How to Compute Electron Ionization Mass Spectra from First Principles

Advancing the Prediction of MS/MS Spectra Using Machine Learning

Tandem mass spectrometry (MS/MS) is an important tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. While popular, this strategy is limited by the contents of existing reference libraries. In response to this limitation, various methods are being developed for the in silico generation of spectra to augment existing libraries. Recently, machine learning and deep learning techniques have been applied to predict spectra with greater speed and accuracy. Here, we investigate the challenges these algorithms face in achieving fast and accurate predictions on a wide range of small molecules. The challenges are often amplified by the use of generic machine learning benchmarking tactics, which lead to misleading accuracy scores. Curating data sets, only predicting spectra for sufficiently high collision energies, and working more closely with experimental mass spectrometrists are recommended strategies to improve overall prediction accuracy in this nuanced field.

Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals

There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.

Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran

The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10-90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10-16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5-140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm.

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

Ultrafast dynamics of fluorene initiated by highly intense laser fields

We present an investigation of the ultrafast dynamics of the polycyclic aromatic hydrocarbon fluorene initiated by an intense femtosecond near-infrared laser pulse (810 nm) and probed by a weak visible pulse (405 nm). Using a multichannel detection scheme (mass spectra, electron and ion velocity-map imaging), we provide a full disentanglement of the complex dynamics of the vibronically excited parent molecule, its excited ionic states, and fragments. We observed various channels resulting from the strong-field ionization regime. In particular, we observed the formation of the unstable tetracation of fluorene, above-threshold ionization features in the photoelectron spectra, and evidence of ubiquitous secondary fragmentation. We produced a global fit of all observed time-dependent photoelectron and photoion channels. This global fit includes four parent ions extracted from the mass spectra, 15 kinetic-energy-resolved ionic fragments extracted from ion velocity map imaging, and five photoelectron channels obtained from electron velocity map imaging. The fit allowed for the extraction of 60 lifetimes of various metastable photoinduced intermediates.

Development of a Python-based electron ionization mass spectrometry amino acid and peptide fragment prediction model

The increased fragmentation caused by harsher ionization methods used during mass spectrometry such as electron ionization can make interpreting the mass spectra of peptides difficult. Therefore, the development of tools to aid in this spectral analysis is important in utilizing these harsher ionization methods to study peptides, as these tools may be more accessible to some researchers. We have compiled fragmentation mechanisms described in the literature, confirmed them experimentally, and used them to create a Python-based fragment prediction model for peptides analyzed under direct exposure probe electron ionization mass spectrometry. This initial model has been tested using single amino acids as well as targeted libraries of short peptides. It was found that the model does well in predicting fragments of peptides composed of amino acids for which the model is well-defined, but several cases where additional mechanistic information needs to be incorporated have been identified.

Mass spectra prediction with structural motif-based graph neural networks

Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures. A prevalent analysis method involves spectral library searches, where unknown spectra are cross-referenced with a database. The effectiveness of such search-based approaches, however, is restricted by the scope of the existing mass spectra database, underscoring the need to expand the database via mass spectra prediction. In this research, we propose the Motif-based Mass Spectrum prediction Network (MoMS-Net), a GNN-based architecture to predict the mass spectra pattern utilizing the structural motif information of the molecule. MoMS-Net considers both a molecule and its substructures as a graph form, which facilitates the incorporation of long-range dependencies while using less memory compared to the graph transformer model. We evaluated our model over various types of mass spectra and showed the validity and superiority over the conventional models.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods

During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions play an important role regarding the performance for compound identification.

Construction of a Mass Spectrum Library Containing Predicted Electron Ionization Mass Spectra Prepared Using a Machine Learning Model and the Development of an Efficient Search Method

Electron ionization (EI) mass spectrum library searching is usually performed to identify a compound in gas chromatography/mass spectrometry. However, compounds whose EI mass spectra are registered in the library are still limited compared to the popular compound databases. This means that there are compounds that cannot be identified by conventional library searching but also may result in false positives. In this report, we report on the development of a machine learning model, which was trained using chemical formulae and EI mass spectra, that can predict the EI mass spectrum from the chemical structure. It allowed us to create a predicted EI mass spectrum database with predicted EI mass spectra for 100 million compounds in PubChem. We also propose a method for improving library searching time and accuracy that includes an extensive mass spectrum library.

Ab initio molecular dynamics calculations on electron ionization induced fragmentations of C4F7N and C5F10O for understanding their decompositions under discharge conditions

C₄F₇N and C₅F₁₀O are the most promising SF₆ alternatives as eco-friendly insulating gaseous mediums in electrical engineering. It is necessary to clarify their electrical stability and decomposition mechanisms. In this work, we first introduced our experimental results for decomposition products of C₄F₇N/CO₂ and C₅F₁₀O/synthetic air mixtures under partial discharge and spark discharge conditions. Then, we performed ab initio molecular dynamics (AIMD) simulations on the typical decomposition products. The simulations were performed under standard electron impact mass spectrometry (EI-MS); thus, the statistical results of the mass spectra were compared with those of the experimentally obtained standard mass spectra from the NIST database. The AIMD simulation method in simulating the electron-induced ionization process was verified and found to be reliable. Finally, the calculations were also performed for C₄F₇N and C₅F₁₀O with incident electron energies of 20 eV and 70 eV, respectively. The dominant pathway for both gases is the formation of CF₃⁺ with the fracture of the C-C bond. The AIMD simulation is able to predict the decomposition channels after electron-impact ionization without any preconceived knowledge of fragmentation pathways, which provides a novel insight into understanding the decomposition mechanisms of C₄F₇N and C₅F₁₀O under different discharge conditions with different energies.

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, SubsetNet, produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.

Calculation of Mass Spectra with the QCxMS Method for Negatively and Multiply Charged Molecules

Analysis and validation of a mass spectrometry (MS) experiment are usually performed by comparison to reference spectra. However, if references are missing, measured spectra cannot be properly matched. To close this gap, the Quantum Chemical Mass Spectrometry (QCxMS) program has been developed. It enables fully automatic calculations of electron ionization (EI) and positive ion collision-induced dissociation (CID) mass spectra of singly charged molecular ions. In this work, the extension to negative and multiple ion charge for the CID run mode is presented. QCxMS is now capable of calculating structures carrying any charge, without the need for pretabulated fragmentation pathways or machine learning of database spectra. Mass spectra of four single negatively charged and two multiple positively charged organic ions with molecular sizes from 12 to 92 atoms were computed and compared to reference spectra. The underlying Born-Oppenheimer molecular dynamics (MD) calculations were conducted using the semiempirical quantum mechanical GFN2-xTB method, while for some small molecules, ab initio DFT-based MD simulations were performed. Detailed insights into the fragmentation pathways were gained, and the effects of the computed charge assignments on the resulting spectrum are discussed. Especially for the negative ion mode, the influence of the deprotonation site to create the anion was found to be substantial. Doubly charged fragments could successfully be calculated fully automatically for the first time, while higher charged structures introduced severe assignment problems. Overall, this extension of the QCxMS program further enhances its applicability and underlines its value as a sophisticated toolkit for CID-based tandem MS structure elucidation.

Electron-induced dissociation dynamics studied using covariance-map imaging

Recently, covariance analysis has found significant use in the field of chemical reaction dynamics. When coupled with data from product time-of-flight mass spectrometry and/or multi-mass velocity-map imaging, it allows us to uncover correlations between two or more ions formed from the same parent molecule. While the approach has parallels with coincidence measurements, covariance analysis allows experiments to be performed at much higher count rates than traditional coincidence methods. We report results from electron-molecule crossed-beam experiments, in which covariance analysis is used to elucidate the dissociation dynamics of multiply-charged ions formed by electron ionisation over the energy range from 50 to 300 eV. The approach is able to isolate signal contributions from multiply charged ions even against a very large 'background' of signal arising from dissociation of singly-charged parent ions. Covariance between the product time-of-flight spectra identifies pairs of fragments arising from the same parent ions, while covariances between the velocity-map images ('recoil-frame covariances') reveal the relative velocity distributions of the ion pairs. We show that recoil-frame covariance analysis can be used to distinguish between multiple plausible dissociation mechanisms, including multi-step processes, and that the approach becomes particularly powerful when investigating the fragmentation dynamics of larger molecules with a higher number of possible fragmentation pathways.

The kinetic energy of PAH dication and trication dissociation determined by recoil-frame covariance map imaging

We investigated the dissociation of dications and trications of three polycyclic aromatic hydrocarbons (PAHs), fluorene, phenanthrene, and pyrene. PAHs are a family of molecules ubiquitous in space and involved in much of the chemistry of the interstellar medium. In our experiments, ions are formed by interaction with 30.3 nm extreme ultraviolet (XUV) photons, and their velocity map images are recorded using a PImMS2 multi-mass imaging sensor. Application of recoil-frame covariance analysis allows the total kinetic energy release (TKER) associated with multiple fragmentation channels to be determined to high precision, ranging 1.94-2.60 eV and 2.95-5.29 eV for the dications and trications, respectively. Experimental measurements are supported by Born-Oppenheimer molecular dynamics (BOMD) simulations.

Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics

Here, we provide an algorithm that introduces excited states into the molecular dynamics prediction of the 70 eV electron ionization mass spectra. To decide the contributions of different electronic states, the ionization cross section associated with relevant molecular orbitals was calculated by the binary-encounter-Bethe (BEB) model. We used a fast orthogonalization model/single and double state configuration interaction (OM2/CISD) method to implement excited states calculations and combined this with the GFN1-xTB semiempirical model. Demonstrated by predicting the mass spectrum of urocanic acid, we showed better accuracies to experimental spectra using excited-state molecular dynamics than calculations that only used the ground-state occupation. For several histidine pathway intermediates, we found that excited-state corrections yielded an average of 73% more true positive ions compared to the OM2 method when matching to experimental spectra and 16% more true positive ions compared to the GFN method. Importantly, the exited state models also correctly predict several fragmentation reactions that were missing from both ground-state methods. Overall, for 48 calculated molecules, we found the best average mass spectral similarity scores for the mixed excited-state method compared to the ground-state methods using either cosine, weighted dot score, or entropy similarity calculations. Therefore, we recommend adding excited-state calculations for predicting the electron ionization mass spectra of small molecules in metabolomics.

Computed Mass-Fragmentation Energy Profiles of Some Acetalized Monosaccharides for Identification in Mass Spectrometry

Our study found that quantum calculations can differentiate fragmentation energies into isomeric structures with asymmetric carbon atoms, such as those of acetalized monosaccharides. It was justified by the good results that have been published in recent years on the discrimination of structural isomers and diastereomers by correlating the calculated mass energy fragmentation profiles with their mass spectra. Based on the quantitative structure–fragmentation relationship (QSFR), this technique compares the intensities of primary ions from the experimental spectrum using the mass energy profiles calculated for the candidate structures. Maximum fit is obtained for the true structure. For a preliminary assessment of the accuracy of the identification of some di-O-isopropylidene monosaccharide diastereomers, we used fragmentation enthalpies (ΔfH) and Gibbs energies (ΔfG) as the energetic descriptors of fragmentation. Four quantum chemical methods were used: RM1, PM7, DFT ΔfH and DFT ΔfG. The mass energy database shows that the differences between the profiles of the isomeric candidate structures could be large enough to be distinguished from each other. This database allows the optimization of energy descriptors and quantum computing methods that can ensure the correct identification of these isomers.

Quantum Chemistry-based Molecular Dynamics Simulations as a Tool for the Assignment of ESI-MS/MS Spectra of Drug Molecules

In organic mass spectrometry, fragment ions provide important information on the analyte as a central part of its structure elucidation. With increasing molecular size and possible protonation sites, the potential energy surface (PES) of the analyte can become very complex, which results in a large number of possible fragmentation patterns. Quantum chemical (QC) calculations can help here, enabling the fast calculation of the PES and thus enhancing the mass spectrometry-based structure elucidation processes. In this work, the previously unknown fragmentation pathways of the two drug molecules Nateglinide (45 atoms) and Zopiclone (51 atoms) were investigated using a combination of generic formalisms and calculations conducted with the Quantum Chemical Mass Spectrometry (QCxMS) program. The computations of the de novo fragment spectra were conducted with the semi-empirical GFNn-xTB (n=1, 2) methods and compared against Orbitrap measured electrospray ionization (ESI) spectra in positive ion mode. It was found that the unbiased QC calculations are particularly suitable to predict non-evident fragment ion structures, sometimes contrasting the accepted generic formulation of fragment ion structures from electron migration rules, where the "true" ion fragment structures are approximated. For the first time, all fragment and intermediate structures of these large-sized molecules could be elucidated completely and routinely using this merger of methods, finding new undocumented mechanisms, that are not considered in common rules published so far. Given the importance of ESI for medicinal chemistry, pharmacokinetics, and metabolomics, this approach can significantly enhance the mass spectrometry-based structure elucidation processes and contribute to the understanding of previously unknown fragmentation pathways.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Background

Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS.

Results

Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-1}$$\end{document}-1, measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected).

Conclusions

Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-021-00544-w.

Determination of kinetic properties in unimolecular dissociation of complex systems from graph theory based analysis of an ensemble of reactive trajectories

In this paper, we report how graph theory can be used to analyze an ensemble of independent molecular trajectories, which can react during the simulation time-length, and obtain structural and kinetic information. This method is totally general and here is applied to the prototypical case of gas phase fragmentation of protonated cyclo-di-glycine. This methodology allows us to analyze the whole set of trajectories in an automatic computer-based way without the need of visual inspection but by getting all the needed information. In particular, we not only determine the appearance of different products and intermediates but also characterize the corresponding kinetics. The use of colored graph and canonical labeling allows for the correct characterization of the chemical species involved. In the present case, the simulations consist of an ensemble of unimolecular fragmentation trajectories at constant energy such that from the rate constants at different energies, the threshold energy can also be obtained for both global and specific pathways. This approach allows for the characterization of ion-molecule complexes, likely through a roaming mechanism, by properly taking into account the elusive nature of such species. Finally, it is possible to directly obtain the theoretical mass spectrum of the fragmenting species if the reacting system is an ion as in the specific example.

Simulation of the electron ionization mass spectra of the Novichok nerve agent

Novichok is one of the most feared and controversial nerve agents, which existence was confirmed only after the Salisbury attack in 2018. A new attack on August 2020, in Russia, was confirmed. After the 2018 attack, the agent was included in the list of the most dangerous chemicals of the Chemical Weapons Convention (CWC). However, information related to its electron ionization mass spectrometry (EI/MS), essential for unambiguous identification, is scarce. Therefore, investigations about Novichok EI/MS are urgent. In this work, we employed Born-Oppenheimer molecular dynamics through the Quantum Chemistry Electron Ionization Mass Spectrometry (QCEIMS) method to simulate and rationalize the EI/MS spectra and fragmentation pathways of 32 Novichok molecules recently incorporated into the CWC. The comparison of additional simulations with the measured EI spectrum of another Novichok analog is very favorable. A general scheme of the fragmentation pathways derived from simulation results was presented. The present results will be useful for elucidation and prediction of the EI spectra and fragmentation pathways of the dangerous Novichok nerve agent.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.

From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics

Mass spectrometry (MS) is a powerful tool in chemical research and substance identification. For the computational modeling of electron ionization MS, we have developed the quantum-chemical electron ionization mass spectra (QCEIMS) program. Here, we present an extension of QCEIMS to calculate collision-induced dissociation (CID) spectra. The more general applicability is accounted for by the new name QCxMS, where "x" refers to EI or CID. To this end, fragmentation and rearrangement reactions are computed "on-the-fly" in Born-Oppenheimer molecular dynamics (MD) simulations with the semiempirical GFN2-xTB Hamiltonian, which provides an efficient quantum mechanical description of all elements up to Z = 86 (Rn). Through the explicit modeling of multicollision processes between precursor ions and neutral gas atoms as well as temperature-induced decomposition reactions, QCxMS provides detailed insight into the collision kinetics and fragmentation pathways. In combination with the CREST program to determine the preferential protonation sites, QCxMS becomes the first standalone MD-based program that can predict mass spectra based solely on molecular structures as input. We demonstrate this for six organic molecules with masses ranging from 159 to 296 Da, for which QCxMS yields CID spectra in reasonable agreement with experiments.

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.

Valence Photoionization and Energetics of Vanillin, a Sustainable Feedstock Candidate

We studied the valence photoionization of vanillin by photoelectron photoion coincidence spectroscopy in the 8.20-19.80 eV photon energy range. Vertical ionization energies by EOM-IP-CCSD calculations reproduce the photoelectron spectral features. Composite method calculations and Franck-Condon simulation of the weak, ground-state band yield the adiabatic ionization energy of the most stable vanillin conformer as 8.306(20) eV. The lowest energy dissociative photoionization channels correspond to hydrogen atom, carbon monoxide, and methyl losses, which form the dominant C₈H₇O₃⁺ (m/z 151) and the less intense C₇H₈O₂⁺ (m/z 124) and C₇H₅O₃⁺ (m/z 137) fragment ions in parallel dissociation channels at modeled 0 K appearance energies of 10.13(1), 10.40(3), and 10.58(10) eV, respectively. On the basis of the breakdown diagram, we explore the energetics of sequential methyl and carbon monoxide loss channels, which dominate the fragmentation mechanism at higher photon energies. The 0 K appearance energy for sequential CO loss from the m/z 151 fragment to C₇H₇O₂⁺ (m/z 123) is 12.99(10) eV, and for sequential CH₃ loss from the m/z 123 fragment to C₆H₄O₂⁺ (m/z 108), it is 15.40(20) eV based on the model. Finally, we review the thermochemistry of the bi- and trifunctionalized benzene derivatives guaiacol, hydroxybenzaldehyde, anisaldehyde, and vanillin. On the basis of isodesmic functional group exchange reactions, we propose new enthalpies of formations, among them Δ_fH°_298K(vanillin, g) = -383.5 ± 2.9 kJ mol^-1. These mechanistic insights and ab initio thermochemistry results will support analytical works to study lignin conversion involving vanillin.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

A general approach to study molecular fragmentation and energy redistribution after an ionizing event

We propose to combine quantum chemical calculations, statistical mechanical methods, and photoionization and particle collision experiments to unravel the redistribution of internal energy of the furan cation and its dissociation pathways. This approach successfully reproduces the relative intensity of the different fragments as a function of the internal energy of the system in photoelectron-photoion coincidence experiments and the different mass spectra obtained when ions ranging from Ar+ to Xe25+ or electrons are used in collision experiments. It provides deep insights into the redistribution of the internal energy in the ionized molecule and its influence on the dissociation pathways and resulting charged fragments. The present pilot study demonstrates the efficiency of a statistical exchange of excitation energy among various degrees of freedom of the molecule and proves that the proposed approach is mature to be extended to more complex systems.

Predicting in silico electron ionization mass spectra using quantum chemistry

Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.

Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers

It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.

Approaching black-box calculations of pump-probe fragmentation dynamics of polyatomic molecules

A general framework for the simulation of ultrafast pump-probe time resolved experiments based on Born-Oppenheimer molecular dynamics (BOMD) is presented. Interaction of the molecular species with a laser is treated by a simple maximum entropy distribution of the excited state occupancies. The latter decay of the electronic excitation into the vibrations is based on an on-the-fly estimation of the rate of the internal conversion, while the energy is distributed in a thermostat-like fashion. The approach was tested by reproducing the results of previous femtosecond studies on ethylene, naphthalene and new results for phenanthrene.

Molecular dynamics simulation of the electron ionization mass spectrum of tabun

Tabun (ethyl N,N-dimethylphosphoramidocyanidate), or GA, is a chemical warfare nerve agent produced during the World War II. The synthesis of its analogs is rather simple; thus, it is a significant threat. Furthermore, experiments with tabun and other nerve agents are greatly limited by the involved life risks and the severe restrictions imposed by the Chemical Weapons Convention. For these reasons, accurate theoretical assignment of fragmentation pathways can be especially important. In this work, we employ the Quantum Chemistry Electron Ionization Mass Spectra method, which combines molecular dynamics, quantum chemistry methods, and stochastic approaches, to accurately investigate the electron ionization/mass spectrometry (EI/MS) fragmentation spectrum and pathways of the tabun molecule. We found that different rearrangement reactions occur including a McLafferty involving the nitrile group. An essential and characteristic pathway for identification of tabun and analogs, a two-step fragmentation producing the m/z 70 ion, was confirmed. The present results will be also useful to predict EI/MS spectrum and fragmentation pathways of other members of the tabun family, namely, the O-alkyl/cycloalkyl N,N-dialkyl (methyl, ethyl, isopropyl, or propyl) phosphoramidocyanidates.

Photochemistry of Amylene Double Bond in Clusters on Free Argon Nanoparticles

We have investigated reactivity of double bond in 2-methyl-2-butene (also trimethylethylene or amylene) in the excited and ionized states. In a combined experimental and theoretical study, we focused on both the intermolecular and intramolecular reactions. In a molecular beam experiment, we have sequentially picked up several amylene molecules on the surface of argon nanoparticles Ar_M, M̅ ≈ 90, acting as a cold support. Ionization with 70 eV electrons yields mass spectra strongly dominated by amylene cluster ions Am(Am)_n⁺. Interestingly, upon multiphoton ionization with 193 nm (6.4 eV) photons, a new strong fragment series appears in the spectra, nominally corresponding to an addition of two carbon atoms, i.e., (Am)_nC₂⁺. This difference between electron and photoionization suggests a reaction in an excited state of amylene with a neighboring amylene molecule. We used techniques of nonadiabatic molecular dynamics to study the reactivity of amylene molecules both in the excited and in ionized states. Possible reaction pathways are proposed, substantiating the observed differences between the electron ionization and photoionization mass spectra.

Automated exploration of the low-energy chemical space with fast quantum chemical methods

We propose and discuss an efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm. The focus of this work is set on the generation of proper thermodynamic ensembles at a quantum chemical level for conformers, but similar procedures for protonation states, tautomerism and non-covalent complex geometries are also discussed. The conformational ensembles consisting of all significantly populated minimum energy structures normally form the basis of further, mostly DFT computational work, such as the calculation of spectra or macroscopic properties. By using basic quantum chemical methods, electronic effects or possible bond breaking/formation are accounted for and a very reasonable initial energetic ranking of the candidate structures is obtained. Due to the huge computational speedup gained by the fast low-cost quantum chemical methods, overall short computation times even for systems with hundreds of atoms (typically drug-sized molecules) are achieved. Furthermore, specialized applications, such as sampling with implicit solvation models or constrained conformational sampling for transition-states, metal-, surface-, or noncovalently bound complexes are discussed, opening many possible applications in modern computational chemistry and drug discovery. The procedures have been implemented in a freely available computer code called CREST, that makes use of the fast and reliable GFNn-xTB methods.

Graph theory-based reaction pathway searches and DFT calculations for the mechanism studies of free radical-initiated peptide sequencing mass spectrometry (FRIPS MS): a model gas-phase reaction of GGR tri-peptide

Graph theory-based reaction pathway searches (ACE-Reaction program) and density functional theory calculations were performed to shed light on the mechanisms for the production of [a_n + H]⁺, x_n⁺, y_n⁺, z_n⁺, and [y_n + 2H]⁺ fragments formed in free radical-initiated peptide sequencing (FRIPS) mass spectrometry measurements of a small model system of glycine-glycine-arginine (GGR). In particular, the graph theory-based searches, which are rarely applied to gas-phase reaction studies, allowed us to investigate reaction mechanisms in an exhaustive manner without resorting to chemical intuition. As expected, radical-driven reaction pathways were favorable over charge-driven reaction pathways in terms of kinetics and thermodynamics. Charge- and radical-driven pathways for the formation of [y_n + 2H]⁺ fragments were carefully compared, and it was revealed that the [y_n + 2H]⁺ fragments observed in our FRIPS MS spectra originated from the radical-driven pathway, which is in contrast to the general expectation. The acquired understanding of the FRIPS fragmentation mechanism is expected to aid in the interpretation of FRIPS MS spectra. It should be emphasized that graph theory-based searches are powerful and effective methods for studying reaction mechanisms, including gas-phase reactions in mass spectrometry.

EMPIRE: a highly parallel semiempirical molecular orbital program: 3: Born-Oppenheimer molecular dynamics

Direct NDDO-based Born-Oppenheimer molecular dynamics (MD) have been implemented in the semiempirical molecular orbital program EMPIRE. Fully quantum mechanical MD simulations on unprecedented time and length scales are possible, since the calculation of self-consistent wavefunctions and gradients is performed in a massively parallel manner. MD simulations can be performed in the NVE and NVT ensembles, using either deterministic (Berendsen) or stochastic (Langevin) thermostats. Furthermore, dynamics for condensed-phase systems can be performed under periodic boundary conditions. We show three exemplary applications: the dynamics of molecular reorganization upon ionization, long timescale dynamics of an endohedral fullerene, and calculation of the vibrational spectrum of a nanoparticle consisting of more than eight hundred atoms.

A guinea pig for conformer selectivity and mechanistic insights into dissociative ionization by photoelectron photoion coincidence: fluorocyclohexane

We studied fluorocyclohexane (C₆H₁₁F, FC6) by double imaging photoelectron photoion coincidence spectroscopy in the 9.90-13.90 eV photon energy range. The photoelectron spectrum can identify species isomer and, in this case, even conformer selectively. Ab initio results indicated that the axial conformer has two, close-lying cation electronic states. With the help of Franck-Condon simulations of the vibrational fine structure, we determined the origin of three transitions, (i) axial FC6 → axial FC6⁺ of C₁ symmetry (X[combining tilde]⁺, A'' in C_S), (ii) equatorial FC6 → equatorial FC6⁺ of C₁ symmetry (X[combining tilde]⁺, A'' in C_S), and (iii) axial FC6 → A' axial FC6⁺ of C_S symmetry (Ã⁺) as 10.12 ± 0.01, 10.15 ± 0.01 and 10.15 ± 0.02 eV, respectively. At slightly higher energies, the FC6 cation starts fragmenting by HF loss (E₀ = 10.60 eV), followed by sequential CH₃ (E₀ = 10.71 eV) or C₂H₄ (E₀ = 11.06 eV) loss. Surprisingly, the methyl-loss step has an effective barrier of only 0.11 eV, and yet it is a slow process at threshold. Based on the statistical model, this is explained by isomerization and stabilization of the C₆H₁₀⁺ intermediate. The highest energy channel observed, vinyl fluoride (C₂H₃F) loss yielding C₄H₈⁺ appears in the breakdown diagram at 12 eV, which agrees with the computed threshold to cyclobutane cation formation. However, the model predicted a ca. 1 eV competitive shift for this parallel channel, i.e., an E₀ = 11.23 eV. This led us to explore the potential energy surface to find a lower-lying fragmentation channel including H-transfer steps. Rate constant measurements and statistical modeling thus yield fundamental insights into the reaction mechanism beyond what is immediately seen in the mass spectra.

Fragmentation Spectra Prediction and DNA Adducts Structural Determination

In this work, chemical dynamics simulations were optimized and used to predict fragmentation mass spectra for DNA adduct structural determination. O⁶-methylguanine (O⁶-Me-G) was used as a simple model adduct to calculate theoretical spectra for comparison with measured high-resolution fragmentation data. An automatic protocol was established to consider the different tautomers accessible at a given energy and obtain final theoretical spectra by insertion of an initial tautomer. In the work reported here, the most stable tautomer was chosen as the initial structure, but in general, any structure could be considered. Allowing for the formation of the various possible tautomers during simulation calculations was found to be important to getting a more complete fragmentation spectrum. The calculated theoretical results reproduce the experimental peaks such that it was possible to determine reaction pathways and product structures. The calculated tautomerization network was crucial to correctly identifying all the observed ion peaks, showing that a mobile proton model holds not only for peptide fragmentation but also for nucleobases. Finally, first principles results were compared to simple machine learning fragmentation models.

Calculation of Electron Ionization Mass Spectra with Semiempirical GFNn-xTB Methods

In this work, we have tested two different extended tight-binding methods in the framework of the quantum chemistry electron ionization mass spectrometry (QCEIMS) program to calculate electron ionization mass spectra. The QCEIMS approach provides reasonable, first-principles computed spectra, which can be directly compared to experiment. Furthermore, it provides detailed insight into the reaction mechanisms of mass spectrometry experiments. It sheds light upon the complicated fragmentation procedures of bond breakage and structural rearrangements that are difficult to derive otherwise. The required accuracy and computational demands for successful reproduction of a mass spectrum in relation to the underlying quantum chemical method are discussed. To validate the new GFN2-xTB approach, we conduct simulations for 15 organic, transition-metal, and main-group inorganic systems. Major fragmentation patterns are analyzed, and the entire calculated spectra are directly compared to experimental data taken from the literature. We discuss the computational costs and the robustness (outliers) of several calculation protocols presented. Overall, the new, theoretically more sophisticated semiempirical method GFN2-xTB performs well and robustly for a wide range of organic, inorganic, and organometallic systems.

Computationally-assisted discovery and structure elucidation of natural products

Computer hardware development coupled with the development of quantum chemistry, new computational models and algorithms, and user-friendly interfaces have lowered the barriers to the use of computation in the discovery and structure elucidation of natural products. Consequently, the use of computational chemistry software as a tool to discover and determine the structure of natural products has become more common in recent years. In this review, we provide several examples of recent studies that used computer technology to facilitate the discovery and structure determination of various natural products.

Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.

The role of the dihedral angle and excited cation states in ionization and dissociation of mono-halogenated biphenyls; a combined experimental and theoretical coupled cluster study

We present a combined theoretical and experimental study on the ionization and primary fragmentation channels of the mono-halogenated biphenyls; 2-chlorobiphenyl, 2-bromobiphenyl and 2-iodobiphenyl. The ionization energies (IEs) of the 2-halobiphenyls and the appearance energies (AEs) of the principal fragments are determined through electron impact ionization, while quantum mechanical calculations at the coupled cluster level of theory are used to elucidate the observed processes and the associated dynamics. The primary fragmentation channels are the direct loss of the halogen upon ionization, the loss of the respective hydrogen halides (HX) as well as loss of the hydrogen halide and an additional hydrogen. We find that the dihedral angle strongly influences the relative potential energy of the neutral and the cation on their respective ground state surfaces, an effect caused by the strong influence of the nuclear motion on the conjugation between the phenyl rings. For the principal dissociative ionization channels from the mono-halogenated biphenyls we reason that these can not be described as statistical decay from the ground state cation, but must rather be understood as direct, state-selective processes from specific excited cationic states characterized through local ionization of either the halogenated or the non-substituted phenyl ring.

Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation

Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.

A Trajectory-Based Method to Explore Reaction Mechanisms

The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.

Questions in natural products synthesis research that can (and cannot) be answered using computational chemistry

Questions of relevance to those working in the field of natural products synthesis that can be answered, at least in part, using computational chemistry approaches are described. Illustrative examples are provided, as are descriptions of limitations.