A deconstruction-reconstruction strategy for pyrimidine diversification.

Structure-activity relationship (SAR) studies are fundamental to drug and agrochemical development, yet only a few synthetic strategies apply to the nitrogen heteroaromatics frequently encountered in small molecule candidates1-3. Here we present an alternative approach in which we convert pyrimidine-containing compounds into various other nitrogen heteroaromatics. Transforming pyrimidines into their corresponding N-arylpyrimidinium salts enables cleavage into a three-carbon iminoenamine building block, used for various heterocycle-forming reactions. This deconstruction-reconstruction sequence diversifies the initial pyrimidine core and enables access to various heterocycles, such as azoles4. In effect, this approach allows heterocycle formation on complex molecules, resulting in analogues that would be challenging to obtain by other methods. We anticipate that this deconstruction-reconstruction strategy will extend to other heterocycle classes.

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p-Block-Atom-Based Molecules.

"How strong is this Lewis acid?" is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI-DSD-BLYP-D3(BJ)/def2-QZVPP//PBEh-3c level of theory, including 13 different p-block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA-GNN, two message-passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol-1 (r2=0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol-1 spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA-GNN and the FIA49k dataset can be reached via a free web app (www.grebgroup.de/fia-gnn).

Organocatalyzed Carbonylation of Alkyl Halides Driven by Visible Light.

Herein, we describe a new strategy for the carbonylation of alkyl halides with different nucleophiles to generate valuable carbonyl derivatives under visible light irradiation. This method is mild, robust, highly selective, and proceeds under metal-free conditions to prepare a range of structurally diverse esters and amides in good to excellent yields. In addition, we highlight the application of this activation strategy for 13C isotopic incorporation. We propose that the reaction proceeds by a photoinduced reduction to afford radical anions from alkyl halides, which undergo subsequent single electron-oxidation to form a carbocationic intermediate. Carbon monoxide is trapped by the carbocation to generate an acylium cation, which can be attacked by a series of nucleophiles to give a range of carbonyl products.

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters.

The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of "top-down" macroscopic observables, such as Hammett parameters using a "bottom-up" computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σm predictions fare much worse than σp. As a result, the choice of where to compute these descriptors-for the ring carbons or the attached H or other substituent atoms-affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Understanding the infection severity and epidemiological characteristics of mpox in the UK.

In May 2022, individuals infected with the monkeypox virus were detected in the UK without clear travel links to endemic areas. Understanding the clinical characteristics and infection severity of mpox is necessary for effective public health policy. The study period of this paper, from the 1st June 2022 to 30th September 2022, included 3,375 individuals that tested positive for the monkeypox virus. The posterior mean times from infection to hospital admission and length of hospital stay were 14.89 days (95% Credible Intervals (CrI): 13.60, 16.32) and 7.07 days (95% CrI: 6.07, 8.23), respectively. We estimated the modelled Infection Hospitalisation Risk to be 4.13% (95% CrI: 3.04, 5.02), compared to the overall sample Case Hospitalisation Risk (CHR) of 5.10% (95% CrI: 4.38, 5.86). The overall sample CHR was estimated to be 17.86% (95% CrI: 6.06, 33.11) for females and 4.99% (95% CrI: 4.27, 5.75) for males. A notable difference was observed between the CHRs that were estimated for each sex, which may be indicative of increased infection severity in females or a considerably lower infection ascertainment rate. It was estimated that 74.65% (95% CrI: 55.78, 86.85) of infections with the monkeypox virus in the UK were captured over the outbreak.

Late-stage benzenoid-to-troponoid skeletal modification of the cephalotanes exemplified by the total synthesis of harringtonolide.

Skeletal modifications enable elegant and rapid access to various derivatives of a compound that would otherwise be difficult to prepare. They are therefore a powerful tool, especially in the synthesis of natural products or drug discovery, to explore different natural products or to improve the properties of a drug candidate starting from a common intermediate. Inspired by the biosynthesis of the cephalotane natural products, we report here a single-atom insertion into the framework of the benzenoid subfamily, providing access to the troponoid congeners - representing the reverse of the proposed biosynthesis (i.e., a contra-biosynthesis approach). Computational evaluation of our designed transformation prompted us to investigate a Büchner-Curtius-Schlotterbeck reaction of a p-quinol methylether, which ultimately results in the synthesis of harringtonolide in two steps from cephanolide A, which we had previously prepared. Additional computational studies reveal that unconventional selectivity outcomes are driven by the choice of a Lewis acid and the nucleophile, which should inform further developments of these types of reactions.

The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England.

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution.

Radical Chlorination of Non-Resonant Heterobenzylic C-H Bonds and High-Throughput Diversification of Heterocycles.

Site-selective functionalization of the heterobenzylic C(sp3)-H bonds of pyridines and related heteroaromatic compounds presents challenges associated with the basic nitrogen atom and the variable reactivity among different positions on the heteroaromatic ring. Methods for functionalization of 2- and 4-alkylpyridines are increasingly available through polar pathways that leverage resonance stabilization of charge build-up at these positions. In contrast, functionalization of 3-alkylpyridines is largely inaccessible. Here, we report a photochemically promoted method for chlorination of non-resonant heterobenzylic C(sp3)-H sites in 3-alkylpyridines and related alkylheteroaromatics. Density functional theory calculations show that the optimal reactivity reflects a balance between the energetics of the two radical-chain propagation steps, with the preferred reagent consisting of an N-chlorosulfonamide. The operationally simple chlorination protocol enables access to heterobenzylic chlorides which serve as versatile intermediates in C-H cross-coupling reactions between heteroaromatic building blocks and diverse oxidatively sensitive nucleophiles using high-throughput experimentation.

Catalytic Effects of Active Site Conformational Change in the Allosteric Activation of Imidazole Glycerol Phosphate Synthase.

Imidazole glycerol phosphate synthase (IGPS) is a class-I glutamine amidotransferase (GAT) that hydrolyzes glutamine. Ammonia is produced and transferred to a second active site, where it reacts with N1-(5'-phosphoribosyl)-formimino-5-aminoimidazole-4-carboxamide ribonucleotide (PrFAR) to form precursors to purine and histidine biosynthesis. Binding of PrFAR over 25 Šaway from the active site increases glutaminase efficiency by ∼4500-fold, primarily altering the glutamine turnover number. IGPS has been the focus of many studies on allosteric communication; however, atomic details for how the glutamine hydrolysis rate increases in the presence of PrFAR are lacking. We present a density functional theory study on 237-atom active site cluster models of IGPS based on crystallized structures representing the inactive and allosterically active conformations and investigate the multistep reaction leading to thioester formation and ammonia production. The proposed mechanism is supported by similar, well-studied enzyme mechanisms, and the corresponding energy profile is consistent with steady-state kinetic studies of PrFAR + IGPS. Additional active site models are constructed to examine the relationship between active site structural change and transition-state stabilization via energy decomposition schemes. The results reveal that the inactive IGPS conformation does not provide an adequately formed oxyanion hole structure and that repositioning of the oxyanion strand relative to the substrate is vital for a catalysis-competent oxyanion hole, with or without the hVal51 dihedral flip. These findings are valuable for future endeavors in modeling the IGPS allosteric mechanism by providing insight into the atomistic changes required for rate enhancement that can inform suitable reaction coordinates for subsequent investigations.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.

BACKGROUND: Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast. METHODS: We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models. RESULTS: Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend. CONCLUSIONS: This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Seasonal influenza causes a burden for hospitals and therefore it is useful to be able to accurately predict how many patients might be admitted with the disease. We attempted to predict influenza admissions up to 14 days in the future by creating a computational model that incorporates how the disease is reported and how it spreads. We evaluated our optimised model on data acquired during the winter of 2022-2023 data in England and compared it with previously developed models. Our model was better at modelling how influenza spreads and predicting future hospital admissions than the models we compared it to. Improving how influenza admissions are forecast can enable hospitals to prepare better for increased admissions, enabling improved treatment and reduced death for all patients in hospital over winter.