TS-tools: Rapid and automated localization of transition states based on a textual reaction SMILES input.

Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.

Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels-Alder Reaction Outcomes.

The application of machine learning models to the prediction of reaction outcomes currently needs large and/or highly featurized data sets. We show that a chemistry-aware model, NERF, which mimics the bonding changes that occur during reactions, allows for highly accurate predictions of the outcomes of Diels-Alder reactions using a relatively small training set, with no pretraining and no additional features. We establish a diverse data set of 9537 intramolecular, hetero-, aromatic, and inverse electron demand Diels-Alder reactions. This data set is used to train a NERF model, and the performance is compared against state-of-the-art classification and generative machine learning models across low- and high-data regimes, with and without pretraining. The predictive accuracy (regio- and site selectivity in the major product) achieved by NERF exceeds 90% when as little as 40% of the data set is used for training. Another high-performing model, Chemformer, requires a larger training data set (>45%) and pretraining to reach 90% Top-1 accuracy. Accurate predictions of less-represented reaction subclasses, such as those involving heteroatomic or aromatic substrates, require higher percentages of training data. We also show how NERF can use small amounts of additional training data to quickly learn new systems and improve its overall understanding of reactivity. Synthetic chemists stand to benefit as this model can be rapidly expanded and tailored to areas of chemistry corresponding to the low-data regime.

Designed Local Electric Fields-Promising Tools for Enzyme Engineering.

Designing efficient catalysts is one of the ultimate goals of chemists. In this Perspective, we discuss how local electric fields (LEFs) can be exploited to improve the catalytic performance of supramolecular catalysts, such as enzymes. More specifically, this Perspective starts by laying out the fundamentals of how local electric fields affect chemical reactivity and review the computational tools available to study electric fields in various settings. Subsequently, the advances made so far in optimizing enzymatic electric fields through targeted mutations are discussed critically and concisely. The Perspective ends with an outlook on some anticipated evolutions of the field in the near future. Among others, we offer some pointers on how the recent data science/machine learning revolution, engulfing all science disciplines, could potentially provide robust and principled tools to facilitate rapid inference of electric field effects, as well as the translation between optimal electrostatic environments and corresponding chemical modifications.

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery.

Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.

Voltage-driven control of single-molecule keto-enol equilibrium in a two-terminal junction system.

Keto-enol tautomerism, describing an equilibrium involving two tautomers with distinctive structures, provides a promising platform for modulating nanoscale charge transport. However, such equilibria are generally dominated by the keto form, while a high isomerization barrier limits the transformation to the enol form, suggesting a considerable challenge to control the tautomerism. Here, we achieve single-molecule control of a keto-enol equilibrium at room temperature by using a strategy that combines redox control and electric field modulation. Based on the control of charge injection in the single-molecule junction, we could access charged potential energy surfaces with opposite thermodynamic driving forces, i.e., exhibiting a preference for the conducting enol form, while the isomerization barrier is also significantly reduced. Thus, we could selectively obtain desired and stable tautomers, which leads to significant modulation of the single-molecule conductance. This work highlights the concept of single-molecule control of chemical reactions on more than one potential energy surface.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

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.

Machine Learning-Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential.

Bioorthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bioorthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate reactions with promising kinetic and thermodynamic properties for bioorthogonal click applications. Sampling only around 0.05 % of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT-computed activation and reaction energies within ∼2-3 kcal/mol across the entire space. Applying this model to screen the full search space through iterative rounds of learning, we identify a broad pool of candidate reactions with rich structural diversity, which can be used as a starting point or source of inspiration for future experimental development of both azide-based and non-azide-based bioorthogonal click reactions.

Local Electric Fields: From Enzyme Catalysis to Synthetic Catalyst Design.

This Mini-Review Article outlines recent advances in the study of local electric field (LEF) governed enzyme catalysis and the application of the LEF principle in synthetic catalyst design. We start by discussing the electrostatics principles that drive enzyme catalysis, and its experimental verifications through vibrational Stark spectroscopy. Subsequently, we describe aspects of LEFs other than catalysis, i.e., induction of mechanistic crossovers, among others. Here, we focus on the early work done using computational tools, along with some recent contributions. Following an in-depth discussion of the role of LEFs in enzyme catalysis, we then highlight some recent works on designed local electric fields (D-LEF) and their applications in organic synthesis. Subsequently, we turn to D-LEFs in synthetic enzymes and supramolecular systems (cf. the work by the Head-Gordon group). We end by discussing some of the software packages that have been developed to analyze local electric fields computationally. Overall, the present Mini-Review Article paints an insightful picture of the current state of the art using LEF in enzyme catalysis and its application for further bioengineering and synthetic organic frameworks in a broad perspective.

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.

Can the Philicity of Radicals Be Influenced by Oriented External Electric Fields?

Herein, the effects of an electric field on radicals are investigated for a set of model radicals with varying complexity. An electric field impacts the intrinsic philicity of a radical, as quantified by the global electrophilicity index, ω. The extent of change in philicity depends on the directionality and strength of the applied electric field and the dipole moment and polarizability of the radical.

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.

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 SN2 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 SN2 and E2 reaction pathways.

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.

Extending conceptual DFT to include additional variables: oriented external electric field.

The extension of the E = E[N, v] functional for exploring chemical reactivity in a conceptual DFT context to include external electric fields is discussed. Concentrating on the case of a homogeneous field the corresponding response functions are identified and integrated, together with the conventional response functions such as permanent dipole moment and polarizability, in an extended response function tree associated with the E = E[N, v, ε] functional. In a case study on the dihalogens F2, Cl2, Br2, I2 the sensitivity of condensed atomic charges (∂q/∂ε) is linked to the polarizability of the halogen atoms. The non-integrated (∂ρ(r)/∂ε) response function, directly related to the field induced density change, is at the basis of these features. It reveals symmetry breaking for a perpendicular field, not detectable in its atom condensed counterpart, and accounts for the induced dipole moment directly related to the molecular polarizability. The much higher sensitivity of the electronic chemical potential/electronegativity as compared to the chemical hardness is highlighted. The response of the condensed Fukui functions to a parallel electric field increases when going down in the periodic table and is interpreted in terms of the extension of the outer contours in the non-condensed Fukui function. In the case of a perpendicular field the (∂f(r)/∂ε) response function hints at stereoselectivity with a preferential side of attack which is not retrieved in its condensed form. In an application the nucleophilic attack on the carbonyl group in H2CO is discussed. Similar to the dihalogens, stereoselectivity is displayed in the Fukui function for nucleophilic attack (f+) in the case of a perpendicular electric field, and opposite to the one that would arise based on the induced density. Disentangling the expression for the evolution of the Fukui function in the presence of an electric field reveals that this difference can be traced back to local differences in the polarization or induced density between the anionic and the neutral system. This difference may be exploited, e.g. for an appropriately substituted H2CO, to generate enantioselectivity.

Single-molecule conductance in a unique cross-conjugated tetra(aminoaryl)ethene.

A 1,1,2,2-tetrakis(4-aminophenyl)ethene with three paths of π-conjugation, linear-cis, linear-trans and a cross-conjugation, has been prepared. The molecule is able to bind to gold electrodes forming molecular junctions for single-molecule conductance measurements. Only two regimes of conduction are found experimentally. The modelling of the conductance allows to assign them to through-bond transmission in the linear case, while the cross-conjugated channel is further assisted by through-space transmission, partially alleviating the destructive quantum interference.

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.

Electric-Field Mediated Chemistry: Uncovering and Exploiting the Potential of (Oriented) Electric Fields to Exert Chemical Catalysis and Reaction Control.

This Perspective discusses oriented external-electric-fields (OEEF), and other electric-field types, as "smart reagents", which enable in principle control over wide-ranging aspects of reactivity and structure. We discuss the potential of OEEFs to control nonredox reactions and impart rate-enhancement and selectivity. An OEEF along the "reaction axis", which is the direction whereby electronic reorganization converts reactants' to products' bonding, will accelerate reactions, control regioselectivity, induce spin-state selectivity, and elicit mechanistic crossovers. Simply flipping the direction of the OEEF will lead to inhibition. Orienting the OEEF off the reaction axis enables control over stereoselectivity, enantioselectivity, and product selectivity. For polar/polarizable reactants, the OEEF itself will act as tweezers, which orient the reactants and drive their reaction. OEEFs also affect bond-dissociation energies and dissociation modes (covalent vs ionic), as well as alteration of molecular geometries and supramolecular aggregation. The "key" to gaining access to this toolbox provided by OEEFs is microscopic control over the alignment between the molecule and the applied field. We discuss the elegant experimental methods which have been used to verify the theoretical predictions and describe various alternative EEF sources and prospects for upscaling OEEF catalysis in solvents. We also demonstrate the numerous ways in which the OEEF effects can be mimicked by use of (designed) local-electric fields (LEFs), i.e., by embedding charges or dipoles into molecules. LEFs and OEEFs are shown to be equivalent and to obey the same ground rules. Outcomes are exemplified for Diels-Alder cycloadditions, oxidative addition of bonds by transition-metal complexes, H-abstractions by oxo-metal species, ionic cleavage of halogen bonds, methane activation, etc.

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

How do local reactivity descriptors, such as the Fukui function and the local spin density distribution, shape the potential energy surface (PES) associated with chemical reactions and thus govern reactivity trends and regioselective preferences? This is the question that is addressed here through a qualitative valence bond (VB) analysis. We demonstrate that common density functional theory (DFT)-based local reactivity descriptors can essentially be regarded-in one way or another-as indirect measures of delocalization, i.e., resonance stabilization, of the reactants within VB theory. The inherent connection between (spatial) delocalization and (energetic) resonance stabilization embedded in VB theory provides a natural and elegant framework for analyzing and comprehending the impact of individual local reactivity descriptors on the global PES. Our analysis provides new insights into the role played by local reactivity descriptors and illustrates under which conditions they can sometimes fail to predict reactivity trends and regioselective preferences, e.g., in the case of ambident reactivity. This treatment constitutes a first step toward a unification of VB theory and conceptual DFT.