an optimisation problem. We propose a hybrid framework for decomposition-based process design, centred around hierarchical reinforcement learning and mathematical programming. The framework enables the agent to assemble processes, employ mathematical programming, and discover optimal designs without the need for a pre-defined process superstructure. The agent is composed of: (i) a higher level, that learns to construct the overall process by connecting process sections, and (ii) a lower level, that learns to build and solve sections by connecting and initialising unit operations. Such modularity allows for flexible and robust optimisation in constrained, nonlinear and nonconvex spaces. The framework is demonstrated in a case study of an intensified ethylene oxide production plant, yielding improved results compared to baseline designs reported in the open literature. The case study was implemented in Pyomo. Results reveal insights on the agent's learning speed and ability to leverage process models.

Production of value-added chemicals from lignocellulose is one of the key strands of the emerging bio-economy. A synthetic approach has been developed to produce p-methoxy coumarate esters from waste corn stover and corn bran, and further transform these compounds into consumer products. In the first step, p-coumaric acid (pCA) and ferulic acid (FA) are released through mild alkaline hydrolysis of corn waste, in more than 20 mg g-1 yield (10 wt % based on lignin content). Subsequently, heterogeneous solid acids catalyze the esterification of pCA with various alcohols. Amberlite IR-15 shows the best catalytic performance, owing to its optimal acid density, large surface area, and suitable pore diameter. The solid acid catalyst can be recycled with slight activity loss and regenerated by acid washing and vacuum drying. Finally, methylation of p-coumarate ester to give target product p-methoxy coumarate esters is carried out by using commercially available and low-cost inorganic bases (e. g., K2 CO3 ). Base-catalyzed transesterification of methyl p-methoxy coumarate to p-methoxy coumarate esters is also investigated.

The development of sustainable energy economies is blocked by the lack of stable electrical energy sources with high power and energy densities. Next generation supercapacitors utilizing two-dimensional layered double hydroxides (LDHs) promise to fill this need in hybrid and standalone architectures; however, despite their high power and energy densities, LDH supercapacitors have poor stability. New methods for creating robust LDH electrodes are necessary to prevent this degradation. In this study, the recently developed annular microreactor is used to synthesize defect-rich NiCo LDH nanocrystals. A simple, solvent-based method is used to rationally generate binder-free, superstructured-thin films on Ni foam electrodes. Control over crystallite size, thinness and orientation improves contact with the conductive substrate, increase reactivity and improve structural stability. Optimized electrodes are fabricated with specific capacitances from 3,000-5,000 F/g at charging rates as high as 1,000 A/g, a performance that was retained after 20,000 cycles. This is twice as stable at 5,000 times the current density of the most stable reported Ni-based supercapacitor. Ultimately, this study addresses key concerns in electrode development, introduces new approaches through reactor technology and solvent-mediated assembly, and opens new ground for more fundamental inquiries into the mechanisms of electron transport in 2D systems.

The topology of endoplasmic reticulum (ER) network is highly regulated by various cellular and environmental stimuli and affects major functions such as protein quality control and the cell’s response to metabolic changes. The ability to quantify the dynamical changes of the ER structures in response to cellular perturbations is crucial for the development of novel therapeutic approaches against ER associated diseases, such as hereditary spastic paraplegias and Niemann Pick Disease type C. However, the rapid movement and small spatial dimension of ER networks make this task challenging. Here, we combine video-rate super-resolution imaging with a state-of-the-art semantic segmentation method capable of automatically classifying sheet and tubular ER domains inside individual cells. Data are skeletonised and represented by connectivity graphs to enable the precise and efficient quantification and comparison of the network connectivity from different complex ER phenotypes. The method, called ERnet, is powered by a Vision Transformer architecture, and integrates multi-head self-attention and channel attention into the model for adaptive weighting of frames in the time domain. We validated the performance of ERnet by measuring different ER morphology changes in response to genetic or metabolic manipulations. Finally, as a means to test the applicability and versatility of ERnet, we showed that ERnet can be applied to images from different cell types and also taken from different imaging setups. Our method can be deployed in an automatic, high-throughput, and unbiased fashion to identify subtle changes in cellular phenotypes that can be used as potential diagnostics for propensity to ER mediated disease, for disease progression, and for response to therapy.

Electrochemical conversion of CO2 (CO2R) into fuels and chemicals can both reduce CO2 emissions and allow for clean manufacturing in the scenario of significant expansion of renewable power generation. However, large-scale process deployment is currently limited by unfavourable process economics resulting from significant up- and down-stream costs for obtaining pure CO2, separation of reaction products and increased logistical effort. We have discovered a method for economically viable recycling of waste CO2 that addresses these challenges. Our approach is based on integration of a CO2R unit into an existing manufacturing process: ethylene oxide (EO) production, which emits CO2 as a by-product. The standard EO process separates waste CO2 from the gas stream, hence the substrate for electroreduction is available at an EO plant at no additional cost. CO2 can be converted into an ethylene-rich stream and recycled on-site back to the EO reactor, which uses ethylene as a raw material, and also the anode product (oxygen) can be simultaneously valorized for the EO production reaction. If powered by a renewable electricity source, the process will significantly (ca. 80%) reduce the CO2 emissions of an EO manufacturing plant. A sensitivity analysis shows that the recycling approach can be economically viable in the short term and that its payback time could be as low as 1–2 years in the regions with higher carbon taxes and/or with access to low-cost electricity sources.

Funder: UCB; Id: http://dx.doi.org/10.13039/100011110, Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine‐readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature‐based, and computer‐learned representations. Three of the most significant representations are simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatics

Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly selective and robust chemistry for complex reaction networks in bio-waste mixtures. We demonstrate an approach to optimising a chemical route for multiple objectives starting from a mixture derived from bio-waste. We optimise the recently developed route from a mixture of waste terpenes to p-cymene. In the first reaction step it was not feasible to build a detailed kinetic model. A Bayesian multiple objectives optimisation algorithm TS-EMO was used to optimise the first two steps of reaction for maximum conversion and selectivity. The model suggests a set of very different conditions that result in simultaneous high values of the two outputs.

Summary: Recycling of waste CO2 to bulk chemicals has a tremendous potential for the decarbonization of the chemical industry. Quantitative analysis of the prospects of this technology is hindered by the lack of flexible techno-economic assessment (TEA) models that enable evaluation of the processing costs under different deployment scenarios. In this protocol, we explain how to convert literature data into metrics useful for evaluation of the emerging electrolysis technologies, derive TEA models, and illustrate their use with a CO2-to-ethylene example.For complete details on the use and execution of this protocol, please refer to Barecka et al. (2021a).

As the chemical sector looks to decarbonize, one promising solution is the utilization of bio-feedstocks and biowaste to produce functional molecules. There is, therefore, great interest in understanding how and where to integrate these resources within chemical supply chains. To assist such efforts, screening methodologies relying on large reaction networks have recently been proposed.1,2 However, they are currently hindered by a lack of data for region-specific heterogenous raw materials compositions, as well as upstream pretreatments to isolate the important feedstocks. This study illustrates the workflow and data requirements of early stage biowaste stream evaluation through a case study on the waste landscape in and around the Singapore region. We first investigate biowaste sources that are available, stable in quantities, underutilized, pure, and yielding the feedstocks of interest. Oil palm empty fruit bunch (EFB), a lignocellulosic biowaste stream widely available in Malaysia and Indonesia, meets these criteria. We then simulate an ethanol organosolv pretreatment process for the fractionation of cellulose, lignin and xylose from EFB, and characterise the economic and environmental performances of the process through its exergy profile; this enables a link to chemical pathway identification in reaction networks. This study outlines the initial steps towards generating open datasets on biowaste for development of sustainable supply chains.

Dry reforming of methane (DRM) has incited significant academic and industrial attention in the past couple of decades. Although Ni-based catalysts have shown good activity for DRM, deactivation due to carbon formation is a serious concern. Several strategies are proposed, including doping by other metals and metalloids, to improve the stability of Ni, and hence we investigated boron-doped Ni (NiB) as a potential catalyst for DRM. In this work, we combined DFT and microkinetic modeling to identify the dominant reaction pathways and kinetically relevant steps of the DRM reaction system on Ni and NiB surfaces. We considered a detailed reaction network involving multiple CO2 and CH4 dissociation routes, side reactions (H2O formation, Boudouard reaction) and desorption of products on both surfaces. Our DFT calculations suggest that both Ni and NiB share similar CO2 (direct dissociation to CO*) and CH4 (sequential dehydrogenation to C*) activation routes. Compared to Ni, the CO2 activation barrier on NiB is higher by 44 kJ mol−1 but the barriers in CH4 activation routes are significantly lower. Subsequently, the DFT-calculated energies were used to derive kinetic rate constants of the elementary reaction steps. These rate constants were employed to develop a microkinetic model of the DRM process over Ni (111) and NiB catalysts. At low CH4 and CO2 partial pressures (

Current chemical industry is heavily reliant on fossil resources as feedstock, resulting in a linear economical model, namely extract-make-use-dispose. In contrast, circular economy, targeting a more sustainable use of materials, aims to construct a loop in which waste is repurposed as feedstock. Circular economy in chemical industry may be achieved by integrating bio-waste into the chemical supply chain. However, bio-waste is different in many ways compared with fossil fuels, such as sparse distribution, geographical diversity, season-dependent supply, variability and complex composition. This short review highlights recent technological advances in valorization of bio-waste including new processing methods and promissing products. Analysis into the specific features of bio-waste suggests that bio-waste could be a promising feedstock for hub and fine chemicals, but not the commodity chemicals. A logic model of bio-waste based circular economy is proposed. Application of data mining and automated chemical routes are envisaged.

Since isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) are the universal precursors of both essential oil components, and the antimalarial agent artemisinin and its derivatives in Artemesia annua L., this paper aims to correlate the spotted differences in their concentrations by screening Artemesia annua L. field-grown in nine locations around the world that may reveal the role of any these compounds as precursors or competitors in the biosynthetic pathway of the sesquiterpene lactone : artemisinin. Principal component analysis (PCA) revealed that artemisinin is positively correlated to β-pinene, 1,8-cineole, sabinene hydrate, borneol and 1-octen-3-ol; but negatively to artemisinic acid and β-caryophyllene oxide. Hierarchical cluster analysis (HCA) classified locations into two distinct groups in which artemisinin concentration stood as the main driving factor to build similarities between the locations. In parallel, an improved convergence approach based on idiosyncratic similarities able to capture heterogeneity across individuals is proposed, which was able to classify compounds into four distinct clusters. Artemisinin appeared to be cross-linked to p-cymene, cis-carvyle acetate, 4-terpinene-1-ol, β-caryophyllene, β-farnesene, β-selinene, α-selinene, β-caryophyllene oxide and α-costol. It is interesting to see how camphor and spathulenol behaved as a distinct cluster group, which suggests that biosynthesis of these two compounds follows a different but a competitive pathway ; thus limiting their production could be a key to control and enhance the production of artemisinin.

The chemical engineering journal 423, 130248 (2021). doi:10.1016/j.cej.2021.130248, Published by Elsevier, Amsterdam

Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous or similar chemical optimization studies. Therefore, BO can still require more iterations than many experimental budgets provide. Here, we overcome this challenge using multi-task BO. Through in silico benchmarking studies, we show how past experimental data can be leveraged to improve the quality and speed of reaction optimization.

An efficient elevated-pressure catalytic oxidative process (2.5 mol % Co(NO3)2, 2.5 mol % MnBr2, air (30 bar), 125 °C, acetic acid, 6 h) has been developed to oxidize p-cymene into crystalline white terephthalic acid (TA) in ∼70% yield. Use of this mixed Co2+/Mn2+ catalytic system is key to obtaining high 70% yields of TA at relatively low reaction temperatures (125 °C) in short reaction times (6 h), which is likely to be due to the synergistic action of bromine and nitrate radicals in the oxidative process. Recycling studies have demonstrated that the mixed metal catalysts present in recovered mother liquors could be recycled three times in successive p-cymene oxidation reactions with no loss in catalytic activity or TA yield. Partial oxidation of p-cymene to give p-methylacetophenone (p-MA) in 55–60% yield can be achieved using a mixed CoBr2/Mn(OAc)2 catalytic system under 1 atm air for 24 h, while use of Co(NO3)2/MnBr2 under 1 atm O2 for 24 h gave p-toluic acid in 55–60% yield. Therefore, access to these simple catalytic aerobic conditions enables multiple biorenewable bulk terpene feedstocks (e.g., crude sulfate turpentine, turpentine, cineole, and limonene) to be converted into synthetically useful bio-p-MA, bio-p-toluic acid, and bio-TA (and hence bio-polyethylene terephthalate) as part of a terpene based biorefinery., Efficient catalytic aerobic oxidative protocols are used to transform terpene-derived bio-p-cymene into biorenewable terephthalic acid, p-toluic acid, and p-methylacetophenone.

Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require to re-examine the existing theoretical frameworks to include the advantages deriving from the new reality of digitalization of business and research. In this work, we review the existing approaches, clearly stating the role of automation and machine-learning-guided optimization in the broader framework. Moving from this, we review the state of the art of automated hardware and software for formulated product design, and identify the open challenges for future research. Perspectives are given on the emerging fields of automated discovery, scale-up, and multistage optimization, and a unitary picture of the existing connections is provided, in the general context of a completely digital R&D workflow.