Methyl Anion Affinities of the Canonical Organic Functional Groups

Atom-based machine learning for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and chemical stability

Nucleophilicity and electrophilicity are important properties for evaluating the reactivity and selectivity of chemical reactions. It allows the ranking of nucleophiles and electrophiles on reactivity scales, enabling a better understanding and prediction of reaction outcomes. Building upon our recent work (N. Ree, A. H. Göller and J. H. Jensen, Automated quantum chemistry for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and covalent inhibitors, Digit. Discov., 2024, 3, 347-354), we introduce an atom-based machine learning (ML) approach for predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs) to estimate nucleophilicity and electrophilicity, respectively. The ML models are trained and validated on QM-derived data from around 50 000 neutral drug-like molecules, achieving Pearson correlation coefficients of 0.97 for MCA and 0.95 for MAA on the held-out test sets. In addition, we demonstrate the ML approach on two different applications: first, as a general tool for filtering retrosynthetic routes based on chemical selectivity predictions, and second, as a tool for assessing the chemical stability of esters and carbamates towards hydrolysis reactions. The code is freely available on GitHub under the MIT open source license and as a web application at https://www.esnuel.org.

Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction

Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive framework based on the interaction of molecular orbitals that operates on mechanistic-level radical reactions. Given our chemistry-aware model, all predictions are provided with different levels of interpretability. Our models are trained and evaluated using the RMechDB database of radical reaction steps. Our model predicts the correct orbital interaction and products for 96% of the test reactions in RMechDB. By chaining these predictions, we perform a pathway search capable of identifying all intermediates and byproducts of a radical reaction. We test the pathway search on two classes of problems in atmospheric and polymerization chemistry. RMechRP is publicly available online at https://deeprxn.ics.uci.edu/rmechrp/.

Defining the Synthetic Scope of ortho-Quinone Methides by Quantifying their Electrophilicity

A series of aryl-substituted ortho-quinone methides (oQMs) was synthesised and structurally characterised. Kinetic studies of the nucleophilic additions of carbanions (reference nucleophiles) to oQMs were used to determine second-order rate constants k2 for the carbon-carbon bond forming reactions (20 °C, DMSO) at the oQMs' exocyclic π-bond. Analysing the kinetic data by the linear free energy relationship lg k2=sN(N+E) revealed the Mayr electrophilicities E of the oQMs. The electrophilicities E of oQMs correlate linearly with Hammett substituent constants and experimentally determined reduction potentials Ep red as well as with quantum-chemically calculated methyl anion affinities (MAAs), which provides valuable tools for prediciting the reactivity of further types of oQMs. Embedding the oQMs in Mayr's reactivity scales enables to predict novel nucleophilic reaction partners for oQMs and can productively be used to prepare simple Michael adducts as well as 4+2 or 4+1 cyclisation products as demonstrated in this work by several novel reactions with neutral or negatively charged C-, N-, and S-nucleophiles.

Electrophilic Intermediates in the Nef and Meyer Reactions: A Computational Study

The generation, interconversion, and reactivity of electrophilic species generated upon activation of nitroalkanes with protic acids (the Nef and Meyer reactions) were investigated by quantum-chemical calculations. N,N-Bis(hydroxy)iminium (R2C═N+(OH)2) and N-oxoiminium (R2C═N+═O) cations were shown to be produced independently from aci-nitroalkanes, while N-hydroxynitrilium cations (RC≡N+-OH) were formed via nearly barrierless C-H bond cleavage in N-oxoiminium cations. The N-oxoiminium and N-hydroxynitrilium cations whose formation is favored under highly acidic anhydrous conditions are strong electrophiles capable of reacting even with nonactivated arenes under ambient conditions. The N-oxoiminium cations R2C═N+═O are highly unusual ambident species containing three contiguous electrophilic centers (C, N, and O atoms). Nucleophilic addition at the oxygen atom is less preferred than the C- and N-attack yet possible in an intramolecular variant. These computational results shed light on some key aspects of the mechanisms of the Nef and Meyer reactions and predict the possibility of numerous interrupted versions of these reactions.

Electrophile-Arene Affinity: An Energy Scale for Evaluating the Thermodynamics of Electrophilic Dearomatization Reactions

Rational design and development of organic reactions are lofty goals in synthetic chemistry. Quantitative description of the properties of molecules and reactions by physical organic parameters plays an important role in this regard. In this Article, we report an energy scale, namely, electrophile-arene affinity (EAA), for evaluating the thermodynamics of electrophilic dearomatization reactions, a class of important transformations that can rapidly build up molecular complexity and structural diversity by converting planar aromatic compounds into three-dimensional cyclic molecules. The acquisition of EAA data can be readily achieved by theoretically calculating the enthalpy changes (ΔH) of the hypothetical reactions of various (cationic) electrophiles with aromatic systems (taking the 1-methylnaphthalen-2-olate ion as an example in this study). Linear correlations are found between the calculated ΔH values and established physical organic parameters such as the percentage of buried volume %VBur (steric effect), Hammett's σ or Brown's σ+ (electronic effect), and Mayr's E (reaction kinetics). Careful analysis of the ΔH values leads to the rational design of a dearomative alkynylation reaction using alkynyl hypervalent iodonium reagents as the electrophiles.

PMechDB: A Public Database of Elementary Polar Reaction Steps

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.

Persistent guaiazulene arylmethylium ions as electrophilic traps for metal enolates

Guaiazulene-stabilized cations reacted with metal enolates affording carbonyl compounds with an azulene moiety. Metal enolates generated by asymmetric conjugate addition of organometallic reagents led to enantioenriched products. Additionally, guaiazulene-substituted cations efficiently react with silyl enol ethers. DFT calculations allowed estimation of the electrophilicities of the carbocations. Reaction progress was monitored by a decrease in the reactant's Vis-light absorption and an increase in the product's anti-Kasha emission.

Methide Affinity Scale: Key Thermodynamic Data Underpinning Catalysis, Organic Synthesis, and Organometallic and Polymer Chemistry

Methide transfer reactions play important roles in many areas of chemistry, including the Grignard reaction, in the transmetalation steps of metal-catalyzed cross-coupling reactions, and in the generation of cationic metal polymerization catalysts. Methide affinities (MAs) are the key thermodynamic quantity that underpin such reactions, and yet comprehensive methide affinity scales are poorly developed. Here, B3LYP-D3BJ/def2-TZVP calculations are used to calculate the energy changes (MAs) for cations (MeZ → Z+ + Me-), neutrals (MeY- → Y + Me-), and anions (MeX2- → X- + Me-) derived from permethyl species of all group s and p elements. The MAs range from 2525.8 for the singlet cation F+ to -820.4 kJ/mol for the tetramethylborate anion, Me4B-. The cations show the clearest trends: the MAs in all cases decrease going down the group, while moving across a period, the MAs increase from group 1 to group 2 and then decrease for group 3, remaining about the same or with a modest increase moving to group 4, and then continue to increase across a period to a maximum for the halogens (group 17). The anions and dianions are sensitive to hypervalency; those elements that cannot expand the octet have very unfavorable MAs (e.g., MA of Me4C requires the formation of Me5C- and of Me4B- requires the formation of Me5B2-). To address whether the anion MeY- and dianion MeZ2- are stable, the vertical detachment energies of the anions and dianions were calculated. All of the anions are thermodynamically stable with respect to electron loss, except for Me4N-, while the dianions are all thermodynamically unstable with respect to electron loss. The kinetic stability of the dianions with respect to methide and electron loss was also evaluated for the lowest MAs. The only dianions that might be kinetically stable and observable in the gas phase are Me4Ca2-, Me4Sr2-, and Me4Ba2-. The dianion CF3CaF32- is predicted to be both thermodynamically and kinetically stable in the gas phase.

RMechDB: A Public Database of Elementary Radical Reaction Steps

We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.

The computational road to reactivity scales

Reactivity scales are useful research tools for chemists, both experimental and computational. However, to determine the reactivity of a single molecule, multiple measurements need to be carried out, which is a time-consuming and resource-intensive task. In this Tutorial Review, we present alternative approaches for the efficient generation of quantitative structure-reactivity relationships that are based on quantum chemistry, supervised learning, and uncertainty quantification. First published in 2002, we observe a tendency for these relationships to become not only more predictive but also more interpretable over time.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

Reliable Functionalization of 5,6-Fused Bicyclic N-Heterocycles Pyrazolopyrimidines and Imidazopyridazines via Zinc and Magnesium Organometallics

DFT-calculations allow prediction of the reactivity of uncommon N-heterocyclic scaffolds of pyrazolo[1,5-a]pyrimidines and imidazo[1,2-b]pyridazines and considerably facilitate their functionalization. The derivatization of these N-heterocycles was realized using Grignard reagents for nucleophilic additions to 5-chloropyrazolo[1,5-a]pyrimidines and TMP2 Zn ⋅ 2 MgCl2  ⋅ 2 LiCl allowed regioselective zincations. In the case of 6-chloroimidazo[1,2-b]pyridazine, bases such as TMP2 Zn ⋅ MgCl2  ⋅ 2 LiCl, in the presence or absence of BF3  ⋅ OEt2 , led to regioselective metalations at positions 3 or 8. Subsequent functionalizations were achieved with TMPMgCl ⋅ LiCl, producing various polysubstituted derivatives (up to penta-substitution). X-ray analysis confirmed the regioselectivity for key functional heterocycles.

Uncertainty Quantification of Reactivity Scales

According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters. Here, we propose an extension to Mayr's reactivity method that is rooted in uncertainty quantification and transforms the reactivity parameters into probability distributions. Through uncertainty propagation, these distributions can be transformed into uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data. We demonstrate the above at the example of the reference data set released by Mayr and co-workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As by-product of the new approach, we obtain revised reactivity parameters for 36 π-nucleophiles and 32 benzhydrylium ions.

Reactivities of allenic and olefinic Michael acceptors towards phosphines

The kinetics of the reactions of tributylphosphine with allenic and olefinic Michael acceptors in dichloromethane at 20 °C was followed by photometric and NMR spectroscopic methods. Combination with DFT-calculated methyl anion affinities revealed the relevance of retroaddition barriers in phosphine-catalysed reactions when mixtures of allenic and olefinic substrates are used.

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity

There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets.

Methyl Cation Affinities of Canonical Organic Functional Groups

Methyl cation affinities are calculated for the canonical nucleophilic functional groups in organic chemistry. These methyl cation affinities, calculated with a solvation model (MCA*), give an emprical correlation with the Ns_N term from the Mayr equation under aprotic conditions when they are scaled to the Mayr reference cation (4-MeOC₆H₄)₂CH⁺ (Mayr E = 0). Highly reactive anionic nucleophiles were found to give a separate correlation, while some ylides and phosphorus compounds were determined to give a poor correlation. MCA*s are estimated for a broad range of simple molecules representing the canonical functional groups in organic chemistry. On the basis of a linear correlation, we estimate the range of nucleophilicities of organic functional groups, ranging from a C-C bond to a hypothetical tert-butyl carbanion, toward the reference electrophile to be about 50 orders of magnitude.

Nucleophilicity Prediction via Multivariate Linear Regression Analysis

The concept of nucleophilicity is at the basis of most transformations in chemistry. Understanding and predicting the relative reactivity of different nucleophiles is therefore of paramount importance. Mayr’s nucleophilicity scale likely represents the most complete collection of reactivity data, which currently includes over 1200 nucleophiles. Several attempts have been made to theoretically predict Mayr’s nucleophilicity parameters N based on calculation of molecular properties, but a general model accounting for different classes of nucleophiles could not be obtained so far. We herein show that multivariate linear regression analysis is a suitable tool for obtaining a simple model predicting N for virtually any class of nucleophiles in different solvents for a set of 341 data points. The key descriptors of the model were found to account for the proton affinity, solvation energies, and sterics.