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A four-component Ugi reaction is described for the stereoselective synthesis of novel C-N atropisomeric peptide analogues. Using this approach, a combination of simple, readily available starting materials ( ortho -substituted anilines, aldehydes, carboxylic acids and isocyanides) could be combined to access complex products possessing both central and axial chirality in up to 92% yield and >95 : 5 d.r. Variation of the reaction temperature enabled the development of stereodivergent reactions capable of selectively targeting either diastereoisomer of a desired product from a single set of starting materials with high levels of stereocontrol. Detailed experimental and computational studies have been performed to probe the reaction mechanism and stereochemical outcome of these reactions. Preliminary studies show that novel atropisomeric scaffolds prepared using this method display inhibitory activity against M. tuberculosis with a significant difference in activity observed between different atropisomers., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol -1 . The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol -1 for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol -1 ., Competing Interests: There are no conflicts of interest., (This journal is © The Royal Society of Chemistry.)

A robust system for automatic processing and assignment of raw 13 C and 1 H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated 13 C and 1 H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at https://github.com/KristapsE/DP4-AI., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

We report a method for the direct and rapid conversion of primary and secondary alcohols to the corresponding phosphorothiolates in yields ranging from 64% to 97%, using as a coupling agent the iminium salt prepared from N,N-dimethylthioformamide and Meerwein's salt. Selective reaction of primary alcohols in the presence of secondary alcohols is possible. The reaction of secondary alcohols proceeds stereospecifically with inversion of configuration.

The origins of the stereodivergence in the thioester oxy-Michael cyclization for the formation of 4-hydroxy-2,6- cis - or 2,6- trans -substituted tetrahydropyran rings under different conditions was investigated both computationally and experimentally. Synthetic studies showed that the 4-hydroxyl group was essential for stereodivergence. When the 4-hydroxyl group was present, TBAF-mediated conditions gave the 2,6- trans -tetrahydropyran and trifluoroacetic acid-mediated conditions gave the 2,6- cis -tetrahydropyran. This stereodivergence vanished when the hydroxyl group was removed or protected. Computational studies revealed that: (i) the trifluoroacetic acid catalysed formation of 2,6- cis -tetrahydropyrans was mediated by a trifluoroacetate-hydroxonium bridge and proceeded via a chair-like transition state; (ii) the TBAF-mediated formation of 2,6- trans -tetrahydropyrans proceeded via a boat-like transition state, where the 4-hydroxyl group formed a crucial hydrogen bond to the cyclizing alkoxide; (iii) both reactions are under kinetic control. The utility of this stereodivergent approach for the formation of 4-hydroxy-2,6-substituted tetrahydropyran rings has been demonstrated by the total syntheses of the anti-osteoporotic natural products diospongin A and B.

In this work, inspired by the graph transformer, we presented an improved protocol, termed GT-NMR, which integrates 2D molecular graph representation with Transformer architecture, for accurate yet efficient prediction of NMR chemical shifts. The effectiveness of the GT-NMR was thoroughly examined with the standard nmrshiftdb2 dataset, 37 natural products and structural elucidation of 11 pairs of natural products. Systematical analysis affirms that GT-NMR outperforms traditional graph-based methods in all aspects, achieving state-of-the-art performance, with the mean absolute error of 0.158 and 1.189 ppm in predicting 1H and 13C NMR chemical shifts, respectively, for the standard nmrshiftdb2 dataset. Further scrutiny of its practical applications indicates that GT-NMR's efficacy is closely tied to molecular complexity, as quantified by the size-normalized spatial score (nSPS). For relatively simple molecules (nSPS < = 27.71), GT-NMR performs comparably to the best density functional while its effectiveness significantly diminishes with complex molecules characterized by higher nSPS values (nSPS > = 38.42). This trend is consistent across other graph-based NMR chemical shift prediction methods as well. Therefore, while employing GT-NMR or other graph-based methods for the rapid and routine prediction of NMR chemical shifts, it is advisable to utilize nSPS to assess their suitability. The source codes and trained model of GT-NMR are publicly available at GitHub. Scientific contribution GT-NMR, which combines the 2D molecular graph representation with the Transformer architecture, was implemented for the first time to predict atom-level NMR chemical shifts, achieving state-of-the-art performance. More importantly, the reliability of the GT-NMR and graph-based methods was assessed for the first time in terms of molecular complexity, as quantified by the size-normalized spacial score (nSPS). Systematical scrutiny demonstrated that GT-NMR offer a valuable way for routine application in structural screening and elucidation of relatively simple molecules. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease (IBD) is a chronic, lifelong disorder characterized by inflammation of the gastrointestinal (GI) tract. The exact etiology of IBD remains incompletely understood due to its multifaceted nature, which includes genetic predisposition, environmental factors, and host immune response dysfunction. Currently, there is no cure for IBD. This review discusses the available treatment options and the challenges they present. Importantly, we examine emerging therapeutics, such as biologics and immunomodulators, that offer targeted treatment strategies for IBD. While many IBD patients do not respond adequately to most biologics, recent clinical trials combining biologics with small-molecule drugs (SMDs) have provided new insights into improving the IBD treatment landscape. Furthermore, numerous novel and specific therapeutic targets have been identified. The high cost of IBD drugs poses a significant barrier to treatment, but this challenge may be alleviated with the development of more affordable biosimilars. Additionally, emerging point-of-care protein biomarkers from serum and plasma are showing potential for enhancing the precision of IBD diagnosis and prognosis. Several natural products (NPs), including crude extracts, small molecules, and peptides, have demonstrated promising anti-inflammatory activity in high-throughput screening (HTS) systems and advanced artificial intelligence (AI)-assisted platforms, such as molecular docking and ADMET prediction. These platforms are advancing the search for alternative IBD therapies derived from natural sources, potentially leading to more affordable and safer treatment options with fewer side effects. [ABSTRACT FROM AUTHOR]

Structure elucidation is a stage of paramount importance in the discovery of novel compounds because molecular structure determines their physical, chemical and biological properties. Computational prediction of spectroscopic data, mainly NMR, has become a widely used tool to help in such tasks due to its increasing easiness and reliability. However, despite the continuous increment in CPU calculation power, classical quantum mechanics simulations still require a lot of effort. Accordingly, simulations of large or conformationally complex molecules are impractical. In this context, a growing number of research groups have explored the capabilities of machine learning (ML) algorithms in computational NMR prediction. In parallel, important advances have been made in the development of machine learninginspired methods to correlate the experimental and calculated NMR data to facilitate the structural elucidation process. Here, we have selected some essential papers to review this research area and propose conclusions and future perspectives for the field. [ABSTRACT FROM AUTHOR]

Natural products (NPs) have played a vital role in human survival for millennia, particularly for their medicinal properties. Many traditional medicine practices continue to utilise crude plants and animal products for treating various diseases, including inflammation. In contrast, contemporary medicine focuses more on isolating drug-lead compounds from NPs to develop new and better treatment drugs for treating inflammatory disorders such as inflammatory bowel diseases. There is an ongoing search for new drug leads as there is still no cure for many inflammatory conditions. Various approaches and technologies are used in drug discoveries from NPs. This review comprehensively focuses on anti-inflammatory small molecules and describes the key strategies in identifying, extracting, fractionating and isolating small-molecule drug leads. This review also discusses the (i) most used approaches and recently available techniques, including artificial intelligence (AI), (ii) machine learning, and computational approaches in drug discovery; (iii) provides various animal models and cell lines used in in-vitro and in-vivo assessment of the anti-inflammatory potential of NPs. [ABSTRACT FROM AUTHOR]

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products. [ABSTRACT FROM AUTHOR]

The review covers research published since 2017 and is focused on enantioselective synthesis using radical reactions. It describes recent approaches to the asymmetric synthesis of chiral molecules based on the application of the metal catalysis, dual metal and organocatalysis and finally, pure organocatalysis including enzyme catalysis. This review focuses on the synthetic aspects of the methodology and tries to show which compounds can be obtained in enantiomerically enriched forms. [ABSTRACT FROM AUTHOR]

The functionalisation of C–H bonds has been an enormous achievement in synthetic methodology, enabling new retrosynthetic disconnections and affording simple synthetic equivalents for synthons. Hydrogen atom transfer (HAT) is a key method for forming alkyl radicals from C–H substrates. Classic reactions, including the Barton nitrite ester reaction and Hofmann–Löffler–Freytag reaction, among others, provided early examples of HAT. However, recent developments in photoredox catalysis and electrochemistry have made HAT a powerful synthetic tool capable of introducing a wide range of functional groups into C–H bonds. Moreover, greater mechanistic insights into HAT have stimulated the development of increasingly site-selective protocols. Site-selectivity can be achieved through the tuning of electron density at certain C–H bonds using additives, a judicious choice of HAT reagent, and a solvent system. Herein, we describe the latest methods for functionalizing C–H/Si–H/Ge–H bonds using indirect HAT between 2018–2023, as well as a critical discussion of new HAT reagents, mechanistic aspects, substrate scopes, and background contexts of the protocols. [ABSTRACT FROM AUTHOR]

The catalytic asymmetric inverse-electron-demand oxa-Diels–Alder (IODA) reaction is a highly effective synthetic method for creating enantioenriched six-membered oxygen-containing heterocycles. Despite significant effort in this area, simple α,β-unsaturated aldehydes/ketones and nonpolarized alkenes are seldom utilized as substrates due to their low reactivity and difficulties in achieving enantiocontrol. This report describes an intermolecular asymmetric IODA reaction between α-bromoacroleins and neutral alkenes that is catalyzed by oxazaborolidinium cation 1f. The resulting dihydropyrans are produced in high yields and excellent enantioselectivities over a broad range of substrates. The use of acrolein in the IODA reaction produces 3,4-dihydropyran with an unoccupied C6 position in the ring structure. This unique feature is utilized in the efficient synthesis of (+)-Centrolobine, demonstrating the practical synthetic utility of this reaction. Additionally, the study found that 2,6-trans-tetrahydropyran can undergo efficient epimerization into 2,6-cis-tetrahydropyran under Lewis acidic conditions. This structural core is widespread in natural products. The asymmetric inverse-electron-demand oxa-Diels–Alder reaction (IODA) between activated α,β-unsaturated carbonyl compounds with highly polarized alkenes has been reported before but acroleins or simple alkenes were so-far rarely employed as substrates. Here, the authors study the catalytic asymmetric intermolecular IODA reaction between acroleins and simple alkenes. [ABSTRACT FROM AUTHOR]

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation. [ABSTRACT FROM AUTHOR]

A convenient regioselective synthesis of allyl- and vinylsilyl alcohols, from a common precursor, was described, by selecting the appropriate reaction conditions. Allyl- and vinylsilyl alcohols were tested in silyl-Prins cyclizations for the preparation of disubstituted oxygenated heterocycles in a one-pot sequential reaction. The methodology was sensitive to the structure of the starting alkenylsilyl alcohol and reaction conditions, with competitive pathways observed (particularly for allylsilyl alcohols), such as Peterson elimination and oxonia-Cope reactions. However, the use of vinylsilyl alcohols allowed the preparation of differently disubstituted cis-2,6-dihydropyrans in moderate to good yields. Computational studies support the proposed mechanism. [ABSTRACT FROM AUTHOR]

Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs of small organic compounds and we use this database to validate a method for the automated analysis of VCD spectra and the assignment of absolute configuration: the Cai•factor (Configuration: absolute information). The analysis of the data demonstrates that the procedure is a reliable and time-efficient method for determination of absolute configuration, which gives both the assignment and a measure of confidence in the outcome, even when the spectra are imperfect. The majority of molecules tested have a high confidence score and all of these have the correct assignment. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) benchmark studies of 1H and 13C NMR chemical shifts often yield differing conclusions, likely due to non-optimal test molecules and non-standardized data acquisition. To address this issue, we carefully selected and measured 1H and 13C NMR chemical shifts for 50 structurally diverse small organic molecules containing atoms from only the first two rows of the periodic table. Our NMR dataset, DELTA50, was used to calculate linear scaling factors and to evaluate the accuracy of 73 density functionals, 40 basis sets, 3 solvent models, and 3 gauge-referencing schemes. The best performing DFT methodologies for 1H and 13C NMR chemical shift predictions were WP04/6-311++G(2d,p) and ωB97X-D/def2-SVP, respectively, when combined with the polarizable continuum solvent model (PCM) and gauge-independent atomic orbital (GIAO) method. Geometries should be optimized at the B3LYP-D3/6-311G(d,p) level including the PCM solvent model for the best accuracy. Predictions of 20 organic compounds and natural products from a separate probe set had root-mean-square deviations (RMSD) of 0.07 to 0.19 for 1H and 0.5 to 2.9 for 13C. Maximum deviations were less than 0.5 and 6.5 ppm for 1H and 13C, respectively. [ABSTRACT FROM AUTHOR]

1H and 13C NMR chemical shifts of the tetrakis monoterpene indole alkaloid alasmontamine A, with a molecular formula of C84H91N8O12, have been calculated within the DFT framework. Six minimum energy conformers of this alkaloid were identified, and three key configurations that contribute to its NMR shielding constants were established. Several ambiguities in the reported assignment of the NMR chemical shifts of alasmontamine A have been resolved. [ABSTRACT FROM AUTHOR]

Hydrogen-bonding catalytic reactions have gained great interest. Herein, a hydrogen-bond-assisted three-component tandem reaction for the efficient synthesis of N-alkyl-4-quinolones is described. This novel strategy features the first proof of polyphosphate ester (PPE) as a dual hydrogen-bonding catalyst and the use of readily available starting materials for the preparation of N-alkyl-4-quinolones. The method provides a diversity of N-alkyl-4-quinolones in moderate to good yields. The compound 4h demonstrated good neuroprotective activity against N-methyl-ᴅ-aspartate (NMDA)-induced excitotoxicity in PC12 cells. [ABSTRACT FROM AUTHOR]

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed. [ABSTRACT FROM AUTHOR]

Structural elucidation has always been challenging, and misassignment remains a stringent issue in the field of natural products. The growing interest in discovering unknown, complex natural structures accompanies the increasing awareness concerning misassignments in the community. The combination of various spectroscopic methods with molecular modeling has gained popularity in recent years. In this work, we demonstrated, for the first time, its power to fully elucidate the 2-dimensional and 3-dimensional structures of two epimers in an epimeric mixture of 6-hydroxyhippeastidine. DFT calculation of chemical shifts was first performed to assist the assignment of planar structures. Furthermore, relative and absolute configurations were established by three different ways of computer-assisted structure elucidation (CASE) coupled with ORD/ECD/VCD spectroscopies. In addition, the significant added value of OR/ORD computations to relative and absolute configuration determination was also revealed. Remarkably, the differentiation of two enantiomeric scaffolds (crinine and haemanthamine) was accomplished via OR/ORD calculations with cross-validation by ECD and VCD. [ABSTRACT FROM AUTHOR]

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called "reference-free metabolomics" methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution. [ABSTRACT FROM AUTHOR]

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery. [ABSTRACT FROM AUTHOR]

Natural products have various and complicated structures, which is still a challenge for elucidating these compounds, especially for those lacking two-dimensional nuclear magnetic resonance (2D NMR) correlations mainly caused by high C/H ratios or proton-deficient and multiple heteroatoms through the conventional structural analytical methods. We reported a novel module-assembly calculation method named Dooerafa, which included constructing the meta-structures by a grafting method based on the crucial and the limited 2D NMR correlations, ring-contraction strategy based on mechanic force field and quantum chemical theory, and self-assemble calculation in Python programming for shaping up the structural candidates along with DFT-GIAO calculation. This new method, verified by a known alkaloid spiroreticulatine with the structure determined by X-ray diffraction, was performed for the structural elucidation of aaptourinamine isolated from marine sponge Aaptos suberitoides, showing us a brand new scaffold of imidazo [4,5,1-ij]pyrrolo [3,2-f]quinolin-7(8H)-one, which has a biosynthetic relationship with the bioactive and structurally unique aaptamine alkaloid. [ABSTRACT FROM AUTHOR]

Heterocycles are particularly common moieties within marine natural products. Specifically, tetrahydrofuranyl rings are present in a variety of compounds which present complex structures and interesting biological activities. Focusing on terpenoids, a high number of tetrahydrofuran-containing metabolites have been isolated during the last decades. They show promising biological activities, making them potential leads for novel antibiotics, antikinetoplastid drugs, amoebicidal substances, or anticancer drugs. Thus, they have attracted the attention of the synthetics community and numerous approaches to their total syntheses have appeared. Here, we offer the reader an overview of marine-derived terpenoids and related compounds, their isolation, structure determination, and a special focus on their total syntheses and biological profiles. [ABSTRACT FROM AUTHOR]

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%. [ABSTRACT FROM AUTHOR]

Over the past few decades (covering 1972 to 2022), astounding progress has been made in the elucidation of structures, bioactivities and biosynthesis of polyene macrolactams (PMLs), but they have only been partially summarized. PMLs possess a wide range of biological activities, particularly distinctive fungal inhibitory abilities, which render them a promising drug candidate. Moreover, the unique biosynthetic pathways including β-amino acid initiation and pericyclic reactions were presented in PMLs, leading to more attention from inside and outside the natural products community. According to current summation, in this review, the chem- and bio-diversity of PMLs from marine and terrestrial sources are considerably rich. A systematic, critical and comprehensive overview is in great need. This review described the PMLs' general structural features, production strategies, biosynthetic pathways and the mechanisms of bioactivities. The challenges and opportunities for the research of PMLs are also discussed. [ABSTRACT FROM AUTHOR]

Flexible marine natural products (MNPs), such as eribulin and bryostatin, play an important role in the development of modern marine drugs. However, due to the multiple chiral centers and geometrical uncertainty of flexible systems, configuration determinations of flexible MNPs face great challenges, which, in turn, have led to obstacles in druggability research. To resolve this issue, the comprehensive use of multiple methods is necessary. Additionally, configuration assignment methods, such as X-ray single-crystal diffraction (crystalline derivatives, crystallization chaperones, and crystalline sponges), NMR-based methods (JBCA and Mosher's method), circular dichroism-based methods (ECCD and ICD), quantum computational chemistry-based methods (NMR calculations, ECD calculations, and VCD calculations), and chemical transformation-based methods should be summarized. This paper reviews the basic principles, characteristics, and applicability of the methods mentioned above as well as application examples to broaden the research and applications of these methods and to provide a reference for the configuration determinations of flexible MNPs. [ABSTRACT FROM AUTHOR]

Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecul ar-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = −2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets. [ABSTRACT FROM AUTHOR]

Cyanobacteria produce a plethora of compounds with unique chemical structures and diverse biological activities. Importantly, the increasing availability of cyanobacterial genome sequences and the rapid development of bioinformatics tools have unraveled the tremendous potential of cyanobacteria in producing new natural products. However, the discovery of these compounds based on cyanobacterial genomes has progressed slowly as the majority of their corresponding biosynthetic gene clusters (BGCs) are silent. In addition, cyanobacterial strains are often slow-growing, difficult for genetic engineering, or cannot be cultivated yet, limiting the use of host genetic engineering approaches for discovery. On the other hand, genetically tractable hosts such as Escherichia coli, Actinobacteria, and yeast have been developed for the heterologous expression of cyanobacterial BGCs. More recently, there have been increased interests in developing model cyanobacterial strains as heterologous production platforms. Herein, we present recent advances in the heterologous production of cyanobacterial compounds in both cyanobacterial and noncyanobacterial hosts. Emerging strategies for BGC assembly, host engineering, and optimization of BGC expression are included for fostering the broader applications of synthetic biology tools in the discovery of new cyanobacterial natural products. [ABSTRACT FROM AUTHOR]

The enantiomeric forms of the alcohol (2,6,6-trimethyltetrahydro-2H-pyran-2-yl)methanol are potential chiral building blocks for the stereoselective synthesis of different natural terpenes. Here, we describe their preparation by means of two different synthetic approaches. The first is based on the stereospecific (+)-10-camphorsulfonic acid (CSA)-catalyzed cyclization of (R)- and (S)-2-methyl-5-(2-methyloxiran-2-yl)pentan-2-ol, which were in turn synthesized from (R)- and (S)-linalool, respectively. The latter monoterpenes are easily available from the chiral pool, with different optical purity. As our synthesis makes use of the intermediate 2,6-dimethyloct-7-ene-2,6-diol, whose enantiopurity can be improved through fractional crystallization, we obtained (2,6,6-trimethyltetrahydro-2H-pyran-2-yl)methanol enantiomers in an almost enantiopure form. The second synthetic approach is based on the lipase-mediated resolution of the aforementioned tetrahydropyranyl alcohol, which was prepared in racemic form starting from the industrial intermediate, dehydrolinalool. In this work, we report a large-scale resolution procedure that exploits the opposite enantioselectivity of Novozym® 435 lipase and lipase AK in the acetylation reaction of (2,6,6-trimethyltetrahydro-2H-pyran-2-yl)methanol. The two enantiomeric forms of the latter alcohol were employed for the first stereoselective synthesis of both enantiomers of the flavor, linaloyl oxide (2,2,6-trimethyl-6-vinyltetrahydro-2H-pyran). [ABSTRACT FROM AUTHOR]

Tetrahydropyrans are structural motifs that are abundantly present in a range of biologically important marine natural products. As such, significant efforts have been paid to the development of efficient and versatile methods for the synthesis of tetrahydropyran derivatives. Neopeltolide, a potent antiproliferative marine natural product, has been an attractive target compound for synthetic chemists because of its complex structure comprised of a 14-membered macrolactone embedded with a tetrahydropyran ring, and twenty total and formal syntheses of this natural product have been reported so far. This review summarizes the total and formal syntheses of neopeltolide and its analogues, highlighting the synthetic strategies exploited for constructing the tetrahydropyran ring. [ABSTRACT FROM AUTHOR]

Oxygen heterocycles are units that are abundant in a great number of marine natural products. Among them, marine polyketides containing tetrahydrofuran rings have attracted great attention within the scientific community due to their challenging structures and promising biological activities. An overview of the most important marine tetrahydrofuran polyketides, with a focused discussion on their isolation, structure determination, approaches to their total synthesis, and biological studies is provided. [ABSTRACT FROM AUTHOR]

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data. [ABSTRACT FROM AUTHOR]

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models. [ABSTRACT FROM AUTHOR]

Very little is known about chemical interactions between fungi and their mollusc host within marine environments. Here, we investigated the metabolome of a Penicillium restrictum MMS417 strain isolated from the blue mussel Mytilus edulis collected on the Loire estuary, France. Following the OSMAC approach with the use of 14 culture media, the effect of salinity and of a mussel-derived medium on the metabolic expression were analysed using HPLC-UV/DAD-HRMS/MS. An untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA) and molecular networking (MN). It highlighted some compounds belonging to sterols, macrolides and pyran-2-ones, which were specifically induced in marine conditions. In particular, a high chemical diversity of pyran-2-ones was found to be related to the presence of mussel extract in the culture medium. Mass spectrometry (MS)- and UV-guided purification resulted in the isolation of five new natural fungal pyran-2-one derivatives—5,6-dihydro-6S-hydroxymethyl-4-methoxy-2H-pyran-2-one (1), (6S, 1'R, 2'S)-LL-P880β (3), 5,6-dihydro-4-methoxy-6S-(1'S, 2'S-dihydroxy pent-3'(E)-enyl)-2H-pyran-2-one (4), 4-methoxy-6-(1'R, 2'S-dihydroxy pent-3'(E)-enyl)-2H-pyran-2-one (6) and 4-methoxy-2H-pyran-2-one (7)—together with the known (6S, 1'S, 2'S)-LL-P880β (2), (1'R, 2'S)-LL-P880γ (5), 5,6-dihydro-4-methoxy-2H-pyran-2-one (8), (6S, 1'S, 2'R)-LL-P880β (9), (6S, 1'S)-pestalotin (10), 1'R-dehydropestalotin (11) and 6-pentyl-4-methoxy-2H-pyran-2-one (12) from the mussel-derived culture medium extract. The structures of 1-12 were determined by 1D- and 2D-MMR experiments as well as high-resolution tandem MS, ECD and DP4 calculations. Some of these compounds were evaluated for their cytotoxic, antibacterial, antileishmanial and in-silico PTP1B inhibitory activities. These results illustrate the utility in using host-derived media for the discovery of new natural products. [ABSTRACT FROM AUTHOR]

Leptolyngbya, a well-known genus of cyanobacteria, is found in various ecological habitats including marine, fresh water, swamps, and rice fields. Species of this genus are associated with many ecological phenomena such as nitrogen fixation, primary productivity through photosynthesis and algal blooms. As a result, there have been a number of investigations of the ecology, natural product chemistry, and biological characteristics of members of this genus. In general, the secondary metabolites of cyanobacteria are considered to be rich sources for drug discovery and development. In this review, the secondary metabolites reported in marine Leptolyngbya with their associated biological activities or interesting biosynthetic pathways are reviewed, and new insights and perspectives on their metabolic capacities are gained. [ABSTRACT FROM AUTHOR]

In this work, I have analyzed the structure of binary mixtures of 1-butyl-3-methylimidazolium chloride ionic liquid, [C4C1im]Cl, and water, using computational NMR spectroscopy. The structure of the complex fluid phase, where the ionic and hydrophobic nature of ionic liquids is further complicated by the addition of water, is first generated by classical Molecular Dynamics (MD) and then validated by calculating the NMR properties with DFT at the ONIOM(B3LYP/cc-pVTZ//B3LYP/3-21G) on clusters extracted during the MD trajectories. Three ionic liquid/water mixtures have been considered with the [C4C1im]Cl mole fraction of 1.00, 0.50, and 0.01, that is the pure ionic liquid [C4C1im]Cl, the equimolar [C4C1im]Cl/water mixture, and a diluted solution of [C4C1im]Cl in water. A good agreement is obtained with published experimental data that, at the same time, validates the structural features obtained from the MD and the force field used, and provides an example of the power of NMR spectroscopy applied to complex fluid phases. [ABSTRACT FROM AUTHOR]

Chemical profiling of the Streptomyces sp. strain SUD119, which was isolated from a marine sediment sample collected from a volcanic island in Korea, led to the discovery of three new metabolites: donghaecyclinones A–C (1–3). The structures of 1–3 were found to be rearranged, multicyclic, angucyclinone-class compounds according to nuclear magnetic resonance (NMR) and mass spectrometry (MS) analyses. The configurations of their stereogenic centers were successfully assigned using a combination of quantum mechanics–based computational methods for calculating the NMR shielding tensor (DP4 and CP3) as well as electronic circular dichroism (ECD) along with a modified version of Mosher's method. Donghaecyclinones A–C (1–3) displayed cytotoxicity against diverse human cancer cell lines (IC50: 6.7–9.6 μM for 3). [ABSTRACT FROM AUTHOR]

Saturated oxygen heterocycles are widely found in a broad array of natural products and other biologically active molecules. In medicinal chemistry, small and medium rings are also important synthetic intermediates since they can undergo ring-opening and -expansion reactions. These applications have driven numerous studies on the synthesis of oxygen-containing heterocycles and considerable effort has been devoted toward the development of methods for the construction of saturated oxygen heterocycles. This paper provides an overview of the biological roles and synthetic strategies of saturated cyclic ethers, covering some of the most studied and newly discovered related natural products in recent years. This paper also reports several promising and newly developed synthetic methods, emphasizing 3–7 membered rings. [ABSTRACT FROM AUTHOR]

There is demand for safer and bio-based solvents, brought on by legislation and sustainability objectives. The prediction of physical properties is highly desirable to help design new molecules. Here we present an in silico approach to obtain calculated Kamlet–Abboud–Taft solvatochromic parameters using virtual experiments. The tautomerisation equilibrium of methyl acetoacetate and dimedone was calculated in different solvents with COSMO-RS theory and converted into estimates of solvent dipolarity and hydrogen bond accepting ability, respectively. Hydrogen bond donating ability was calculated as a function of the electron deficient surface area on protic solvents. These polarity descriptors correlate with rate constants and equilibria, and so ability of calculated Kamlet–Abboud–Taft solvatochromic parameters to recreate experimental free energy relationships was tested with sixteen case studies taken from the literature. The accuracy of the calculated parameters was also satisfactory for solvent selection, as demonstrated with a 1,4-addition reaction and a multicomponent heterocycle synthesis. [ABSTRACT FROM AUTHOR]

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries. [ABSTRACT FROM AUTHOR]