Probing Noncovalent Interactions by Fast Magic-Angle Spinning NMR at 100 kHz and More

Noncovalent interactions are the basis for a large number of chemical and biological molecular-recognition processes, such as those occurring in supramolecular chemistry, catalysis, solid-state reactions in mechanochemistry, protein folding, protein-nucleic acid binding, and biomolecular phase separation processes. In this perspective article, some recent developments in probing noncovalent interactions by proton-detected solid-state Nuclear Magnetic Resonance (NMR) spectroscopy at Magic-Angle Spinning (MAS) frequencies of 100 kHz and more are reviewed. The development of MAS rotors with decreasing outer diameters, combined with the development of superconducting magnets operating at high static magnetic-field strengths up to 28.2 T (1200 MHz proton Larmor frequency) improves resolution and sensitivity in proton-detected solid-state NMR, which is the fundamental requirement for shedding light on noncovalent interactions in solids. The examples reported in this article range from protein-nucleic acid binding in large ATP-fueled motor proteins to a hydrogen-π interaction in a calixarene-lanthanide complex.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

The nucleation process leading to the formation of new atmospheric particles plays a crucial role in aerosol research. Quantum chemical (QC) calculations can be used to model the early stages of aerosol formation, where atmospheric vapor molecules interact and form stable molecular clusters. However, QC calculations heavily depend on the chosen computational method, and when dealing with large systems, striking a balance between accuracy and computational cost becomes essential. We benchmarked the binding energies and structures and found the B97-3c method to be a good compromise between the accuracy and computational cost for studying large cluster systems. Further, we carefully assessed configurational sampling procedures for targeting large atmospheric molecular clusters containing up to 30 molecules (approximately 2 nm in diameter) and proposed a funneling approach with highly improved accuracy. We find that several parallel ABCluster explorations lead to better guesses for the cluster global energy minimum structures than one long exploration. This methodology allows us to bridge computational studies of molecular clusters, which typically reach only around 1 nm, with experimental studies that often measure particles larger than 2 nm. By employing this workflow, we searched for low-energy configurations of large sulfuric acid-ammonia and sulfuric acid-dimethylamine clusters. We find that the binding free energies of clusters containing dimethylamine are unequivocally more stable than those of the ammonia-containing clusters. Our improved configurational sampling protocol can in the future be applied to study the growth and dynamics of large clusters of arbitrary compositions.

Gas-phase C60Hn+q (n = 0-4, q = 0,1) fullerenes and fulleranes: spectroscopic simulations shed light on cosmic molecular structures

The discovery of C60, C60+, and C70 in the interstellar medium has ignited a profound interest in the astrochemistry of fullerene and related systems. In particular, the presence of diffuse interstellar bands and their association with C60+ has led to the hypothesis that hydrogenated derivatives, known as fulleranes, may also exist in the interstellar medium and contribute to these bands. In this study, we systematically investigated the structural and spectroscopic properties of C60Hn+q (n = 0-4, q = 0,1) using an automated global minimum search and density functional theory calculations. Our results revealed novel global minimum structures for C60H2 and C60H4, distinct from previous reports. Notably, all hydrogenated fullerenes exhibited lower ionization potentials and higher proton affinities compared to C60. From an astrochemical perspective, our results exposed the challenges in establishing definitive spectroscopic criteria for detecting fulleranes using mid-infrared and UV-Vis spectroscopies. However, we successfully identified distinct electronic transitions in the near-infrared range that serve as distinctive signatures of cationic fulleranes. We strongly advocate for further high-resolution experimental studies to fully explore the potential of these transitions for the interstellar detection of fulleranes.

Environmental differences between Japan and Indonesia provide endophyte diversity associated with Artemisia plant and variety of artemisinin derivatives in microbial conversion

We compared the endophytic compositions of Artemisia plant from different environments (Japan and Indonesia) to demonstrate that the endophytic filamentous fungi in both species differed based on their environments. To prove that the species were identical, both Artemisia plants were identified by comparing the scanning electron micrographs of their pollens, as well as the nucleotide sequences (ribosomal internal transcribed spacer and mitochondrial maturase K) of the two gene regions. After isolating the endophytic filamentous fungi from each plant, we observed that those from Japan and Indonesia comprised 14 and 6 genera, respectively. We assumed that the genera, Arthrinium and Colletotrichum, which exist in both Artemisia species, were species-specific filamentous fungi, while the other genera were environment-dependent. In the microbial-conversion reaction with artemisinin as a substrate using Colletotrichum sp., the peroxy bridge of artemisinin, which is an active site for achieving antimalarial effect, was converted into an ether bond. However, the reaction using the environment-dependent endophyte did not eliminate the peroxy bridge. These endophytic reactions indicated the different roles of endophytes within Artemisia plants.

Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies

Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol^-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.

Deciphering and investigating fragment mechanism of quinolones using multi-collision energy mass spectrometry and computational chemistry strategy

RATIONALE

Quinolones show characteristic fragments in mass spectrometry (MS) analysis due to their common core structures, and energy-dependent differences among these fragments are generated through the same fragmentation pathway of different molecules. Computational chemistry, which provides quantitative results of molecule parameters, is helpful for investigating the mechanisms of chemistry.

METHODS

MS/MS spectra of five quinolones, namely norfloxacin (NOR), enoxacin (ENO), enrofloxacin (ENR), gatifloxacin (GAT), and lomefloxacin (LOM), were acquired for deciphering fragmentation pathways under multi-collision energy (CE). Computational methods were used for excluding little possibility pathways from the point of view of energy and stable conformations, whereas optimized collision energy (OCE) and maximum relative intensity (MRI) of major competitive fragments were investigated and confirmed using computational results.

RESULTS

Fragmentation results of NOR, ENO, ENR, and GAT were deciphered using experimental and computational data, of which fragmentation regularities were summarized. Fragmentation pathways of LOM were deciphered under the guidance of foregoing regularities. Meanwhile, the whole process was validated by comparing OCE and MRI and computational energy results, which showed good agreement.

CONCLUSIONS

A strategy for explaining quinolone fragmentation results of multi-CE values and deciphering fragment mechanism using computational methods was developed. Relevant data and strategy may provide ideas for how to design and decipher new drug molecules with similar structures.

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.

Understanding of protomers/deprotomers by combining mass spectrometry and computation

Multifunctional compounds may form different prototropic isomers under different conditions, which are known as protomers/deprotomers. In biological systems, these protomer/deprotomer isomers affect the interaction modes and conformational landscape between compounds and enzymes and thus present different biological activities. Study on protomers/deprotomers is essentially the study on the acidity/basicity of each intramolecular functional group and its effect on molecular structure. In recent years, the combination of mass spectrometry (MS) and computational chemistry has been proven to be a powerful and effective means to study prototropic isomers. MS-based technologies are developed to discriminate and characterize protomers/deprotomers to provide structural information and monitor transformations, showing great superiority than other experimental methods. Computational chemistry is used to predict the thermodynamic stability of protomers/deprotomers, provide the simulated MS/MS spectra, infrared spectra, and calculate collision cross-section values. By comparing the theoretical data with the corresponding experimental results, the researchers can not only determine the protomer/deprotomer structure, but also investigate the structure-activity relationship in a given system. This review covers various MS methods and theoretical calculations and their devotion to isomer discrimination, structure identification, conformational transformation, and phase transition investigation of protomers/deprotomers.

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.

Guided discovery of chemical reaction pathways with imposed activation

Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.

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.

Vibrational Spectroscopy of Homo- and Heterochiral Amino Acid Dimers: Conformational Landscapes

The homo- and heterochiral protonated dimers of asparagine with serine and with valine were investigated using infrared multiple-photon dissociation (IRMPD) spectroscopy. Extensive quantum-chemical calculations were used in a three-tiered strategy to screen the conformational spaces of all four dimer species. The resulting binary structures were further grouped into five different types based on their intermolecular binding topologies and subunit configurations. For each dimer species, there are eight to fourteen final conformational geometries within a 10 kJ mol-1 window of the global minimum structure for each species. The comparison between the experimental IRMPD spectra and the simulated harmonic IR features allowed us to clearly identify the types of structures responsible for the observation. The monomeric subunits of the observed homo- and heterochiral dimers are compared to the corresponding protonated/neutral amino acid monomers observed experimentally in previous IRMDP/rotational spectroscopic studies. Possible chirality and kinetic influences on the experimental IRMPD spectra are discussed.

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.

Efficient Quantum-Chemical Calculations of Acid Dissociation Constants from Free-Energy Relationships

The calculation of acid dissociation constants (pK_a) is an important task in computational chemistry and chemoinformatics. Theoretically and with minimal empiricism, this is possible from computed acid dissociation free energies via so-called linear free-energy relationships. In this study some modifications are introduced to the latter, providing a straightforward, broadly applicable protocol with an adjustable degree of sophistication for quantum chemistry-based calculations of pK_a in water. It targets a wide pK_a range (∼70 units) and medium-sized, flexible molecules. Herein, a focus is set on the recently published r²SCAN-3c and related efficient composite density functionals and the semiempirical GFN2-xTB method, including a newly introduced energy correction for heterolytic dissociation, both in combination with implicit solvation models. The performance is evaluated in comparison with experimental data, showing mean errors often smaller than a targeted 1 pK_a unit accuracy. Larger deviations are observed only upon inclusion of challenging highly negative (<-5) or positive (>15) pK_a values. Among all those tested, it is found that B97-3c is the best performing functional, although rather independently of the density functional theory (DFT) method used; low root-mean-square errors of 0.8-1.0 pK_a units for typical drugs are obtained. For optimal performance, it is recommended to employ DFT functional specific free-energy relationship parameters. Additionally, a significant conformational dependence of the pK_a values is revealed and quantified for some nonrigid drug molecules.

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.

Active discovery of organic semiconductors

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.

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.

Accurate and rapid prediction of pKa of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

Data-augmented high-throughput QM approach to compute pK_a of transition metal hydride complexes with hDFT accuracy and low cost.

Effective corrosion inhibition of mild steel by eco-friendly thiourea functionalized glucosamine derivatives in acidic solution

In the view of environmental protection and sustainable development, the application of green effective inhibitors for metal corrosion in industry field is of great significance. In this work, two thiourea functionalized glucosamine derivatives, 5-hydroxy-1-phenyl-4-(1,2,3,4-tetrahydroxybutyl)imidazolidine-2-thione (GA-1) and 1-phenyl-3-(2,4,5-trihydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran-3-yl)thiourea (GA-2), were synthesized as eco-friendly corrosion inhibitors for mild steel (MS) in 1 M HCl solution, and their inhibition performance were evaluated by electrochemical tests and surface analyses. The electrochemical tests and surface analyses indicate that both GA-1 and GA-2 have high inhibition performance. Especially for GA-2, the inhibition efficiency reaches 97.7% with a concentration of 0.64 mM. Theoretical calculations were also conducted to elucidate the adsorption mechanism of GA-1 and GA-2 on MS surface.

Quantum Chemical Study Aimed at Modeling Efficient Aza-BODIPY NIR Dyes: Molecular and Electronic Structure, Absorption, and Emission Spectra

A comprehensive study of the molecular structure of aza-BODIPY and its derivatives, obtained by introduction of one or more substituents, was carried out. We considered the changes in the characteristics of the electronic and geometric structure of the unsubstituted aza-BODIPY introducing the following substituents into the dipyrrin core; phenyl, 2-thiophenyl, 2-furanyl, 3-pyridinyl, 4-pyridinyl, 2-pyridinyl, and ethyl groups. The ground-state geometries of the unsubstituted Aza-BODIPY and 27 derivatives were computed at the PBE/6-31G(d) and CAM-B3LYP/6-31+G(d,p) levels of theory. The time-dependent density-functional theory (TDDFT) together with FC vibronic couplings was used to investigate their absorption and emission spectra.

Unifying Conceptual Density Functional and Valence Bond Theory: The Hardness-Softness Conundrum Associated with Protonation Reactions and Uncovering Complementary Reactivity Modes

In this study, we address the long-standing issue-arising prominently from conceptual density functional theory (CDFT)-of the relative importance of electrostatic, i.e., "hard-hard", versus spin-pairing, i.e., "soft-soft", interactions in determining regiochemical preferences. We do so from a valence bond (VB) perspective and demonstrate that VB theory readily enables a clear-cut resolution of both of these contributions to the bond formation/breaking process. Our calculations indicate that appropriate local reactivity descriptors can be used to gauge the magnitude of both interactions individually, e.g., Fukui functions or HOMO/LUMO orbitals for the spin-pairing/(frontier) orbital interactions and molecular electrostatic potentials (and/or partial charges) for the electrostatic interactions. In contrast to previous reports, we find that protonation reactions cannot generally be classified as either charge- or frontier orbital-controlled; instead, our results indicate that these two bonding contributions generally interplay in more subtle patterns, only giving the impression of a clear-cut dichotomy. Finally, we demonstrate that important covalent, i.e., spin pairing, reactivity modes can be missed when only a single spin-pairing/orbital interaction descriptor is considered. This study constitutes an important step in the unification of CDFT and VB theory.

Quantum Chemical Calculation of Molecular and Periodic Peptide and Protein Structures

Special-purpose classical force fields (FFs) provide good accuracy at very low computational cost, but their application is limited to systems for which potential energy functions are available. This excludes most metal-containing proteins or those containing cofactors. In contrast, the GFN2-xTB semiempirical quantum chemical method is parametrized for almost the entire periodic table. The accuracy of GFN2-xTB is assessed for protein structures with respect to experimental X-ray data. Furthermore, the results are compared with those of two special-purpose FFs, HF-3c, PM6-D3H4X, and PM7. The test sets include proteins without any prosthetic groups as well as metalloproteins. Crystal packing effects are examined for a set of smaller proteins to validate the molecular approach. For the proteins without prosthetic groups, the special purpose FF OPLS-2005 yields the smallest overall RMSD to the X-ray data but GFN2-xTB provides similarly good structures with even better bond-length distributions. For the metalloproteins with up to 5000 atoms, a good overall structural agreement is obtained with GFN2-xTB. The full geometry optimizations of protein structures with on average 1000 atoms in wall-times below 1 day establishes the GFN2-xTB method as a versatile tool for the computational treatment of various biomolecules with a good accuracy/computational cost ratio.

Clam-like Cyclotricatechylene-based Capsules: Identifying the Roles of Protonation State and Guests as well as the Drivers for Stability and (Anti-)Cooperativity

Cyclotricatechylene (ctcH₆ ) is a bowl-shaped macrocyclic compound that can be used as a building block for self-assembled capsules. ctcH₆ and its derivatives in various protonation states - here collectively labeled as CTC - form dimers that resemble the shape of a clam. These clam-shaped entities have been studied experimentally by Abrahams, Robson, and co-workers [B. F. Abrahams, N. J. FitzGerald, T. A. Hudson, R. Robson and T. Waters, Angew. Chem. Int. Ed. 2009, 48, 3129-3132] where the capsules acted as an excellent host for large alkali-metal cations. In this study, we present a detailed analysis based on accurate dispersion-corrected Density Functional Theory approaches that reveals the factors that stabilise such CTC-based capsules at different protonation states and their interaction with various encapsulated guests. Our results show that the capsules' overall stability results as an interplay of hydrogen bonding, London dispersion, and electrostatic effects. The most stable capsules with group-1 and group-2 cations as guests contain only six phenolic hydrogens, as opposed to the maximum possible number of twelve. Inclusion of larger alkali-metal cations is favoured due to larger London-dispersion contributions. Cations are favoured as guests over isoelectronic neutral species, as the resulting host-guest complexes experience additional stability due to cooperative effects. In fact, using the latter to drive the formation of specific capsules could be used in future strategies aimed at synthesising similar aggregates; our results provide an insightful understanding and useful guidance for such future endeavours.

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.

Structure Optimisation of Large Transition-Metal Complexes with Extended Tight-Binding Methods

Large transition-metal complexes are used in numerous areas of chemistry. Computer-aided theoretical investigations of such complexes are limited by the sheer size of real systems often consisting of hundreds to thousands of atoms. Accordingly, the development and thorough evaluation of fast semi-empirical quantum chemistry methods that are universally applicable to a large part of the periodic table is indispensable. Herein, we report on the capability of the recently developed GFNn-xTB method family for full quantum-mechanical geometry optimisation of medium to very large transition-metal complexes and organometallic supramolecular structures. The results for a specially compiled benchmark set of 145 diverse closed-shell transition-metal complex structures for all metals up to Hg are presented. Further the GFNn-xTB methods are tested on three established benchmark sets regarding reaction energies and barrier heights of organometallic reactions.

Generation of Tautomers Using Micro-p Ka's

Solutions of organic molecules containing one or more heterocycles with conjugated bonds may exist as a mixture of tautomers, but typically only a few of them are significantly populated even though the potential number grows combinatorially with the number of protonation and deprotonation sites. Generating the most stable tautomers from a given input structure is an important and challenging task, and numerous algorithms to tackle it have been proposed in the literature. This work describes a novel approach for tautomer prediction that involves the combined use of molecular mechanics, semiempirical quantum chemistry, and density functional theory. The key idea in our method is to identify the protonation and deprotonation sites using estimated micro-p K_a's for every atom in the molecule as well as in its nearest protonated and deprotonated forms. To generate tautomers in a systematic way with minimal bias, we then consider the full set of tautomers that arise from the combinatorial distribution of all such mobile protons among all protonatable sites, with efficient postprocessing to screen away high-energy species. To estimate the micro-p K_a's, we present a new method designed for the current task, but we emphasize that any alternative method can be used in conjunction with our basic algorithm. Our approach is therefore grounded in the computational prediction of physical properties in aqueous solution, in contrast to other approaches that may rely on the use of hard-coded rules of proton distribution, previously observed tautomerization patterns from a known chemical space, or human input. We present examples of the application of our algorithm to organic and drug-like molecules, with a focus on novel structures where traditional methods are expected to perform worse.

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations

The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and reaction space. The biasing potential given as a sum of Gaussian functions is expressed with the root-mean-square-deviation (RMSD) in Cartesian space as a metric for the collective variables. This choice makes the approach robust and generally applicable to three common problems (i.e., conformer search, chemical reaction space exploration in a virtual nanoreactor, and for guessing reaction paths). Because of the inherent locality of the atomic RMSD, functional group or fragment selective treatments are possible facilitating the investigation of catalytic processes where, for example, only the substrate is thermally activated. Due to the approximate character of the GFN2-xTB method, the resulting structure ensembles require further refinement with more sophisticated, for example, density functional or wave function theory methods. However, the approach is extremely efficient running routinely on common laptop computers in minutes to hours of computation time even for realistically sized molecules with a few hundred atoms. Furthermore, the underlying potential energy surface for molecules containing almost all elements ( Z = 1-86) is globally consistent including the covalent dissociation process and electronically complicated situations in, for example, transition metal systems. As examples, thermal decomposition, ethyne oligomerization, the oxidation of hydrocarbons (by oxygen and a P450 enzyme model), a Miller-Urey model system, a thermally forbidden dimerization, and a multistep intramolecular cyclization reaction are shown. For typical conformational search problems of organic drug molecules, the new MTD(RMSD) algorithm yields lower energy structures and more complete conformer ensembles at reduced computational effort compared with its already well performing predecessor.

GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

An extended semiempirical tight-binding model is presented, which is primarily designed for the fast calculation of structures and noncovalent interaction energies for molecular systems with roughly 1000 atoms. The essential novelty in this so-called GFN2-xTB method is the inclusion of anisotropic second order density fluctuation effects via short-range damped interactions of cumulative atomic multipole moments. Without noticeable increase in the computational demands, this results in a less empirical and overall more physically sound method, which does not require any classical halogen or hydrogen bonding corrections and which relies solely on global and element-specific parameters (available up to radon, Z = 86). Moreover, the atomic partial charge dependent D4 London dispersion model is incorporated self-consistently, which can be naturally obtained in a tight-binding picture from second order density fluctuations. Fully analytical and numerically precise gradients (nuclear forces) are implemented. The accuracy of the method is benchmarked for a wide variety of systems and compared with other semiempirical methods. Along with excellent performance for the "target" properties, we also find lower errors for "off-target" properties such as barrier heights and molecular dipole moments. High computational efficiency along with the improved physics compared to its precursor GFN-xTB makes this method well-suited to explore the conformational space of molecular systems. Significant improvements are furthermore observed for various benchmark sets, which are prototypical for biomolecular systems in aqueous solution.

SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model

A variety of fields would benefit from accurate [Formula: see text] predictions, especially drug design due to the effect a change in ionization state can have on a molecule's physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic [Formula: see text]s of 24 drug like small molecules. We recently built a general model for predicting [Formula: see text]s using a Gaussian process regression trained using physical and chemical features of each ionizable group. Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton. These features are fed into a Scikit-learn Gaussian process to predict microscopic [Formula: see text]s which are then used to analytically determine macroscopic [Formula: see text]s. Our Gaussian process is trained on a set of 2700 macroscopic [Formula: see text]s from monoprotic and select diprotic molecules. Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge. Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic. Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy. The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable.

High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge

Recent advances in the development of low-cost quantum chemical methods have made the prediction of conformational preferences and physicochemical properties of medium-sized drug-like molecules routinely feasible, with significant potential to advance drug discovery. In the context of the SAMPL6 challenge, macroscopic pKa values were blindly predicted for a set of 24 of such molecules. In this paper we present two similar quantum chemical based approaches based on the high accuracy calculation of standard reaction free energies and the subsequent determination of those pKa values via a linear free energy relationship. Both approaches use extensive conformational sampling and apply hybrid and double-hybrid density functional theory with continuum solvation to calculate free energies. The blindly calculated macroscopic pKa values were in excellent agreement with the experiment.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Fully Automated Quantum-Chemistry-Based Computation of Spin-Spin-Coupled Nuclear Magnetic Resonance Spectra

We present a composite procedure for the quantum‐chemical computation of spin–spin‐coupled ¹H NMR spectra for general, flexible molecules in solution that is based on four main steps, namely conformer/rotamer ensemble (CRE) generation by the fast tight‐binding method GFN‐xTB and a newly developed search algorithm, computation of the relative free energies and NMR parameters, and solving the spin Hamiltonian. In this way the NMR‐specific nuclear permutation problem is solved, and the correct spin symmetries are obtained. Energies, shielding constants, and spin–spin couplings are computed at state‐of‐the‐art DFT levels with continuum solvation. A few (in)organic and transition‐metal complexes are presented, and very good, unprecedented agreement between the theoretical and experimental spectra was achieved. The approach is routinely applicable to systems with up to 100–150 atoms and may open new avenues for the detailed (conformational) structure elucidation of, for example, natural products or drug molecules.