Nucleophilicity Prediction via Multivariate Linear Regression Analysis

Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles

Carbon dioxide (CO2) is an economically viable and abundant carbon source that can be incorporated into compounds such as C2-carboxylated 1,3-azoles relevant to the pharmaceutical, cosmetics, and pesticide industries. Of the 2.4 million commercially available C2-unsubstituted 1,3-azole compounds, less than 1 % are currently purchasable as their C2-carboxylated derivatives, highlighting the substantial gap in compound availability. This availability gap leaves ample opportunities for exploring the synthetic accessibility and use of carboxylated azoles in bioactive compounds. In this study, we analyze and quantify the relevance of C2-carboxylated 1,3-azoles in small-molecule research. An analysis of molecular databases such as ZINC, ChEMBL, COSMOS, and DrugBank identified relevant C2-carboxylated 1,3-azoles as anticoagulant and aroma-giving compounds. Moreover, a pharmacophore analysis highlights promising pharmaceutical potential associated with C2-carboxylated 1,3-azoles, revealing the ATP-sensitive inward rectifier potassium channel 1 (KATP) and Kinesin-like protein KIF18 A as targets that can potentially be addressed with C2-carboxylated 1,3-azoles. Moreover, we identified several bioisosteres of C2-carboxylated 1,3-azoles. In conclusion, further exploration of the chemical space of C2-carboxylated 1,3-azoles is recommended to harness their full potential in drug discovery and related fields.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Reactivity and Steric Parameters from 2D to 3D Bulky Pyridines: Increasing Steric Demand at Nitrogen with Chiral Azatriptycenes

Sterically hindered pyridines embedded in a three-dimensional triptycene framework have been synthesized, and their resolution by chiral HPLC enabled access to unprecedented enantiopure pyridines exceeding the known steric limits. The design principles for new axially chiral pyridine derivatives are then described. To rationalize their associations with Lewis acids and transition metals, a comprehensive determination of the steric and electronic parameters for this new class of pyridines was performed. This led to the general parameterization of the steric parameters (percent buried volume %VBur, Tolman cone angle θ, and He8_steric descriptor) for a large set of two- and three-dimensional pyridine derivatives. These parameters are shown to describe quantitatively their interactions with carbon- and boron-centered Lewis acids and were used to predict the ΔG° of association with the prototypical B(C6F5)3 Lewis acid widely used in frustrated Lewis pair catalysis. This first parameterization of pyridine sterics is a fundamental basis for the future development of predictive reactivity models and for guiding new applications of bulky and chiral pyridines in organocatalysis, frustrated Lewis pairs, and transition-metal catalysis.

Reductive Transamination of Pyridinium Salts to N-Aryl Piperidines

Saturated N-heterocycles are found in numerous bioactive natural products and are prevalent in pharmaceuticals and agrochemicals. While there are many methods for their synthesis, each has its limitations, such as scope and functional group tolerance. Herein, we describe a rhodium-catalyzed transfer hydrogenation of pyridinium salts to access N-(hetero)aryl piperidines. The reaction proceeds via a reductive transamination process, involving the initial formation of a dihydropyridine intermediate via reduction of the pyridinium ion with HCOOH, which is intercepted by water and then hydrolyzed. Subsequent reductive amination with an exogenous (hetero)aryl amine affords an N-(hetero)aryl piperidine. This reductive transamination method thus allows for access of N-(hetero)aryl piperidines from readily available pyridine derivatives, expanding the toolbox of dearomatization and skeletal editing.

%VBur index and steric maps: from predictive catalysis to machine learning

Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.

Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field, as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevent access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling de facto real-time reactivity predictions. We use the well-understood chemical space of benzhydrylium ions as an example to demonstrate the functionality of our approach and the performance of the resulting quantitative structure-reactivity relationships (QSRRs). Our results suggest that it is straightforward to build low-cost QSRR models that are accurate, interpretable, and transferable to unexplored systems within a given scope of application. Moreover, our QSRR approach suggests that Hammett σ parameters are only approximately additive.

Visible-Light-Mediated Divergent and Regioselective Vicinal Difunctionalization of Styrenes with Arylazo Sulfones

Activated by visible light, arylazo sulfones can serve as multifaceted reactants and are employed in diazenylation, sulfonylation, and arylation reactions under (photo)catalyst-free conditions. Such versatile reactivity enabled us to develop an operationally simple, regioselective, and tunable difunctionalization of styrenes with arylazo sulfones to produce α-sulfonyl arylhydrazones and 1,2-alkoxyarylated products in moderate to excellent yields. Furthermore, such difunctionalized products have been exploited as key building blocks for the synthesis of various heterocycles.

Reactivity Prediction of Cu-Catalyzed Halogen Atom Transfer Reactions Using Data-Driven Techniques

In catalysis, linear free energy relationships (LFERs) are commonly used to identify reaction descriptors that enable the prediction of outcomes and the design of more effective catalysts. Herein, LFERs are established for the reductive cleavage of the C(sp3)-X bond in alkyl halides (RX) by Cu complexes. This reaction represents the activation step in atom transfer radical polymerization and atom transfer radical addition/cyclization. The values of the activation rate constant, kact, for 107 Cu complex/RX couples in 5 different solvents spanning over 13 orders of magnitude were effectively interpolated by the equation: log kact = sC(I + C + S), where I, C, and S are, respectively, the initiator, catalyst, and solvent parameters, and sC is the catalyst-specific sensitivity parameter. Furthermore, each of these parameters was correlated to relevant descriptors, which included the bond dissociation free energy of RX and its Tolman cone angle θ, the electron affinity of X, the radical stabilization energy, the standard reduction potential of the Cu complex, the polarizability parameter π* of the solvent, and the distortion energy of the complex in its transition state. This set of descriptors establishes the fundamental properties of Cu complexes and RX that determine their reactivity and that need to be considered when designing novel systems for atom transfer radical reactions. Finally, a multivariate linear regression (MLR) approach was adopted to develop an objective model that surpassed the predictive capability of the LFER equation. Thus, the MLR model was employed to predict kact values for >2000 Cu complex/RX pairs.

Exceptional reactivity of the bridgehead amine on bicyclo[1.1.1]pentane

Bicyclo[1.1.1]pentane (BCP) has received substantial interest in the field of synthetic chemistry recently for its potential use as a benzene isostere in medicinal chemistry. Whereas bicyclo[2.2.2]octane (BCO) has also been used as a bioisostere of benzene, the condensation of BCP-amine with nadic anhydride is significantly easier than that of BCO-amine. Analyses of the geometries and the frontier molecular orbitals of these amines suggest that the low steric hindrance and high intrinsic nucleophilicity of BCP-amine together contribute to its exceptional reactivity.

One-Bond-Nucleophilicity and -Electrophilicity Parameters: An Efficient Ordering System for 1,3-Dipolar Cycloadditions

Diazoalkanes are ambiphilic 1,3-dipoles that undergo fast Huisgen cycloadditions with both electron-rich and electron-poor dipolarophiles but react slowly with alkenes of low polarity. Frontier molecular orbital (FMO) theory considering the 3-center-4-electron π-system of the propargyl fragment of diazoalkanes is commonly applied to rationalize these reactivity trends. However, we recently found that a change in the mechanism from cycloadditions to azo couplings takes place due to the existence of a previously overlooked lower-lying unoccupied molecular orbital. We now propose an alternative approach to analyze 1,3-dipolar cycloaddition reactions, which relies on the linear free energy relationship lg k₂(20 °C) = s_N(N + E) (eq 1) with two solvent-dependent parameters (N, s_N) to characterize nucleophiles and one parameter (E) for electrophiles. Rate constants for the cycloadditions of diazoalkanes with dipolarophiles were measured and compared with those calculated for the formation of zwitterions by eq 1. The difference between experimental and predicted Gibbs energies of activation is interpreted as the energy of concert, i.e., the stabilization of the transition states by the concerted formation of two new bonds. By linking the plot of lg k₂ vs N for nucleophilic dipolarophiles with that of lg k₂ vs E for electrophilic dipolarophiles, one obtains V-shaped plots which provide absolute rate constants for the stepwise reactions on the borderlines. These plots furthermore predict relative reactivities of dipolarophiles in concerted, highly asynchronous cycloadditions more precisely than the classical correlations of rate constants with FMO energies or ionization potentials. DFT calculations using the SMD solvent model confirm these interpretations.

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.

Machine-Learning-Guided Discovery of Electrochemical Reactions

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (S_NAr) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative S_NAr rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental S_NAr free energies of activation and three molecular descriptors that can be obtained computationally: the electron affinity (EA) of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the ortho and para atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured S_NAr reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning.

Analysis of the Stage Performance Effect of Environmental Protection Music and Dance Drama Based on Artificial Intelligence Technology

There are a lot of environmental protection musicals and dances in public entertainment life to support the concept of environmental protection and to entice people to support the cause of environmental protection. As we all know, in a musical performance, the distance between the audience and the actors, the design of the stage environment, the training of the actors, the costumes of the actors, the makeup of the actors, and the musical accompaniment will all have more or less influence on the stage performance effects. In order to make environmental protection music and dance dramas profoundly meaningful to social production and human activities, it is especially important to analyze the stage performance of such music and dance dramas. The analysis of stage performance effects is not only beneficial to making the ideas spread by the music and dance drama penetrate into people's hearts but can also provide guiding suggestions for the production of music and dance drama. Therefore, this paper proposes a linear regression algorithm based on artificial intelligence to deeply explore and analyze the influence of the above six factors on the stage performance effects of environmental protection music and dance drama. In our method, first we preprocess the data collected by classification to remove the odd values from the data so that the various types of data conform to a normal distribution. Secondly, we obtained linear fit plots of the six factors with the stage performance effect scores by using a linear regression algorithm to deeply analyze the correlation between various types of data and the stage performance effect scores. Finally, through numerical calculations, we found that the distance between the audience and the actors, the training of the actors, and the musical accompaniment have a greater influence on the performance effect of the musical cabaret in the environmental protection category. Meanwhile, the costumes of the actors, the makeup of the actors, and the design of the stage environment have less influence on the performance effect of the musical cabaret. Therefore, in the production and performance of environmental protection music and dance dramas, producers and performers should pay more attention to the distance between the audience and the actors, the design of musical accompaniment, and the training of actors. To sum up, this paper has made a scientific, detailed, and reasonable analysis of the performance effect of environmental protection music and dance dramas on stage, contributing to the dissemination of environmental protection ideas.

Nucleophilicity Prediction Using Graph Neural Networks

The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important representatives. However, the determination of Mayr's nucleophilicity parameter N often requires time-consuming experiments with reference electrophiles in the solvent. Several machine learning (ML)-based models have been proposed to realize the data-driven prediction of N in recent years. However, in addition to DFT-calculated electronic descriptors, most of them also use a set of artificially predefined structural descriptors as input, which may result in a biased representation of the nucleophile's structural information depending on descriptors' definition preference. Compared with traditional ML algorithms, graph neural networks (GNNs) can naturally take the molecule's structural information into account by applying the message passing technique. We herein proposed a SchNet-based GNN model that only takes the molecular conformation and solvent type as input. The model achieves a comparable performance to the previous benchmark study on 10-fold cross-validation of 894 data points (R² = 0.91, RMSE = 2.25). To enhance the model's ability to capture the molecule's electronic information, some DFT-calculated parameters are then incorporated into the model via graph global features, and substantial improvement is achieved in the prediction precision (R² = 0.95, RMSE = 1.63). These results demonstrate that both structural and electronic information are important for the prediction of N, and GNN can integrate these two kinds of information more effectively.

Asymmetric cyclopropanation of electron-rich alkenes by the racemic diene rhodium catalyst: the chiral poisoning approach

Asymmetric cyclopropanation of alkenes by aryldiazoacetates was achieved using the readily-available racemic (diene)rhodium complex in combination with the chiral oxazoline-phenol ligand, which acts as the chiral poison and selectively inhibits one of the enantiomers of the catalyst. This approach eliminates a common problematic step of the synthesis of chiral catalysts.

Gut Microbiota of Individuals Could Be Balanced by a 14-Day Supplementation With Laminaria japonica and Differed in Metabolizing Alginate and Galactofucan

Laminaria japonica is rich in alginate (Alg) and galactofucan (GF) which have both been reported to regulate gut microbiota composition. To reveal the effect of L. japonica on human gut microbiota, the fecal microbiota of 12 volunteers before and after 14-day L. japonica intake was sequenced and compared, and the capabilities of the gut microbiota to utilize Alg and GF were also investigated. The 16S rRNA gene sequencing results demonstrated that Firmicutes/Bacteroidetes ratio could be balanced by L. japonica supplementation. The ability of gut microbiota to utilize Alg was significantly enhanced by L. japonica supplementation. Furthermore, the multiple linear regression analysis suggested that bacteria from Bacteroidaceae and Ruminococcaceae were positively correlated with Alg utilization while those from Erysipelotrichaceae, Bacteroidaceae, and Prevotellaceae participated in GF degradation. Moreover, the production of acetic acid and the total short-chain fatty acids (SCFAs) in fermentation were consistent with the consumption of Alg or GF, and propionic acid content was positively correlated with Alg consumption. In addition, the percentage of monosaccharides in the consumed GF after the fermentation suggested that gut microbiota from individuals could consume GF with different monosaccharide preferences. These findings shed a light on the impacts of dietary L. japonica on human health.

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.

Predicting relative efficiency of amide bond formation using multivariate linear regression

Amides are ubiquitous in biologically active natural products and commercial drugs. The most common strategy for introducing this functional group is the coupling of a carboxylic acid with an amine, which requires the use of a coupling reagent to facilitate elimination of water. However, the optimal reaction conditions often appear rather arbitrary to the specific reaction. Herein, we report the development of statistical models correlating measured rates to physical organic descriptors to enable the prediction of reaction rates for untested carboxylic acid/amine pairs. The key to the success of this endeavor was the development of an end-to-end data science–based workflow to select a set of coupling partners that are appropriately distributed in chemical space to facilitate statistical model development. By using a parameterization, dimensionality reduction, and clustering protocol, a training set was identified. Reaction rates for a range of carboxylic acid and primary alkyl amine couplings utilizing carbonyldiimidazole (CDI) as the coupling reagent were measured. The collected rates span five orders of magnitude, confirming that the designed training set encompasses a wide range of chemical space necessary for effective model development. Regressing these rates with high-level density functional theory (DFT) descriptors allowed for identification of a statistical model wherein the molecular features of the carboxylic acid are primarily responsible for the observed rates. Finally, out-of-sample amide couplings are used to determine the limitations and effectiveness of the model.

A machine learning approach for predicting the nucleophilicity of organic molecules

Nucleophilicity provides important information about the chemical reactivity of organic molecules. Experimental determination of the nucleophilicity parameter is a tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that uses key structural descriptors to predict the nucleophilicities of organic molecules, which agree well with the experimental values. A data driven approach was used where quantum mechanical molecular and thermodynamic descriptors from a wide range of structurally diverse nucleophiles and relevant solvents were extracted and modelled using advanced algorithms against the experimentally available nucleophilicity values. Despite the structural diversity of nucleophiles, we are able to achieve statistically robust models with a high predictive power using tree-based and neural network algorithms trained on an in-house developed unique dataset consisting of 752 nucleophilicity values and 27 molecular descriptors.

Basicity as a Thermodynamic Descriptor of Carbanions Reactivity with Carbon Dioxide: Application to the Carboxylation of α,β-Unsaturated Ketones

The utilization of carbon dioxide as a raw material represents nowadays an appealing strategy in the renewable energy, organic synthesis, and green chemistry fields. Besides reduction strategies, carbon dioxide can be exploited as a single-carbon-atom building block through its fixation into organic scaffolds with the formation of new C-C bonds (carboxylation processes). In this case, activation of the organic substrate is commonly required, upon formation of a carbanion C^-, being sufficiently reactive toward the addition of CO₂. However, the prediction of the reactivity of C^- with CO₂ is often problematic with the process being possibly associated with unfavorable thermodynamics. In this contribution, we present a thermodynamic analysis combined with density functional theory calculations on 50 organic molecules enabling the achievement of a linear correlation of the standard free energy (ΔG⁰) of the carboxylation reaction with the basicity of the carbanion C^-, expressed as the pK_a of the CH/C^- couple. The analysis identifies a threshold pK_a of ca 36 (in CH₃CN) for the CH/C^- couple, above which the ΔG⁰ of the carboxylation reaction is negative and indicative of a favorable process. We then apply the model to a real case involving electrochemical carboxylation of flavone and chalcone as model compounds of α,β-unsaturated ketones. Carboxylation occurs in the β-position from the doubly reduced dianion intermediates of flavone and chalcone (calculated ΔG⁰ of carboxylation in β = -12.8 and -20.0 Kcalmol^-1 for flavone and chalcone, respectively, associated with pK_a values for the conjugate acids of 50.6 and 51.8, respectively). Conversely, the one-electron reduced radical anions are not reactive toward carboxylation (ΔG⁰ > +20 Kcalmol^-1 for both substrates, in either α or β position, consistent with pK_a of the conjugate acids < 18.5). For all the possible intermediates, the plot of calculated ΔG⁰ of carboxylation vs. pK_a is consistent with the linear correlation model developed. The application of the ΔG⁰ vs. pK_a correlation is finally discussed for alternative reaction mechanisms and for carboxylation of other C=C and C=O double bonds. These results offer a new mechanistic tool for the interpretation of the reactivity of CO₂ with organic intermediates.

Box-copper catalyzed cascade asymmetric amidation for chiral exo-methylene aminoindoline derivatives

Enantioselective copper-catalyzed cascade inter- and intramolecular amidation was achieved between ethynyl benzoxazinanones and α-halohydroxamates in the presence of an indapybox ligand. The one-pot cascade transformation was triggered by the attack of hydroxamates to dipolar copper-allenylidene intermediates, followed by a nucleophilic annulation reaction. Thus, a series of exo-methylene 3-aminoindoline derivatives were obtained in good yields with high enantioselectivities under mild reaction conditions.

Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship

Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter N in the four most-common solvents for nucleophiles in the Mayr's reactivity parameter database (R² = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values.