Computer-assisted methods for molecular structure elucidation: realizing a spectroscopist's dream

Automated nuclear magnetic resonance fingerprinting of mixtures

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

Model-Free Approach for the Configurational Analysis of Marine Natural Products

The NMR-based configurational analysis of complex marine natural products is still not a routine task. Different NMR parameters are used for the assignment of the relative configuration: NOE/ROE, homo- and heteronuclear J couplings as well as anisotropic parameters. The combined distance geometry (DG) and distance bounds driven dynamics (DDD) method allows a model-free approach for the determination of the relative configuration that is invariant to the choice of an initial starting structure and does not rely on comparisons with (DFT) calculated structures. Here, we will discuss the configurational analysis of five complex marine natural products or synthetic derivatives thereof: the cis-palau’amine derivatives 1a and 1b, tetrabromostyloguanidine (1c), plakilactone H (2), and manzamine A (3). The certainty of configurational assignments is evaluated in view of the accuracy of the NOE/ROE data available. These case studies will show the prospective breadth of application of the DG/DDD method.

Chemistry in Times of Artificial Intelligence

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.

Structure reassignment of two triterpenes with CASE algorithms and DFT chemical shift predictions

Two triterpenes (14S,17S,20S,24R)-25-hydroxy-14,17-cyclo-20,24-epoxy-malabarican-3-one (CEM, 1a) and (14S,17S,20S,24R)-20,24,25-trihydroxy-14,17-cyclomalabarican-3-one (CM, 2a) with a cyclobutane ring were reported, which have the same NMR data as ocotillone (1b) and gardaubryone C (2b), respectively. An incorrect structure might be reported. Therefore, the structure reanalysis of these triterpenes was achieved by CASE algorithm and DFT chemical shift predictions, and the results showed that the structures of CEM and CM might be incorrect. To further verify the structure of compound 1, the HMBC, ¹H-¹H COSY and HSQC-TOCSY spectra were employed. Herein, we revised the structure of CEM and CM, and our study also showed that CASE algorithm and DFT chemical shift predictions can hold the post of effective structure reassignment method.

Antibiotic drug discovery: Challenges and perspectives in the light of emerging antibiotic resistance

Amid a rising threat of antimicrobial resistance in a global scenario, our huge investments and high-throughput technologies injected for rejuvenating the key therapeutic scaffolds to suppress these rising superbugs has been diminishing severely. This has grasped world-wide attention, with increased consideration being given to the discovery of new chemical entities. Research has now proven that the relatively tiny and simpler microbes possess enhanced capability of generating novel and diverse chemical constituents with huge therapeutic leads. The usage of these beneficial organisms could help in producing new chemical scaffolds that govern the power to suppress the spread of obnoxious superbugs. Here in this review, we have explicitly focused on several appealing strategies employed for the generation of new chemical scaffolds. Also, efforts on providing novel insights on some of the unresolved questions in the production of metabolites, metabolic profiling and also the serendipity of getting "hit molecules" have been rigorously discussed. However, we are highly aware that biosynthetic pathway of different classes of secondary metabolites and their biosynthetic route is a vast topic, thus we have avoided discussion on this topic.

A KNIME Workflow for Automated Structure Verification

Adequate characterization of chemical entities made for biological screening in the drug discovery context is critical. Incorrectly characterized structures lead to mistakes in the interpretation of structure-activity relationships and confuse an already multidimensional optimization problem. Mistakes in the later use of these compounds waste money and valuable resources in a discovery process already under cost pressure. Left unidentified, these errors lead to problems in project data packages during quality review. At worst, they put intellectual property and patent integrity at risk. We describe a KNIME workflow for the early and automated identification of these errors during registration of a new chemical entity into the corporate screening catalog. This Automated Structure Verification workflow provides early identification (within 24 hours) of missing or inconsistent analytical data and therefore reduces any mistakes that inevitably get made. Automated identification removes the burden of work from the chemist submitting the compound into the registration system. No additional work is required unless a problem is identified and the submitter alerted. Before implementation, 14% of samples within the existing sample catalog were missing data on initial pass. A year after implementation, only 0.2% were missing data.

Unlocking the potential of late-stage functionalisation: an accurate and fully automated method for the rapid characterisation of multiple regioisomeric products

An automated pipeline for structure determination is outlined that will help unlock the potential of late-stage functionalisation (LSF).

Reconsidering the Structure of Serlyticin-A

Serlyticin-A is a secondary metabolite first isolated from a culture of Serratia ureilytica grown using squid pen as the sole carbon/nitrogen source. A previous study by Kuo et al. demonstrated that it has antioxidative and antiproliferative properties. However, the proposed chemical structure of serlyticin-A is likely incorrect based on the thermodynamic instability of its three contiguous heteroatom-heteroatom bonds. Here, we use quantum chemical calculations to predict 1H and 13C chemical shifts for serlyticin-A and demonstrate a discrepancy between the calculated and experimental chemical shifts. We then propose several reasonable alternative structures for serlyticin-A. Considering the known antioxidant and antiproliferative activity of hydroxamic acids as well as their stability and prevalence in natural products of bacterial origin, we believe that serlyticin-A is most likely 3-indolylacetohydroxamic acid (4). We provide our rationale for this assignment as well as experimental data for pure 3-indolylacetohydroxamic acid obtained via de novo synthesis. This study highlights the power of computational NMR shift prediction to revise chemical structures for natural products like serlyticin-A.

Computer-Assisted 3D Structure Elucidation (CASE-3D) of Natural Products Combining Isotropic and Anisotropic NMR Parameters

A computer-assisted structural elucidation (CASE-3D) strategy based on the use of isotropic and/or anisotropic NMR data is proposed to elucidate relative configuration and preferred conformation in complex natural products. The methodology involves the selection of conformational models through the use of the Akaike Information Criterion and scoring of the different configurations. As illustrative examples, the methodology furnished the correct configuration of the already known compounds artemisinin (1) and homodimericin A (2). Revised structures (5 and 6), including their absolute configuration, for the recently reported curcusones I (3) and J (4) are proposed.

NP-StructurePredictor: Prediction of Unknown Natural Products in Plant Mixtures

Identification of the individual chemical constituents of a mixture, especially solutions extracted from medicinal plants, is a time-consuming task. The identification results are often limited by challenges such as the development of separation methods and the availability of known reference standards. A novel structure elucidation system, NP-StructurePredictor, is presented and used to accelerate the process of identifying chemical structures in a mixture based on a branch and bound algorithm combined with a large collection of natural product databases. NP-StructurePredictor requires only targeted molecular weights calculated from a list of m/z values from liquid chromatography-mass spectrometry (LC-MS) experiments as input information to predict the chemical structures of individual components matching the weights in a mixture. NP-StructurePredictor also provides the predicted structures with statistically calculated probabilities so that the most likely chemical structures of the natural products and their analogs can be proposed accordingly. Four data sets consisting of different Chinese herbs with mixtures containing known compounds were selected for validation studies, and all their components were correctly identified and highly predicted using NP-StructurePredictor. NP-StructurePredictor demonstrated its applicability for predicting the chemical structures of novel compounds by returning highly accurate results from four different validation case studies.

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link (http://csccp.cmdm.tw/).

A stochastic method for asphaltene structure formulation from experimental data: avoidance of implausible structures

We present a sequential-stochastic algorithm to propose asphaltene molecular representations from experimental data, avoiding the pentane effect and following Clar's sextet rule.

Synergistic Combination of CASE Algorithms and DFT Chemical Shift Predictions: A Powerful Approach for Structure Elucidation, Verification, and Revision

Structure elucidation of complex natural products and new organic compounds remains a challenging problem. To support this endeavor, CASE (computer-assisted structure elucidation) expert systems were developed. These systems are capable of generating a set of all possible structures consistent with an ensemble of 2D NMR data followed by selection of the most probable structure on the basis of empirical NMR chemical shift prediction. However, in some cases, empirical chemical shift prediction is incapable of distinguishing the correct structure. Herein, we demonstrate for the first time that the combination of CASE and density functional theory (DFT) methods for NMR chemical shift prediction allows the determination of the correct structure even in difficult situations. An expert system, ACD/Structure Elucidator, was used for the CASE analysis. This approach has been tested on three challenging natural products: aquatolide, coniothyrione, and chiral epoxyroussoenone. This work has demonstrated that the proposed synergistic approach is an unbiased, reliable, and very efficient structure verification and de novo structure elucidation method that can be applied to difficult structural problems when other experimental methods would be difficult or impossible to use.

Turning Spiroketals Inside Out: A Rearrangement Triggered by an Enol Ether Epoxidation

Spiroketals organize small molecule structures into well-defined, three-dimensional configurations that make them good ligands of proteins. We recently discovered a tandem cycloisomerization-dimerization reaction of alkynyl hemiketals that delivered polycyclic, enol-ether-containing spiroketals. Here we describe rearrangements of those compounds, triggered by epoxidation of their enol ethers that completely remodel their structures, essentially turning them "inside out". Due to the high level of substitution on the carbon skeletons of the substrates and products, characterization resorted to X-ray crystallography and advanced computation and NMR techniques to solve the structures of representative compounds. In particular, a new proton-detected ADEQUATE NMR experiment (1,1-HD-ADEQUATE) enabled the unequivocal assignment of the carbon skeleton of one of the new compounds. Solution of the structures of the representative compounds allowed for the assignment of product structures for the other compounds in two separate series. Both the rearrangement and the methods used for structural determination of the products are valuable tools for the preparation of characterization of new small molecule compounds.

Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures

A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to Escherichia coli extract, where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides, and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures, overcoming the need of experimental MS and NMR metabolite databases.

The chemical space project

One of the simplest questions that can be asked about molecular diversity is how many organic molecules are possible in total? To answer this question, my research group has computationally enumerated all possible organic molecules up to a certain size to gain an unbiased insight into the entire chemical space. Our latest database, GDB-17, contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens, by far the largest small molecule database reported to date. Molecules allowed by valency rules but unstable or nonsynthesizable due to strained topologies or reactive functional groups were not considered, which reduced the enumeration by at least 10 orders of magnitude and was essential to arrive at a manageable database size. Despite these restrictions, GDB-17 is highly relevant with respect to known molecules. Beyond enumeration, understanding and exploiting GDBs (generated databases) led us to develop methods for virtual screening and visualization of very large databases in the form of a "periodic system of molecules" comprising six different fingerprint spaces, with web-browsers for nearest neighbor searches, and the MQN- and SMIfp-Mapplet application for exploring color-coded principal component maps of GDB and other large databases. Proof-of-concept applications of GDB for drug discovery were realized by combining virtual screening with chemical synthesis and activity testing for neurotransmitter receptor and transporter ligands. One surprising lesson from using GDB for drug analog searches is the incredible depth of chemical space, that is, the fact that millions of very close analogs of any molecule can be readily identified by nearest-neighbor searches in the MQN-space of the various GDBs. The chemical space project has opened an unprecedented door on chemical diversity. Ongoing and yet unmet challenges concern enumerating molecules beyond 17 atoms and synthesizing GDB molecules with innovative scaffolds and pharmacophores.

SYNTHESIS AND GIAO NMR CALCULATIONS FOR TWO DIASTEREOISOMERS OF 2′-ACETYLOXY-2′-PHENYLSPIRO[INDENO[1,2-b]QUINOXALIN-11,1′-CYCLOPROPANE]

Two diastereoisomers of 2′-acetyloxy-2′-phenylspiro[indeno[1,2-b]quinoxalin-11,1′-cyclopropane] were synthesized and their 1 H NMR spectra were recorded. Their chemical structures were fully optimized at B3LYP/6-311+G(d,p) level of theory using the Gaussian 03W program package. The 1 H NMR chemical shifts were calculated for geometry-optimized structures of the diastereoisomers with the gauge independent atomic orbital (GIAO) and B3LYP method with the 6-311+G(d,p), 6-311++G(d), 6-31++G(d,p) and 6-31+G(d) basis sets. The computational results were then compared with the experimental values and the structures associated with each spectrum were assigned.

Blind trials of computer-assisted structure elucidation software

Background

One of the largest challenges in chemistry today remains that of efficiently mining through vast amounts of data in order to elucidate the chemical structure for an unknown compound. The elucidated candidate compound must be fully consistent with the data and any other competing candidates efficiently eliminated without doubt by using additional data if necessary. It has become increasingly necessary to incorporate an in silico structure generation and verification tool to facilitate this elucidation process. An effective structure elucidation software technology aims to mimic the skills of a human in interpreting the complex nature of spectral data while producing a solution within a reasonable amount of time. This type of software is known as computer-assisted structure elucidation or CASE software. A systematic trial of the ACD/Structure Elucidator CASE software was conducted over an extended period of time by analysing a set of single and double-blind trials submitted by a global audience of scientists. The purpose of the blind trials was to reduce subjective bias. Double-blind trials comprised of data where the candidate compound was unknown to both the submitting scientist and the analyst. The level of expertise of the submitting scientist ranged from novice to expert structure elucidation specialists with experience in pharmaceutical, industrial, government and academic environments.

Results

Beginning in 2003, and for the following nine years, the algorithms and software technology contained within ACD/Structure Elucidator have been tested against 112 data sets; many of these were unique challenges. Of these challenges 9% were double-blind trials. The results of eighteen of the single-blind trials were investigated in detail and included problems of a diverse nature with many of the specific challenges associated with algorithmic structure elucidation such as deficiency in protons, structure symmetry, a large number of heteroatoms and poor quality spectral data.

Conclusion

When applied to a complex set of blind trials, ACD/Structure Elucidator was shown to be a very useful tool in advancing the computer's contribution to elucidating a candidate structure from a set of spectral data (NMR and MS) for an unknown. The synergistic interaction between humans and computers can be highly beneficial in terms of less biased approaches to elucidation as well as dramatic improvements in speed and throughput. In those cases where multiple candidate structures exist, ACD/Structure Elucidator is equipped to validate the correct structure and eliminate inconsistent candidates. Full elucidation can generally be performed in less than two hours; this includes the average spectral data processing time and data input.

Fast and accurate algorithm for the simulation of NMR spectra of large spin systems

The computational cost for the simulation of NMR spectra grows exponentially with the number of nuclei. Today, the memory available to store the Hamiltonian limits the size of the system that can be studied. Modern computers enable to tackle systems containing up to 13 spins [1], which obviously does not allow to study most molecules of interest in research. This issue can be addressed by identifying groups of spins or fragments that are not or only weakly interacting together, i.e., that only share weakly coupled spin pairs. Such a fragmentation is only permitted in the weak coupling regime, i.e., when the coupling interaction is weak compared to the difference in chemical shift of the coupled spins. Here, we propose a procedure that removes weak coupling interactions in order to split the spin system efficiently and to correct a posteriori for the effect of the neglected couplings. This approach yields accurate spectra when the adequate interactions are removed, i.e., between spins only involved in weak coupling interactions, but fails otherwise. As a result, the computational time for the simulation of 1D spectra grows linearly with the size of the spin system.

Computational strategies for metabolite identification in metabolomics

Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.

Theoretical prediction and assignment of vicinal 1H-1H coupling constants of diastereomeric 3-alkoxy-6,7-epoxy-2-oxabicyclo[3.3.0]octanes

Spin-spin coupling constants between nuclei in NMR spectroscopy reflect their spatial arrangement. A number of calculation methods, applying different levels of theory, have been developed to support the stereochemical assignment of novel compounds. Nevertheless, revisions of the assignment of structures in the literature are not rare. In the present work, the reliability of the calculation methods amenable for a theoretical prediction of spin-spin coupling constants of vicinal protons to support correct stereochemical assignment of substitution at five-membered rings of 3-alkoxy-6,7-epoxy-2-oxabicyclo[3.3.0]octanes was studied. Experimental (3)J(H,H) coupling constants were compared with the coupling constants calculated for all possible diastereomers. The fully quantum chemical approach provided theoretical (3)J(H,H) coupling constants with an absolute deviation of no more than 1.1 Hz for 91% of the experimentally studied coupled spins, whereas the methods without quantum chemical geometry optimization resulted in completely unreliable predictions. Consequently, for a reliable stereochemical assignment of small and medium size molecules, the protocol for calculating the coupling constants based on the results of the quantum chemical geometry optimization is recommended.

Empirical and DFT GIAO quantum-mechanical methods of (13)C chemical shifts prediction: competitors or collaborators?

The accuracy of (13)C chemical shift prediction by both DFT GIAO quantum-mechanical (QM) and empirical methods was compared using 205 structures for which experimental and QM-calculated chemical shifts were published in the literature. For these structures, (13)C chemical shifts were calculated using HOSE code and neural network (NN) algorithms developed within our laboratory. In total, 2531 chemical shifts were analyzed and statistically processed. It has been shown that, in general, QM methods are capable of providing similar but inferior accuracy to the empirical approaches, but quite frequently they give larger mean average error values. For the structural set examined in this work, the following mean absolute errors (MAEs) were found: MAE(HOSE) = 1.58 ppm, MAE(NN) = 1.91 ppm and MAE(QM) = 3.29 ppm. A strategy of combined application of both the empirical and DFT GIAO approaches is suggested. The strategy could provide a synergistic effect if the advantages intrinsic to each method are exploited.