High-Throughput in Silico Structure Validation and Revision of Halogenated Natural Products Is Enabled by Parametric Corrections to DFT-Computed 13C NMR Chemical Shifts and Spin-Spin Coupling Constants

Triantaspirols A-C and Paraphaeolactone Cs from Paraphaeosphaeria sp. KT4192: Sensitivity of CP3 in Distinguishing Close NMR Signals

Hybridized spirobisnaphthalene derivatives, triantaspirols A-C (1-3) and paraphaeolactones C1 and C2 (4 and 5), were identified from the culture broth of the fungus Paraphaeosphaeria sp. KT4192. The NMR spectra of 2 and 3, as well as 4 and 5, closely resembled each other, indicating that these were pairs of diastereomers. Although this NMR spectral resemblance made it challenging to distinguish their relative configurations, detailed analysis of the electronic circular dichroism (ECD) spectra and NOE correlations allowed us to deduce them. The CP3 metric with the DFT-based NMR chemical shifts was found to distinguish configurations of diastereomers in a highly sensitive and accurate manner that DP4 could not account for because of the very close chemical shift differences in the experimental NMR spectra. The reliability of this method was assessed using 23 published examples which could not be distinguished by DP4 protocol.

molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction

The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

Empirical Chiroptical Analyses of Vicinal Bromochloro Natural Products by van't Hoff's Principle of Optical Superposition: Assignment of the C-16 Configurations of Callophycols A and B

A simple empirical method is described that allows the assignment of absolute configurations of natural products containing chiral vicinal bromochloro (VBC) units, including the bromochloro substituted isoprenyl units present in the structures of antiproliferative halomon (1a) and its halogen-swapped isomer iso-halomon (1b) from the red alga, Portieria hornemannii, and callophycols A (3) and B (4) from Callophycus serratus. The relative configurations of 3 and 4, published in 2007, were incomplete: C-16 was left unassigned. It is now shown that the additivity of molar rotations, [M]D (herein, abbreviated [M])─a consequence of van't Hoff's principle of optical superposition─could be used to deconvolute rotatory contributions, designated as [MX] and [MY] of the two remotely spaced chiral substructures within 3 and 4 using simple arithmetic. Input of proxy values, [M Y1] and [MY2], for the two different VBC units in two equations for [MX] and application of a "conditional test" returns the same value for [MX] only when a proxy with the correct configuration is included. It is revealed that 3 and 4 have opposite configurations at the C-16 stereocenter: 16S and 16R, respectively. Two important implications lie in these findings: 3 and 4 appear to qualify as paired-regioisomers, coupled through a putative dyotropic rearrangement (DR), and the biosyntheses of other Callophycus secondary metabolites, now numbering over 50, are tightly controlled by stereoelectronic considerations including neighboring group interactions of the DR. It now appears, counter to earlier suggestions, that the chirality of Callophycus secondary metabolites, despite their high chemodiversity, are surprisingly highly conserved. Enantiofacial halogenation additions to the C═C double bonds of precursor alkenes appear to direct the formation of the remaining stereocenters at both the halogenated benzoate-decalin core and the distal VBC of 3 and 4. A consistent hypothesis is proposed to account for macrolactonizations in other Callophycus natural products including bromophycolides A and B. The conditional test of molar rotations was applied in a different context to understand the chiroptical properties and trends observed in the highly iodinated meroditerpenes, iodocallophycols A-E, also from Callophycus sp., resulting in the revision of the configuration of callophycol E from (10R,14R) to (10S,14S).

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

HSQC Spectra Simulation and Matching for Molecular Identification

In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git).

Study of heavy atom influence on poly-halogenated compounds using DP4/MM-DP4+/DP4+: insights and trends

Nuclear magnetic resonance (NMR) spectroscopy complemented by density functional theory (DFT) calculations is a crucial tool for structural elucidation. Nevertheless, the precision of NMR predictions is influenced by the 'heavy atom effect', wherein heavy atoms affect the shielding values of neighboring light atoms (HALA effect). Standard practice in the field involves removing the conflicting signals. However, in the case of polyhalogenated molecules, this is challenging due to the significant amount of information that ends up being lost. In this study the HALA is thoroughly investigated in the context of three leading probability methods: DP4, MM-DP4+, and DP4+. The results show that DP4+ is more sensitive to C-Cl or C-Br signals, which is a consequence of the longer bond lengths computed with DFT. Removing conflicting signals is highly effective in DP4+, but has an uncertain outcome in methods based on molecular mechanics geometries, such as DP4 and MM-DP4+. A detailed investigation of the effect of bond distance on the corresponding chemical shifts has also been conducted.

The accuracy limit of chemical shift predictions for species in aqueous solution

Interpreting NMR experiments benefits from first-principles predictions of chemical shifts. Reaching the accuracy limit of theory is relevant for unambiguous structural analysis and dissecting theoretical approximations. Since accurate chemical shift measurements are based on using internal reference compounds such as trimethylsilylpropanesulfonate (DSS), a detailed comparison of experimental with theoretical data requires simultaneous consideration of both target and reference species ensembles in the same solvent environment. Here we show that ab initio molecular dynamics simulations to generate liquid-state ensembles of target and reference compounds, including explicitly their short-range solvation environments and combined with quantum-mechanical solvation models, allows for predicting highly accurate 1H (∼0.1-0.5 ppm) and aliphatic 13C (∼1.5 ppm) chemical shifts for aqueous solutions of the model compounds trimethylamine N-oxide (TMAO) and N-methylacetamide (NMA), referenced to DSS without any system-specific adjustments. This encompasses the two peptide bond conformations of NMA identified by NMR. The results are used to derive a general-purpose guideline set for predictive NMR chemical shift calculations of NMA in the liquid state and to identify artifacts of force field models. Accurate predictions are only obtained if a sufficient number of explicit water molecules is included in the quantum-mechanical calculations, disproving a purely electrostatic model of the solvent effect on chemical shifts.

Machine learning-based correction for spin-orbit coupling effects in NMR chemical shift calculations

As one of the most powerful analytical methods for molecular and solid-state structure elucidation, NMR spectroscopy is an integral part of chemical laboratories associated with a great research interest in its computational simulation. Particularly when heavy atoms are present, a relativistic treatment is essential in the calculations as these influence also the nearby light atoms. In this work, we present a Δ-machine learning method that approximates the contribution to 13C and 1H NMR chemical shifts that stems from spin-orbit (SO) coupling effects. It is built on computed reference data at the spin-orbit zeroth-order regular approximation (ZORA) DFT level for a set of 6388 structures with 38 740 13C and 64 436 1H NMR chemical shifts. The scope of the methods covers the 17 most important heavy p-block elements that exhibit heavy atom on the light atom (HALA) effects to covalently bound carbon or hydrogen atoms. Evaluated on the test data set, the approach is able to recover roughly 85% of the SO contribution for 13C and 70% for 1H from a scalar-relativistic PBE0/ZORA-def2-TZVP calculation at virtually no extra computational costs. Moreover, the method is transferable to other baseline DFT methods even without retraining the model and performs well for realistic organotin and -lead compounds. Finally, we show that using a combination of the new approach with our previous Δ-ML method for correlation contributions to NMR chemical shifts, the mean absolute NMR shift deviations from non-relativistic DFT calculations to experimental values can be halved.

The Synergy between Nuclear Magnetic Resonance and Density Functional Theory Calculations

This paper deals with the synergy between Nuclear Magnetic Resonance (NMR) spectroscopic investigations and DFT calculations, mainly of NMR parameters. Both the liquid and the solid states are discussed here. This text is a mix of published results supplemented with new findings. This paper deals with examples in which useful results could not have been obtained without combining NMR measurements and DFT calculations. Examples of such cases are tautomeric systems in which NMR data are calculated for the tautomers; hydrogen-bonded systems in which better XH bond lengths can be determined; cage compounds for which assignment cannot be made based on NMR data alone; revison of already published structures; ionic compounds for which reference data are not available; assignment of solid-state spectra and crystal forms; and the creation of libraries for biological molecules. In addition to these literature cases, a revision of a cage structure and substituent effects on pyrroles is also discussed.

Penicitone: Structural Reassignment of a Proposed Natural Product Acid Chloride

The proposed structure for the natural product penicitone, which contained a chemically improbable acid chloride functional group, was reassigned to a more probable structure using a combination of chemical knowledge, computer-assisted structure elucidation, and DFT methods.

DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation

DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in certain cases. In this work, we made a substantial improvement to these limitations. First, we deeply explored the effect of molecular mechanics architecture on the DP4+ formalism (MM-DP4+). In addition, a Python applet (DP4+App) was developed to automate the entire process, requiring only the Gaussian NMR output files and a spreadsheet containing the experimental NMR data and labels. The script is designed to use the statistical parameters from the original 24 levels of theory (employing B3LYP/6-31G* geometries) and the new 36 levels explored in this work (over MMFF geometries). Furthermore, it enables the development of customizable methods using any desired level of theory, allowing for a free choice of test molecules.

MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids

We developed a random forest machine learning (ML) model for the prediction of 1H and 13C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 1H chemical shifts and 1780 13C chemical shifts on the 10 nucleic acids). The ML model is then used to make chemical shift predictions on 8 new nucleic acids ranging in size from 600 to 900 atoms and compared directly to experimental data. Though no experimental data was used in the training, the performance of our model is excellent (mean absolute deviation of 0.34 ppm for 1H chemical shifts and 2.52 ppm for 13C chemical shifts for the test set), despite having some nonstandard structures. A simple analysis suggests that both structural and electronic descriptors are critical for achieving reliable predictions. This is the first attempt to combine ML from fragment-based DFT calculations to predict experimental chemical shifts accurately, making the MIM-ML model a valuable tool for NMR predictions of nucleic acids.

The Ethics of Publishing Biomedical and Natural Products Research

Given that the essence of Science is a search for the truth, one might expect that those identifying as scientists would be conscientious and observant of the demands this places on them. However, that expectation is not fulfilled universally as, not too surprisingly, egregious examples of unethical behavior appear and are driven by money, personal ambition, performance pressure, and other incentives. The reproducibility-, fact-, and truth-oriented modus operandi of Science has come to face a variety of challenges. Organized into 11 cases, this article outlines examples of compromised integrity from borderline to blatant unethical behavior that disgrace our profession unnecessarily. Considering technological developments in neural networks/artificial intelligence, a host of factors are identified as impacting Good Ethical Practices. The goal is manifold: to raise awareness and offer perspectives for refocusing on Science and true scientific evidence; to trigger discussion and developments that strengthen ethical behavior; to foster the recognition of the beauty, simplicity, and rewarding nature of scientific integrity; and to highlight the originality of intelligence.

Green Halogenation of Indoles with Oxone-Halide

Oxidative functionalization of indoles is one of the most widely used approaches to exploit the synthetic utility of indoles. In continuation of our research interest in the green oxidation of indoles, we further explore the oxidation of indoles with oxone-halide and discover that the protecting group on the nitrogen of indoles plays a decisive role in controlling the pathways of indole oxidation with oxone-halide. An electron-withdrawing group on the nitrogen of indoles (N-EWG) enables C2 halogenation with stoichiometric halide, while C3 halogenation could be selectively achieved by using stoichiometric halide without dependence on the electronic property of the protecting group on the indole nitrogen. Different from our previous results obtained by using catalytic halide, these findings lead to the development of an environmentally friendly, efficient, and mild protocol for access to 2- or 3-haloindoles (chloro and bromo). As compared to the previous synthetic methods for 2-/3-haloindoles, our method exploits the in situ-generated reactive halogenating species from oxone-halide for halogenation of indoles and thus eliminates the use of stoichiometric halogenating agents and the production of toxic and hazardous organic byproducts derived from oxidants.

Combined NMR Spectroscopy and Quantum-Chemical Calculations in Fluorescent 1,2,3-Triazole-4-carboxylic Acids Fine Structures Analysis

The peculiarities of the optical properties of 2-aryl-1,2,3-triazole acids and their sodium salts were investigated in different solvents (1,4-dioxane, dimethyl sulfoxide DMSO, methanol MeOH) and in mixtures with water. The results were discussed in terms of the molecular structure formed by inter- and intramolecular noncovalent interactions (NCIs) and their ability to ionize in anions. Theoretical calculations using the Time-Dependent Density Functional Theory (TDDFT) were carried out in different solvents to support the results. In polar and nonpolar solvents (DMSO, 1,4-dioxane), fluorescence was provided by strong neutral associates. Protic MeOH can weaken the acid molecules' association, forming other fluorescent species. The fluorescent species in water exhibited similar optical characteristics to those of triazole salts; therefore, their anionic character can be assumed. Experimental 1H and 13C-NMR spectra were compared to their corresponding calculated spectra using the Gauge-Independent Atomic Orbital (GIAO) method and several relationships were established. All these findings showed that the obtained photophysical properties of the 2-aryl-1,2,3-triazole acids noticeably depend on the environment and, therefore, are good candidates as sensors for the identification of analytes with labile protons.

Simple, accurate, adjustable-parameter-free prediction of NMR shifts for molecules in solution

Accurate prediction of NMR shifts is invaluable for interpreting and assigning NMR spectra, especially for complex applications such as determining the identity of unknown substances or resolving stereochemical assignments. Statistical linear regression models have proven effective for accurately correlating density functional theory predictions of chemical shieldings with experimentally-measured shifts, but lack transferability - they must be reparameterised using a reasonably extensive training set at each level of theory and for each choice of NMR solvent. We have previously introduced a novel two-point "shift-and-scale" correction procedure for gas phase shieldings that overcomes these limitations without significant loss of accuracy. In this work, we demonstrate that this approach is equally applicable for predicting solution-phase shifts from computed gas phase shieldings, using acetaldehyde as an experimentally and computationally convenient reference system. We also present all of the required experimental reference data to enable this approach to be used for any target analyte in a range of commonly used NMR solvents (chloroform, dichloromethane, acetonitrile, methanol, acetone, DMSO, D2O, benzene, pyridine).

seco-Pregnane Glycosides from Australian Caustic Vine (Cynanchum viminale subsp. australe)

Cynanchum viminale subsp. australe, more commonly known as caustic vine, is a leafless succulent that grows in the northern arid zone of Australia. Toxicity toward livestock has been reported for this species, along with use in traditional medicine and its potential anticancer activity. Disclosed herein are novel seco-pregnane aglycones cynavimigenin A (5) and cynaviminoside A (6), together with new pregnane glycosides cynaviminoside B (7) and cynavimigenin B (8). Cynavimigenin B (8) contains an unprecedented 7-oxobicyclo[2.2.1]heptane moiety in the seco-pregnane series, likely arising from a pinacol-type rearrangement. Interestingly, these isolates displayed only limited cytotoxicity in cancer and normal human cell lines, in addition to low activity against acetylcholinesterase and Sarcoptes scabiei bioassays, suggesting that 5-8 are not associated with the reported toxicity of this plant species.

Isolation and NMR Scaling Factors for the Structure Determination of Lobatolide H, a Flexible Sesquiterpene from Neurolaena lobata

A new flexible germacranolide (1, lobatolide H) was isolated from the aerial parts of Neurolaena lobata. The structure elucidation was performed by classical NMR experiments and DFT NMR calculations. Altogether, 80 theoretical level combinations with existing ¹³C NMR scaling factors were tested, and the best performing ones were applied on 1. ¹H and ¹³C NMR scaling factors were also developed for two combinations utilizing known exomethylene containing derivatives, and the results were complemented by homonuclear coupling constant (J_HH) and TDDFT-ECD calculations to elucidate the stereochemistry of 1. Lobatolide H possessed remarkable antiproliferative activity against human cervical tumor cell lines with different HPV status (SiHa and C33A), induced cell cycle disturbance and exhibited a substantial antimigratory effect in SiHa cells.

DELTA50: A Highly Accurate Database of Experimental 1H and 13C NMR Chemical Shifts Applied to DFT Benchmarking

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.

Triterpenoids from Uncaria macrophylla as ferroptosis inhibitors

Seven undescribed triterpenoids were obtained from the ethanol extract of the dried stems of Uncaria macrophylla Wall. (Rubiaceae).All of the isolates were urs-28-oic acid or olean-28-oic acid skeletons, including three triterpenoids with rare 3β,23-(1R-4-hydroxy-butyl-1,1-dioxy) or 23-(2R-tetrahydrofuran-2-oxy) substituents. Five triterpenoids showed promising inhibitory activity against erastin-induced ferroptosis in PC12 cells, while 3β,6α,23-trihydroxy-olean-12-en-28-oic acid was the most significant inhibitor to resist ferroptosis by activating the Nrf2/SLC7A11/GPx4 axis with an EC₅₀ value of 4.2 ± 0.7 μM.

Naturally Occurring Organohalogen Compounds-A Comprehensive Review

The present volume is the third in a trilogy that documents naturally occurring organohalogen compounds, bringing the total number-from fewer than 25 in 1968-to approximately 8000 compounds to date. Nearly all of these natural products contain chlorine or bromine, with a few containing iodine and, fewer still, fluorine. Produced by ubiquitous marine (algae, sponges, corals, bryozoa, nudibranchs, fungi, bacteria) and terrestrial organisms (plants, fungi, bacteria, insects, higher animals) and universal abiotic processes (volcanos, forest fires, geothermal events), organohalogens pervade the global ecosystem. Newly identified extraterrestrial sources are also documented. In addition to chemical structures, biological activity, biohalogenation, biodegradation, natural function, and future outlook are presented.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

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%.

Computational 1 H and 13 C NMR in structural and stereochemical studies

Present review outlines the advances and perspectives of computational ¹ H and ¹³ C NMR applied to the stereochemical studies of inorganic, organic, and bioorganic compounds, involving in particular natural products, carbohydrates, and carbonium ions. The first part of the review briefly outlines theoretical background of the modern computational methods applied to the calculation of chemical shifts and spin-spin coupling constants at the DFT and the non-empirical levels. The second part of the review deals with the achievements of the computational ¹ H and ¹³ C NMR in the stereochemical investigation of a variety of inorganic, organic, and bioorganic compounds, providing in an abridged form the material partly discussed by the author in a series of parent reviews. Major attention is focused herewith on the publications of the recent years, which were not reviewed elsewhere.

Reassignment of the structures of pestalopyrones A-D

Pestalopyrones A-D are four unusual tricyclic pyrone derivatives with flexible chiral structures, isolated from the endophytic fungus Pestalotiopsis neglecta S3. The full elucidation of their structures was a challenging task, and remained unsolved in the original article. Herein, the relative configurations of pestalopyrones A and pestalopyrones B were unambiguously assigned by detailed analyses on spectroscopic data and GIAO ¹³C NMR calculation method with sorted training sets (STS). The planar structures of pestalopyrones C and pestalopyrones D were revised by reinterpretation of their reported spectroscopic data, and then their relative configurations were deduced by STS GIAO ¹³C NMR calculation and NOE analysis. The absolute configurations of all the mentioned compounds were determined by the comparison of their experimental and calculated ECD curves.

Peculiar Reaction Products and Mechanisms Revisited with Machine Learning-Augmented Computational NMR

DU8ML, a fast and accurate machine learning-augmented density functional theory (DFT) method for computing nuclear magnetic resonance (NMR) spectra, proved effective for high-throughput revision of misassigned natural products. In this paper, we disclose another important aspect of its application: correction of unusual reaction mechanisms originally proposed because of incorrect product structures.

Pitfalls in the structural elucidation of small molecules. A critical analysis of a decade of structural misassignments of marine natural products

Covering: July 2010 to August 2021This article summarizes more than 200 cases of misassigned marine natural products reported between July 2010 and August 2021, sorting out errors according to the structural elements. Based on a comparative analysis of the original and the revised structures, major pitfalls still plaguing the structural elucidation of small molecules were identified, emphasizing the role of total synthesis, crystallography, as well as chemical- and biosynthetic logic to complement spectroscopic data. Distinct "trends" in natural product misassignment are evident between compounds of marine and plant origin, with an overall much lower incidence of "impossible" structures within misassigned marine natural products.

ML-J-DP4: An Integrated Quantum Mechanics-Machine Learning Approach for Ultrafast NMR Structural Elucidation

A new tool, ML-J-DP4, provides an efficient and accurate method for determining the most likely structure of complex molecules within minutes using standard computational resources. The workflow involves combining fast Karplus-type J calculations with NMR chemical shifts predictions at the cheapest HF/STO-3G level enhanced using machine learning (ML), all embedded in the J-DP4 formalism. Our ML provides accurate predictions, which compare favorably alongside with other ML methods.

Identification of a poly-cyclopropylglycine-containing peptide via bioinformatic mapping of radical S-adenosylmethionine enzymes

Peptide-derived natural products are a large class of bioactive molecules that often contain chemically challenging modifications. In the biosynthesis of ribosomally synthesized and posttranslationally modified peptides (RiPPs), radical-SAM (rSAM) enzymes have been shown to catalyze the formation of ether, thioether, and carbon-carbon bonds on the precursor peptide. The installation of these bonds typically establishes the skeleton of the mature RiPP. To facilitate the search for unexplored rSAM-dependent RiPPs for the community, we employed a bioinformatic strategy to screen a subfamily of peptide-modifying rSAM enzymes which are known to bind up to three [4Fe-4S] clusters. A sequence similarity network was used to partition related families of rSAM enzymes into >250 clusters. Using representative sequences, genome neighborhood diagrams were generated using the Genome Neighborhood Tool. Manual inspection of bacterial genomes yielded numerous putative rSAM-dependent RiPP pathways with unique features. From this analysis, we identified and experimentally characterized the rSAM enzyme, TvgB, from the tvg gene cluster from Halomonas anticariensis. In the tvg gene cluster, the precursor peptide, TvgA, is comprised of a repeating TVGG motif. Structural characterization of the TvgB product revealed the repeated formation of cyclopropylglycine, where a new bond is formed between the γ-carbons on the precursor valine. This novel RiPP modification broadens the functional potential of rSAM enzymes and validates the proposed bioinformatic approach as a practical broad search tool for the discovery of new RiPP topologies.

Radical SAM-dependent ether crosslink in daropeptide biosynthesis

Darobactin is a ribosomally synthesized and post-translationally modified peptide (RiPP), which possesses potent activity against various Gram-negative bacteria. Darobactin features a highly unique bicyclic scaffold, consisting of an ether crosslink between two Trp residues and a C-C crosslink between a Lys and a Trp. Here we report in vivo and in vitro activity of darobactin synthase DarE. We show DarE is a radical S-adenosylmethionine (rSAM) enzyme and is solely responsible for forming the bicyclic scaffold of darobactin. DarE mainly produced the ether-crosslinked product in vitro, and when the assay was performed in H₂¹⁸O, apparent ¹⁸O incorporation was observed into the ether-crosslinked product. These observations suggested an rSAM-dependent process in darobactin biosynthesis, involving a highly unusual oxygen insertion step from a water molecule and subsequent O-H and C-H activations. Genome mining analysis demonstrates the diversity of darobactin-like biosynthetic gene clusters, a subclade of which likely encode monocyclic products with only an ether linkage. We propose the name daropeptide for this growing family of ether-containing RiPPs produced by DarE enzymes.

DU8ML: Machine Learning-Augmented Density Functional Theory Nuclear Magnetic Resonance Computations for High-Throughput In Silico Solution Structure Validation and Revision of Complex Alkaloids

Machine learning (ML) profoundly improves the accuracy of the fast DU8+ hybrid density functional theory/parametric computations of nuclear magnetic resonance spectra, allowing for high throughput in silico validation and revision of complex alkaloids and other natural products. Of nearly 170 alkaloids surveyed, 35 structures are revised with the next-generation ML-augmented DU8 method, termed DU8ML.

An inexpensive density functional theory-based protocol to predict accurate 19 F-NMR chemical shifts

Thanks to its advantages, ¹⁹ F-NMR is an increasingly popular technique for the structural characterization of F-containing molecules, among which polymers, materials, fluorophores, pharmaceuticals, and so forth. However, the computational calculation of the ¹⁹ F-NMR chemical shifts, both for prediction and interpretation of experimental spectra, remains a challenge. In this work a density functional theory (DFT) based protocol for the calculation of the chemical shifts is established within the framework of the gauge-independent atomic orbital method, upon verifying the performance of Hartree-Fock and 60 DFT functionals coupled with seven different basis sets. The benchmark is conducted using two sets of molecules, namely one used for testing methods and another used for probing; the former set consists of 134 molecules, the latter 50, yet both of them with F in different chemical environments. Following Bally-Rablen-Tantillo strategy, the scaling parameters and other statistical quantities were computed for each method upon least squares linear regression between experimental and computed chemical shifts. The designed computational workflow is computationally inexpensive and represents a significant improvement with respect to the current state of the art.

DU8+ Computations Reveal a Common Challenge in the Structure Assignment of Natural Products Containing a Carboxylic Anhydride Moiety

DU8+ computations of NMR spectra revealed a relatively common error in the structure assignment of carboxylic anhydride-containing natural products. Computationally driven revisions of ten of these structures are reported in this Note. The majority of the misassigned structures featured a hydroxy group that is proximal to the proposed anhydride moiety and capable of lactone formation.

Maximizing Step-Normalized Increases in Molecular Complexity: Formal [4+2+2+2] Photoinduced Cyclization Cascade to Access Polyheterocycles Possessing Privileged Substructures

A new complexity building photoinduced cascade which amounts to an unprecedented formal [4+2+2+2] cycloaddition topology is developed to access complex nitrogen polyheterocycles. This photocascade is initiated by the excited state intramolecular proton transfer (ESIPT) in aromatic amino ketones with tethered dual unsaturated pendants, i.e. pyrrole and alkenic moieties, resulting in the formation of four σ-bonds and setting six new stereogenic centers in a single experimentally simple photochemical step with up to 220 mcbit complexity increases.

Do Double-Hybrid Exchange-Correlation Functionals Provide Accurate Chemical Shifts? A Benchmark Assessment for Proton NMR

A benchmark density functional theory (DFT) study of ¹H NMR chemical shifts for data sets comprising 200 chemical shifts, including complex natural products, has been carried out to assess the performance of DFT methods. Two new benchmark data sets, NMRH33 and NMRH148, have been established. The meta-GGA revTPSS performs remarkably well against the NMRH33 benchmark set (mean absolute deviation (MAD), 0.10 ppm; maximum deviation (max), 0.26 ppm) with the smallest MAD of all evaluated functionals. The best-performing double-hybrid density functional (DHDF), revDSD-BLYP (MAD, 0.16 ppm; max, 0.35 ppm), performs similarly to hybrid-GGA methods (e.g., mPW1PW91/6-311G(d) (MAD, 0.15 ppm; max, 0.36 ppm)), but at a considerably higher computational cost. The results indicate that currently available double-hybrid DFT methods offer no benefit over GGA (including hybrid and meta) functionals in the calculation of ¹H NMR chemical shifts.

Nickel(II)-Based Building Blocks with Schiff Base Derivatives: Experimental Insights and DFT Calculations

We have recently reported a series of neutral square planar tridentate Schiff base (L) complexes of the general formula [(L)M(py)], showing relatively high first-order hyperpolarizabilities and NLO redox switching behavior. In the present study, new members of this family of compounds have been prepared with the objective to investigate their potential as building blocks in the on-demand construction of D-π-A push–pull systems. Namely, ternary nickel(II) building blocks of general formula [(L^A/D)Ni(4-pyX)] (4–7), where L^A/D stands for an electron accepting or donating dianionic O,N,O-tridentate Schiff base ligand resulting from the monocondensation of 2-aminophenol or its 4-substituted nitro derivative and β-diketones R-C(=O)CH₂C(=O)CH₃ (R = methyl, anisyl, ferrocenyl), and 4-pyX is 4-iodopyridine or 4-ethynylpyridine, were synthesized and isolated in 60–78% yields. Unexpectedly, the Sonogashira cross-coupling reaction between the 4-iodopyridine derivative 6 and 4-ethynylpyridine led to the formation of the bis(4-pyridyl) acetylene bridged centrosymmetric dimer [{(L^D)Ni}₂(µ²-py-C≡C-py)] (8). Complexes 4–8 were characterized by elemental analysis, FT-IR and NMR spectroscopy, single crystal X-ray diffraction and computational methods. In each compound, the four-coordinate Ni(II) metal ion adopts a square planar geometry with two nitrogen and two oxygen atoms as donors occupying trans positions. In 8, the Ni…Ni separation is of 13.62(14) Å. Experimental results were proved and explained theoretically exploiting Density Functional Theory calculations.

Pseudocyclic Form of 4-Hydroxypyrrolidine-2-carboxanilide Podands with Trioxyethylene Chain: Modeling, Conformational Search, and NMR Analysis

The 4-hydroxypyrrolidine-2-carboxanilide podand salt demonstrates catalytic activity in asymmetric Biginelli reaction. The systematic search for prevalent conformational state of the cation was carried out by computer simulations in combination with one- and two-dimensional NMR experiments. For that purpose, we proposed a novel algorithm for the generation and selection of conformers based on molecular dynamics and clustering in the space of principal components. The search had found an important trend of the podand to form a pseudocyclic structure with a horseshoe-shaped conformation of the oligooxyethylene fragment. This conformation is stabilized by different types of intramolecular hydrogen bonds between the acidic and basic centers of the two 4-hydroxypyrrolidine-2-carboxanilide residuals (branches). The proposed approach had made it possible to identify the major structural factors, providing a correlation between the calculated and experimental chemical shifts of hydrogen atoms in the ¹H NMR spectra of the protonated podand.

A critical review on the use of DP4+ in the structural elucidation of natural products: the good, the bad and the ugly. A practical guide

Covering: 2015 up to the end of 2020Even in the golden age of NMR, the number of natural products being incorrectly assigned is becoming larger every day. The use of quantum NMR calculations coupled with sophisticated data analysis provides ideal complementary tools to facilitate the elucidation process in challenging cases. Among the current computational methodologies to perform this task, the DP4+ probability is a popular and widely used method. This updated version of Goodman's DP4 synergistically combines NMR calculations at higher levels of theory with the Bayesian analysis of both scaled and unscaled data. Since its publication in late 2015, the use of DP4+ to solve controversial natural products has substantially grown, with several predictions being confirmed by total synthesis. To date, the structures of more than 200 natural products were determined with the aid of DP4+. However, all that glitters is not gold. Besides its intrinsic limitations, on many occasions it has been improperly used with potentially important consequences on the quality of the assignment. Herein we present a critical revision on how the scientific community has been using DP4+, exploring the strengths of the method and how to obtain optimal results from it. We also analyze the weaknesses of DP4+, and the paths to by-pass them to maximize the confidence in the structural elucidation.

NMR of Natural Products as Potential Drugs

This review outlines methods to investigate the structure of natural products with emphasis on intramolecular hydrogen bonding, tautomerism and ionic structures using NMR techniques. The focus is on 1H chemical shifts, isotope effects on chemical shifts and diffusion ordered spectroscopy. In addition, density functional theory calculations are performed to support NMR results. The review demonstrates how hydrogen bonding may lead to specific structures and how chemical equilibria, as well as tautomeric equilibria and ionic structures, can be detected. All these features are important for biological activity and a prerequisite for correct docking experiments and future use as drugs.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.