Unsymmetrical Imidazopyrimidine-Based Ligand and Bimetallic Complexes.

With bimetallic catalysts becoming increasingly important, the development of electronically and structurally diverse binucleating ligands is desired. This work describes the synthesis of unsymmetric ligand 2,7-di(pyridin-2-yl)imidazo[1,2-a]pyrimidine (dpip) that is achieved in four steps on a multigram scale in an overall 54% yield. The ability of dpip to act as a scaffold for the formation of bimetallic complexes is demonstrated with the one-step syntheses of the dicopper complex [Cu2(dpip)(µ-OH)(CF3COO)3] (4), the dipalladium complex [Pd2(dpip)(µ-OH)(CF3COO)2](CF3COO)·CF3COOH (5), and the dimeric dinickel complex [Ni4(dpip)2(µ-Cl)4Cl2MeOH6][2Cl] (6) in good yields (79-92%). All bimetallic complexes were characterized by spectroscopic methods and X-ray crystallography, which revealed metal-metal distances between 3.4821(9) and 4.106(2) Å. Additionally, quantum chemical calculations were conducted on complex 4 and an analogous 1,8-naphthyridine-based dicopper complex to investigate the differences between the imidazopyrimidine motif reported here and the widely used 1,8-naphthyridine motif. Natural bonding orbital (NBO) and Mayer bond order (MBO) analyses validated the ability of dpip to coordinate metals more strongly. Finally, NBO calculations quantified the differences in the binding energy between the two pockets of the unsymmetrical dpip ligand.

Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention.

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISµ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISµ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISµ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry.

As the legality of cannabis continues to evolve globally, there is a growing demand for methods that can accurately quantitate cannabinoids found in commercial products. However, the isobaric nature of many cannabinoids, along with variations in extraction methods and product formulations, makes cannabinoid quantitation by mass spectrometry (MS) challenging. Here, we demonstrate that differential mobility spectrometry (DMS) and tandem-MS can distinguish a set of seven cannabinoids, five of which are isobaric: Δ9-tetrahydrocannabinol (Δ9-THC), Δ8-THC, exo-THC, cannabidiol, cannabichromene, cannabinol, and cannabigerol. Analytes were detected as argentinated species ([M + Ag]+), which, when subjected to collision-induced dissociation, led to the unexpected discovery that argentination promotes distinct fragmentation patterns for each cannabinoid. The unique fragment ions formed were rationalized by discerning fragmentation mechanisms that follow each cannabinoid's MS3 behavior. The differing fragmentation behaviors between species suggest that argentination can distinguish cannabinoids by tandem-MS, although not quantitatively as some cannabinoids produce small amounts of a fragment ion that is isobaric with the major fragment generated by another cannabinoid. By adding DMS to the tandem-MS workflow, it becomes possible to resolve each cannabinoid in a pure N2 environment by deconvoluting the contribution of each cannabinoid to a specific fragmentation channel. To this end, we used DMS in conjunction with a multiple reaction monitoring workflow to assess cannabinoid levels in two cannabis extracts. Our methodology exhibited excellent accuracy, limits of detection (10-20 ppb depending on the cannabinoid), and linearity during quantitation by standard addition (R2 > 0.99).

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis.

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R2 = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R2 = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R2 = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R2 = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

MobCal-MPI 2.0: an accurate and parallelized package for calculating field-dependent collision cross sections and ion mobilities.

Ion mobility spectrometry (IMS), which can be employed as either a stand-alone instrument or coupled to mass spectrometry, has become an important tool for analytical chemistry. Because of the direct relation between an ion's mobility and its structure, which is intrinsically related to its collision cross section (CCS), IMS techniques can be used in tandem with computational tools to elucidate ion geometric structure. Here, we present MobCal-MPI 2.0, a software package that demonstrates excellent accuracy (RMSE 2.16%) and efficiency in calculating low-field CCSs via the trajectory method (≤30 minutes on 8 cores for ions with ≤70 atoms). MobCal-MPI 2.0 expands on its predecessor by enabling the calculation of high-field mobilities through the implementation of the 2nd order approximation to two-temperature theory (2TT). By further introducing an empirical correction to account for deviations between 2TT and experiment, MobCal-MPI 2.0 can compute accurate high-field mobilities that exhibit a mean deviation of <4% from experimentally measured values. Moreover, the velocities used to sample ion-neutral collisions were updated from a weighted to a linear grid, enabling the near-instantaneous evaluation of mobility/CCS at any effective temperature from a single set of N2 scattering trajectories. Several enhancements made to the code are also discussed, including updates to the statistical analysis of collision event sampling and benchmarking of overall performance.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time.

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Protomers of the green and cyan fluorescent protein chromophores investigated using action spectroscopy.

The photophysics of biochromophore ions often depends on the isomeric or protomeric distribution, yet this distribution, and the individual isomer contributions to an action spectrum, can be difficult to quantify. Here, we use two separate photodissociation action spectroscopy instruments to record electronic spectra for protonated forms of the green (pHBDI+) and cyan (Cyan+) fluorescent protein chromophores. One instrument allows for cryogenic (T = 40 ± 10 K) cooling of the ions, while the other offers the ability to perform protomer-selective photodissociation spectroscopy. We show that both chromophores are generated as two protomers when using electrospray ionisation, and that the protomers have partially overlapping absorption profiles associated with the S1 ← S0 transition. The action spectra for both species span the 340-460 nm range, although the spectral onset for the pHBDI+ protomer with the proton residing on the carbonyl oxygen is red-shifted by ≈40 nm relative to the lower-energy imine protomer. Similarly, the imine and carbonyl protomers are the lowest energy forms of Cyan+, with the main band for the carbonyl protomer red-shifted by ≈60 nm relative to the lower-energy imine protomer. The present strategy for investigating protomers can be applied to a wide range of other biochromophore ions.

σ-Holes in Iodonium Ylides: Halogen-Bond Activation of Carboxylic Acids, Phenols and Thiophenols May Enable Their X-H Insertion Reactions.

The first systematic evaluation of the electrostatic potential energy maps of iodonium ylides was conducted. We determined that they possess two σ-holes of differing electron deficiencies, with the more electropositive σ-hole located opposite the dative I-C bond to the ß-dicarbonyl motif, and the lesser electropositive σ-hole located opposite the iodoarene C-I bond. We also conducted the first systematic evaluation of carboxylic acids, phenols and thiophenols in the O/S-alkylation reaction of iodonium ylides. While carboxylic acids and thiophenols were found to be generally viable, only phenols possessing electron-withdrawing substituents were effective. This high-yielding and highly chemoselective reaction is believed to involve halogen-bond activation of heteroatoms, and nicely complements existing diazo-based methods for alkylation of acidic functional groups.

The hitchhiker's guide to dynamic ion-solvent clustering: applications in differential ion mobility spectrometry.

This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (e.g., methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (e.g., charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored.

Protonation-Induced Chirality Drives Separation by Differential Ion Mobility Spectrometry.

Upon development of a workflow to analyze (±)-Verapamil and its metabolites using differential mobility spectrometry (DMS), we noticed that the ionogram of protonated Verapamil consisted of two peaks. This was inconsistent with its metabolites, as each exhibited only a single peak in the respective ionograms. The unique behaviour of Verapamil was attributed to protonation at its tertiary amino moiety, which generated a stereogenic quaternary amine. The introduction of additional chirality upon N-protonation of Verapamil renders four possible stereochemical configurations for the protonated ion: (R,R), (S,S), (R,S), or (S,R). The (R,R)/(S,S) and (R,S)/(S,R) enantiomeric pairs are diastereomeric and thus exhibit unique conformations that are resolvable by linear and differential ion mobility techniques. Protonation-induced chirality appears to be a general phenomenon, as N-protonation of 12 additional chiral amines generated diastereomers that were readily resolved by DMS.

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry.

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion's CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86-683 Da and CCS 110-236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion's m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.

Predicting differential ion mobility behaviour in silico using machine learning.

Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means of accurately predicting differential ion mobility would benefit practitioners by significantly reducing the time associated with method development. Here, we report a machine learning (ML) approach that predicts dispersion curves in an N2 environment, which are the compensation voltages (CVs) required for optimal ion transmission across a range of separation voltages (SVs) between 1500 to 4000 V. After training a random-forest based model using the DMS information of 409 cationic analytes, dispersion curves were reproduced with a mean absolute error (MAE) of ≤ 2.4 V, approaching typical experimental peak FWHMs of ±1.5 V. The predictive ML model was trained using only m/z and ion-neutral collision cross section (CCS) as inputs, both of which can be obtained from experimental databases before being extensively validated. By updating the model via inclusion of two CV datapoints at lower SVs (1500 V and 2000 V) accuracy was further improved to MAE ≤ 1.2 V. This improvement stems from the ability of the "guided" ML routine to accurately capture Type A and B behaviour, which was exhibited by only 2% and 17% of ions, respectively, within the dataset. Dispersion curve predictions of the database's most common Type C ions (81%) using the unguided and guided approaches exhibited average errors of 0.6 V and 0.1 V, respectively.

Electronic spectroscopy of differential mobility-selected prototropic isomers of protonated para-aminobenzoic acid.

para-Aminobenzoic acid (PABA) was electrosprayed from mixtures of protic and aprotic solvents, leading to formation of two prototropic isomers in the gas phase whose relative populations depended on the composition of the electrospray solvent. The two ion populations were separated in the gas phase using differential mobility spectrometry (DMS) within a nitrogen-only environment at atmospheric pressure. Under high-field conditions, the two prototropic isomers eluted with baseline signal separation with the N-protonated isomer having a more negative CV shift than the O-protonated isomer, in accord with previous DMS studies. The conditions most favorable for formation and separation of each tautomer were used to trap each prototropic isomer in a quadrupole ion trap for photodissociation action spectroscopy experiments. Spectral interrogation of each prototropic isomer in the UV region (3-6 eV) showed good agreement with previously recorded spectra, although a previously reported band (4.8-5.4 eV) was less intense for the O-protonated isomer in our measured spectrum. Without DMS selection, the measured spectra contained features corresponding to both protonated isomers even when solvent conditions were optimised for formation of a single isomer. Interconversion between protonated isomers within the ion trap was observed when protic ESI solvents were employed, leading to spectral cross contamination even with mobility selection. CCSD vertical excitation energies and vertical gradient (VG) Franck-Condon simulations are presented and reproduce the measured spectral features with near-quantitative agreement, providing supporting evidence for spectral assignments.

UVPD spectroscopy of differential mobility-selected prototropic isomers of protonated adenine.

Two prototropic isomers of adenine are formed in an electrospray ion source and are resolved spatially in a differential mobility spectrometer before detection in a triple quadrupole mass spectrometer. Each isomer is gated in CV space before being trapped in the linear ion trap of the modified mass spectrometer, where they are irradiated by the tuneable output of an optical parametric oscillator and undergo photodissociation to form charged fragments with m/z 119, 109, and 94. The photon-normalised intensity of each fragmentation channel is measured and the action spectra for each DMS-gated tautomer are obtained. Our analysis of the action spectra, aided by calculated vibronic spectra and thermochemical data, allow us to assign the two signals in our measured ionograms to specific tautomers of protonated adenine.

UVPD Spectroscopy of Differential Mobility-Selected Prototropic Isomers of Rivaroxaban.

Two ion populations of protonated Rivaroxaban, [C19H18ClN3O5S + H]+, are separated under pure N2 conditions using differential mobility spectrometry prior to characterization in a hybrid triple quadrupole linear ion trap mass spectrometer. These populations are attributed to bare protonated Rivaroxaban and to a proton-bound Rivaroxaban-ammonia complex, which dissociates prior to mass-selecting the parent ion. Ultraviolet photodissociation (UVPD) and collision-induced dissociation (CID) studies indicate that both protonated Rivaroxaban ion populations are comprised of the computed global minimum prototropic isomer. Two ion populations are also observed when the collision environment is modified with 1.5% (v/v) acetonitrile. In this case, the protonated Rivaroxaban ion populations are produced by the dissociation of the ammonium complex and by the dissociation of a proton-bound Rivaroxaban-acetonitrile complex prior to mass selection. Again, both populations exhibit a similar CID behavior; however, UVPD spectra indicate that the two ion populations are associated with different prototropic isomers. The experimentally acquired spectra are compared with computed spectra and are assigned to two prototropic isomers that exhibit proton sharing between distal oxygen centers.

Action spectroscopy of the isolated red Kaede fluorescent protein chromophore.

Incorporation of fluorescent proteins into biochemical systems has revolutionized the field of bioimaging. In a bottom-up approach, understanding the photophysics of fluorescent proteins requires detailed investigations of the light-absorbing chromophore, which can be achieved by studying the chromophore in isolation. This paper reports a photodissociation action spectroscopy study on the deprotonated anion of the red Kaede fluorescent protein chromophore, demonstrating that at least three isomers-assigned to deprotomers-are generated in the gas phase. Deprotomer-selected action spectra are recorded over the S1 ← S0 band using an instrument with differential mobility spectrometry coupled with photodissociation spectroscopy. The spectrum for the principal phenoxide deprotomer spans the 480-660 nm range with a maximum response at ≈610 nm. The imidazolate deprotomer has a blue-shifted action spectrum with a maximum response at ≈545 nm. The action spectra are consistent with excited state coupled-cluster calculations of excitation wavelengths for the deprotomers. A third gas-phase species with a distinct action spectrum is tentatively assigned to an imidazole tautomer of the principal phenoxide deprotomer. This study highlights the need for isomer-selective methods when studying the photophysics of biochromophores possessing several deprotonation sites.

Ligand specificity and affinity in the sulforhodamine B binding RNA aptamer.

Binding affinity and selectivity are critical properties of aptamers that must be optimized for any application. The sulforhodamine B binding RNA aptamer (SRB-2) is a somewhat promiscuous aptamer that can bind ligands that vary markedly in shape, size and charge. Here we categorize potential ligands based on their binding mode and structural characteristics required for high affinity and selectivity. Several known and potential ligands of SRB-2 were screened for binding affinity using LSPR, ITC and NMR spectroscopy. The study shows that rhodamine B has the ideal structural and electrostatic properties for selective and high-affinity binding of the SRB-2 aptamer.

A parallelized molecular collision cross section package with optimized accuracy and efficiency.

Ion mobility-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas-phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion's collision cross section (CCS). Herein, we present a refined methodology for calculating ion CCS using parallel computing architectures that makes use of atom specific parameters, which we have called MobCal-MPI. Tuning of ion-nitrogen van der Waals potentials on a diverse calibration set of 162 molecules returned a RMSE of 2.60% in CCS calculations of molecules containing the elements C, H, O, N, F, P, S, Cl, Br, and I. External validation of the ion-nitrogen potential was performed on an additional 50 compounds not present in the validation set, returning a RMSE of 2.31% for the CCSs of these compounds. Owing to the use of parameters from the MMFF94 forcefield, the calibration of the van der Waals potential can be extended to additional atoms defined in the MMFF94 forcefield (i.e., Li, Na, K, Si, Mg, Ca, Fe, Cu, Zn). We expect that the work presented here will serve as a foundation for facile determination of molecular CCSs, as MobCal-MPI boasts up to 64-fold speedups over traditional calculation packages.

Blue LED Irradiation of Iodonium Ylides Gives Diradical Intermediates for Efficient Metal-free Cyclopropanation with Alkenes.

A facile and highly chemoselective synthesis of doubly activated cyclopropanes is reported where mixtures of alkenes and ß-dicarbonyl-derived iodonium ylides are irradiated with light from blue LEDs. This metal-free synthesis gives cyclopropanes in yields up to 96 %, is operative with cyclic and acyclic ylides, and proceeds with a variety of electronically-diverse alkenes. Computational analysis explains the high selectivity observed, which derives from exclusive HOMO to LUMO excitation, instead of free carbene generation. The procedure is operationally simple, uses no photocatalyst, and provides access in one step to important building blocks for complex molecule synthesis.

Identifying Fenton-Reacted Trimethoprim Transformation Products Using Differential Mobility Spectrometry.

A transformation product of trimethoprim, a contaminant of emerging concern in the environment, is generated using an electro-assisted Fenton reaction and analyzed using differential mobility spectrometry (DMS) in combination with MS/MS techniques and quantum chemical calculations to develop a rapid method for identification. DMS is used as a prefilter to separate positional isomers prior to subsequent identification by mass spectrometric analyses. Collision induced dissociation of each DMS separated species is used to reveal fragmentation patterns that can be correlated to specific isomer structures. Analysis of the experimental data and supporting quantum chemical calculations show that methylene-hydroxylated and methoxy-containing phenyl ring hydroxylated transformation products are observed. The proposed methodology outlines a high-throughput technique to determine transformation products of small molecules accurately, in a short time and requiring minimal sample concentrations (<25 ng/mL).