How Do Local Reactivity Descriptors Shape the Potential Energy Surface Associated with Chemical Reactions? The Valence Bond Delocalization Perspective

Multi-channel electron transfer induced by polyvanadate in metal-organic framework for boosted peroxymonosulfate activation

Catalytic peroxymonosulfate (PMS) activation processes don't solely rely on electron transfer from dominant metal centers due to the complicated composition and interface environment of catalysts. Herein the synthesis of a cobalt based metal-organic framework containing polyvanadate [V4O12]4- cluster, Co2(V4O12)(bpy)2 (bpy = 4,4'-bipyridine), is presented. The catalyst demonstrates superior degradation activity toward various micropollutants, with higher highest occupied molecular orbital (HOMO), via nonradical attack. The X-ray absorption spectroscopy and density functional theory (DFT) calculations demonstrate that Co sites act as both PMS trapper and electron donor. In situ spectral characterizations and DFT calculations reveal that the terminal oxygen atoms in the [V4O12]4- electron sponge could interact with the terminal hydrogen atoms in PMS to form hydrogen bonds, promoting the generation of SO5* intermediate via both dynamic pull and direct electron transfer process. Further, Co2(V4O12)(bpy)2 exhibits long-term water purification ability, up to 40 h, towards actual wastewater discharged from an ofloxacin production factory. This work not only presents an efficient catalyst with an electron sponge for water environmental remediation via nonradical pathway, but also provides fundamental insights into the Fenton-like reaction mechanism.

Reaction mechanism - explored with the unified reaction valley approach

One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly.

Phenoxy- and Phenylamino-Heterocyclic Quinones: Synthesis and Preliminary Anti-Pancreatic Cancer Activity

The successful application of fragment-based drug discovery strategy for the efficient synthesis of phenoxy- or phenylamino-2-phenyl-benzofuran, -benzoxazole and -benzothiazole quinones is described. Interestingly, in the final step of the synthesis of the target compounds, unusual results were observed on the regiochemistry of the reaction of bromoquinones with phenol and aniline. A theoretical study was carried out for better understanding the factors that control the regiochemistry of these reactions. The substituted heterocyclic quinones were evaluated in vitro to determine their cytotoxicity by the MTT method in three pancreatic cancer cell lines (MIA-PaCa-2, BxPC-3, and AsPC-1). Phenoxy benzothiazole quinone 26a showed potent cytotoxic activity against BxPC-3 cell lines, while phenylamino benzoxazole quinone 20 was the most potent on MIA-PaCa-2 cells. Finally, electrochemical properties of these quinones were determined to correlate with a potential mechanism of action. All these results, indicate that the phenoxy quinone fragment led to compounds with increased activity against pancreatic cancer cells.

Design of 2,5-furandicarboxylic based polyesters degraded in different environmental conditions: Comprehensive experimental and theoretical study

Nowadays, the promotion and application of aliphatic-aromatic copolyesters, such as poly (butylene adipate-co-terephthalate) (PBAT), are growing into a general trend. Although the structures of diacids exerted substantial impacts on degradation behavior, the underlying mechanisms have rarely been studied. In this work, 2,5-Furandicarboxylic acid was combined with succinic acid (PBSF), adipic acid (PBAF) and diglycolic acid (PBDF) to prepare three kinds of copolyesters. They showed unique degradation behaviors in buffer, enzyme environment and artificial seawater. These characteristics are closely related to the structural compositions of diacids. PBAFs displayed impressive biodegradability when catalyzed by Candida antarctica lipase B (CALB), while the more hydrophilic PBDFs exhibited faster hydrolysis in both buffer and artificial seawater. PBSFs, with hydrophobic and short segments, obtained a relatively slower rate of hydrolysis and enzymatic degradation. The reactivity sites and hydrolytic pathway were revealed by the combination of DFT calculation and Fukui function analysis. MD simulations, QM/MM optimizations and theozyme calculations showed that PBAF-CALB was prone to form a pre-reaction state, leading to the reduced energy barrier in the acylation process. This work revealed the effects of different structural features of diacids on polymer degradation and paved a way to design target biodegradable polymers in different degradation conditions.

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability

There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generalizability, and explainability of the quantum mechanics-augmented graph neural network (ml-QM-GNN) architecture as applied to the prediction of regioselectivity (classification) and of activation energies (regression). In our hybrid QM-augmented model architecture, structure-based representations are first used to predict a set of atom- and bond-level reactivity descriptors derived from density functional theory calculations. These estimated reactivity descriptors are combined with the original structure-based representation to make the final reactivity prediction. We demonstrate that our model architecture leads to significant improvements over structure-based GNNs in not only overall accuracy but also in generalization to unseen compounds. Even when provided training sets of only a couple hundred labeled data points, the ml-QM-GNN outperforms other state-of-the-art structure-based architectures that have been applied to these tasks as well as descriptor-based (linear) regressions. As a primary contribution of this work, we demonstrate a bridge between data-driven predictions and conceptual frameworks commonly used to gain qualitative insights into reactivity phenomena, taking advantage of the fact that our models are grounded in (but not restricted to) QM descriptors. This effort results in a productive synergy between theory and data science, wherein QM-augmented models provide a data-driven confirmation of previous qualitative analyses, and these analyses in turn facilitate insights into the decision-making process occurring within ml-QM-GNNs.

Two Sides of Quantum-Based Modeling of Enzyme-Catalyzed Reactions: Mechanistic and Electronic Structure Aspects of the Hydrolysis by Glutamate Carboxypeptidase

We report the results of a computational study of the hydrolysis reaction mechanism of N-acetyl-l-aspartyl-l-glutamate (NAAG) catalyzed by glutamate carboxypeptidase II. Analysis of both mechanistic and electronic structure aspects of this multistep reaction is in the focus of this work. In these simulations, model systems are constructed using the relevant crystal structure of the mutated inactive enzyme. After selection of reaction coordinates, the Gibbs energy profiles of elementary steps of the reaction are computed using molecular dynamics simulations with ab initio type QM/MM potentials (QM/MM MD). Energies and forces in the large QM subsystem are estimated in the DFT(PBE0-D3/6-31G**) approximation. The established mechanism includes four elementary steps with the activation energy barriers not exceeding 7 kcal/mol. The models explain the role of point mutations in the enzyme observed in the experimental kinetic studies; namely, the Tyr552Ile substitution disturbs the "oxyanion hole", and the Glu424Gln replacement increases the distance of the nucleophilic attack. Both issues diminish the substrate activation in the enzyme active site. To quantify the substrate activation, we apply the QTAIM-based approaches and the NBO analysis of dynamic features of the corresponding enzyme-substrate complexes. Analysis of the 2D Laplacian of electron density maps allows one to define structures with the electron density deconcentration on the substrate carbon atom, i.e., at the electrophilic site of reactants. The similar electronic structure element in the NBO approach is a lone vacancy on the carbonyl carbon atom in the reactive species. The electronic structure patterns revealed in the NBO and QTAIM-based analyses consistently clarify the reactivity issues in this system.

Kick-Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction

Here, we introduce a hybrid method, named Kick-Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms.

Bimodal Evans-Polanyi Relationships in Hydrogen Atom Transfer from C(sp3)-H Bonds to the Cumyloxyl Radical. A Combined Time-Resolved Kinetic and Computational Study

The applicability of the Evans-Polanyi (EP) relationship to HAT reactions from C(sp3)-H bonds to the cumyloxyl radical (CumO•) has been investigated. A consistent set of rate constants, kH, for HAT from the C-H bonds of 56 substrates to CumO•, spanning a range of more than 4 orders of magnitude, has been measured under identical experimental conditions. A corresponding set of consistent gas-phase C-H bond dissociation enthalpies (BDEs) spanning 27 kcal mol-1 has been calculated using the (RO)CBS-QB3 method. The log kH' vs C-H BDE plot shows two distinct EP relationships, one for substrates bearing benzylic and allylic C-H bonds (unsaturated group) and the other one, with a steeper slope, for saturated hydrocarbons, alcohols, ethers, diols, amines, and carbamates (saturated group), in line with the bimodal behavior observed previously in theoretical studies of reactions promoted by other HAT reagents. The parallel use of BDFEs instead of BDEs allows the transformation of this correlation into a linear free energy relationship, analyzed within the framework of the Marcus theory. The ΔG⧧HAT vs ΔG°HAT plot shows again distinct behaviors for the two groups. A good fit to the Marcus equation is observed only for the saturated group, with λ = 58 kcal mol-1, indicating that with the unsaturated group λ must increase with increasing driving force. Taken together these results provide a qualitative connection between Bernasconi's principle of nonperfect synchronization and Marcus theory and suggest that the observed bimodal behavior is a general feature in the reactions of oxygen-based HAT reagents with C(sp3)-H donors.

Resolving Entangled Reactivity Modes through External Electric Fields and Substitution: Application to E2/SN2 Reactions

In this study, we explore strategies to resolve entangled reactivity modes. More specifically, we consider the competition between S_N2 and E₂ reaction pathways for alkyl halides and nucleophiles/bases. We first demonstrate that the emergence of an E₂-preference is associated with an enhancement of the magnitude of the resonance stabilization in the transition-state (TS) region, resulting from the improved mixing of electrostatically stabilized valence bond structures into the TS wavefunction. Subsequently, we show that the TS resonance energy can be tuned selectively and rationally either through the application of an oriented external electric field directed along the C-C axis of the alkyl halide or through a regular substitution approach of the C-C moiety. We end our study by demonstrating that the insights gained from our analysis enable one to rationalize the main reactivity trends emerging from a recently published large database of competing S_N2 and E₂ reaction pathways.

Density Functional Theory Study into the Reaction Mechanism of Isonitrile Biosynthesis by the Nonheme Iron Enzyme ScoE

The nonheme iron enzyme ScoE catalyzes the biosynthesis of an isonitrile substituent in a peptide chain. To understand details of the reaction mechanism we created a large active site cluster model of 212 atoms that contains substrate, the active oxidant and the first- and second-coordination sphere of the protein and solvent. Several possible reaction mechanisms were tested and it is shown that isonitrile can only be formed through two consecutive catalytic cycles that both use one molecule of dioxygen and α-ketoglutarate. In both cycles the active species is an iron(IV)-oxo species that in the first reaction cycle reacts through two consecutive hydrogen atom abstraction steps: first from the N–H group and thereafter from the C–H group to desaturate the NH-CH2 bond. The alternative ordering of hydrogen atom abstraction steps was also tested but found to be higher in energy. Moreover, the electronic configurations along that pathway implicate an initial hydride transfer followed by proton transfer. We highlight an active site Lys residue that is shown to donate charge in the transition states and influences the relative barrier heights and bifurcation pathways. A second catalytic cycle of the reaction of iron(IV)-oxo with desaturated substrate starts with hydrogen atom abstraction followed by decarboxylation to give isonitrile directly. The catalytic cycle is completed with a proton transfer to iron(II)-hydroxo to generate the iron(II)-water resting state. The work is compared with experimental observation and previous computational studies on this system and put in a larger perspective of nonheme iron chemistry.

Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions

To develop an approach to pre-emptively predict the existence of major reaction modes associated with a chemical system, based on exclusive consideration of reactant properties, we build herein on the valence bond perspective of chemical reactivity. In this perspective, elementary chemical reactions are conceptualized as crossovers between individual diabatic/semilocalized states. As demonstrated, the spacings between the main diabatic states in the reactant geometries-the so-called promotion energies-contain predictive information about which types of crossings are likely to occur on a potential energy surface, facilitating the identification of potential transition states and products. As an added bonus, promotion energy analysis provides direct insight into the impact of environmental effects, e.g., the presence of (polar) solvents and/or (local) electric fields, on a mechanistic landscape. We illustrate the usefulness of our approach by focusing on model nucleophilic and electrophilic aromatic substitution reactions. Overall, we envision our analysis to be useful not only as a tool for conceptualizing individual mechanistic landscapes but also as a facilitator of systematic reaction-network exploration efforts. Because the emerging VB descriptors are computationally inexpensive (and can alternatively be inferred through machine learning), they could be evaluated on-the-fly as part of an exploration algorithm. The so-predicted reaction modes could subsequently be examined in detail through computationally more-demanding methods.

Superposition of waves or densities: Which is the nature of chemical resonance?

Resonance is a fundamental and widely used concept in chemistry, but there exist two distinct theories of chemical resonance, based on quite different and incompatible premises: the wave-function-based resonance theory (WFRT), assuming the superposition of wave functions, versus the density-matrix-based resonance theory (DMRT), which interprets the resonance phenomenon as the superposition of density matrices. The latter theory, best known to the chemistry community as the natural resonance theory (NRT), has received much more popularity than the WFRT. In this contribution, the DMRT is shown to be inherently inadequate: (i) the exact density matrix expansion is mathematically impossible unless unphysical negative weights are introduced; (ii) any approximate density matrix representing the resonance hybrid lacks the idempotent property. Therefore, the validity of the NRT ansatz should be seriously questioned. The WFRT seems the only reasonable explanation of resonance so far, and has been shown to provide valuable insights into diverse chemical problems.

Modulating the radical reactivity of phenyl radicals with the help of distonic charges: it is all about electrostatic catalysis

This manuscript reports the modulation of H-abstraction reactivity of phenyl radicals by (positive and negative) distonic ions. Specifically, we focus on the origins of this modulating effect: can the charged functional groups truly be described as "extreme forms of electron-withdrawing/donating substituents" - implying a through-bond mechanism - as argued in the literature, or is the modulation mainly caused by through-space effects? Our analysis indicates that the effect of the remote charges can be mimicked almost perfectly with the help of a purely electrostatic treatment, i.e. replacing the charged functional groups by a simple uniform electric field is sufficient to recover the quantitative reactivity trends. Hence, through-space effects dominate, whereas through-bond effects play a minor role at best. We elucidate our results through a careful Valence Bond (VB) analysis and demonstrate that such a qualitative analysis not only reveals through-space dominance, but also demonstrates a remarkable reversal in the reactivity trends of a given polarity upon a rational modification of the reaction partner. As such, our findings demonstrate that VB theory can lead to productive predictions about the behaviour of distonic radical ions.

How external perturbations affect the chemoselectivity of substrate activation by cytochrome P450 OleTJE

Cytochrome P450 enzymes are versatile biocatalysts found in most forms of life. Generally, the cytochrome P450s react with dioxygen and hence are haem-based mono-oxygenases; however, in specific isozymes, H2O2 rather than O2 is used and these P450s act as peroxygenases. The P450 OleTJE is a peroxygenase that binds long to medium chain fatty acids and converts them to a range of products originating from Cα-hydroxylation, Cβ-hydroxylation, Cα-Cβ desaturation and decarboxylation of the substrate. There is still controversy regarding the details of the reaction mechanism of P450 OleTJE; how the products are formed and whether the product distributions can be influenced by external perturbations. To gain further insights into the structure and reactivity of P450 OleTJE, we set up a range of large active site model complexes as well as full enzymatic structures and did a combination of density functional theory studies and quantum mechanics/molecular mechanics calculations. In particular, the work focused on the mechanisms leading to these products under various reaction conditions. Thus, for a small cluster model, we find a highly selective Cα-hydroxylation pathway that is preferred over Cβ-H hydrogen atom abstraction by at least 10 kcal mol-1. Introduction of polar residues to the model, such as an active site protonated histidine residue or through external electric field effects, lowers the Cβ-H hydrogen atom abstraction barriers are lowered, while a full QM/MM model brings the Cα-H and Cβ-H hydrogen atom abstraction barriers within 1 kcal mol-1. Our studies; therefore, implicate that environmental effects in the second-coordination sphere can direct and guide selectivities in enzymatic reaction mechanisms.

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C-H functionalization, aromatic C-X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings.

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.

Dynamical properties of enzyme-substrate complexes disclose substrate specificity of the SARS-CoV-2 main protease as characterized by the electron density descriptors

A dynamical approach is proposed to discriminate between reactive (rES) and nonreactive (nES) enzyme-substrate complexes taking the SARS-CoV-2 main protease (Mpro) as an important example. Molecular dynamics simulations with the quantum mechanics/molecular mechanics potentials (QM(DFT)/MM-MD) followed by the electron density analysis are employed to evaluate geometry and electronic properties of the enzyme with different substrates along MD trajectories. We demonstrate that mapping the Laplacian of the electron density and the electron localization function provides easily visible images of the substrate activation that allow one to distinguish rES and nES. The computed fractions of reactive enzyme-substrate complexes along MD trajectories well correlate with the findings of recent experimental studies on the substrate specificity of Mpro. The results of our simulations demonstrate the role of the theory level used in QM subsystems for a proper description of the nucleophilic attack of the catalytic cysteine residue in Mpro. The activation of the carbonyl group of a substrate is correctly characterized with the hybrid DFT functional PBE0, whereas the use of a GGA-type PBE functional, that lacks the admixture of the Hartree-Fock exchange fails to describe activation.

Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.