Methyl Anion Affinities of the Canonical Organic Functional Groups

Mood, Aaron; Tavakoli, Mohammadamin; Gutman, Eugene S.; Kadish, Dora; Baldi, Pierre; Vranken, David L. Van

doi:10.1021/acs.joc.9b03187

Cited by 25 publications

(56 citation statements)

References 68 publications

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“…[17,18] Up to now, the most predictive models have been those based on the calculation of methyl anion affinities (MAAs). [19,20] This promising approach however differs from that retained in this paper. In fact, MAA corresponds to the reaction energy of the various electrophiles with a unique selected nucleophile (CH 3…”

Section: Introductionmentioning

confidence: 83%

Predicting experimental electrophilicities from quantum and topological descriptors: A machine learning approach

et al. 2020

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In this paper, we assess the ability of various machine learning methods, either linear or non‐linear, to efficiently predict Mayr's experimental scale for electrophilicity. To this aim, molecular and atomic descriptors rooted in conceptual density functional theory and in the quantum theory of atoms‐in‐molecules as well as topological features defined within graph theory were evaluated for a large set of molecules widely used in organic chemistry. State‐of‐the‐art regression tools belonging to the support vector machines family and decision tree models were in particular considered and implemented. They afforded a promising predictive model, validating the use of such methodologies for the study of chemical reactivity.

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Section: Introductionmentioning

confidence: 83%

Predicting experimental electrophilicities from quantum and topological descriptors: A machine learning approach

et al. 2020

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“…Recently, we used QM with Density Functional Theory (DFT) simulations to investigate this problem for electron donor and electron acceptor functional groups [16,27]. Specifically, we applied DFT to over 100 diverse molecular structures and showed that in general methyl ion affinities, using a solvation model, are highly correlated to the Mayr reactivity scale.…”

Section: A Million Molecular Structuresmentioning

confidence: 99%

“…The resulting chemical reactivity scores (MCA* and MAA*), formulated as the difference between the energy of reactants and products of the reactions shown in Figure 2a, can be interpreted in slightly different ways, depending on the entity to which the score is attributed to. Van Vranken et al interpreted these quantities as the electrophilicity and nucleophilicity of the reacting functional groups [16,27]. However, in cases where only one functional group is going to react with methyl ions, the corresponding values can be attributed to the reactivity of the entire molecule.…”

Section: Reactivity Scalesmentioning

confidence: 99%

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

Tavakoli¹,

Mood²,

Vranken³

et al. 2021

Preprint

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There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and does not scale to the astronomical size of chemical 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 dataset of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated dataset in combination with different representations of molecular structures. Using ten-fold cross-validation, we show that graph attention neural networks applied to informative input fingerprints 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; (2) combinatorial generation of reaction mechanisms. The curated dataset of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at www.cdb.ics.uci.edu.

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“…We propose a third and (for now) last way to exploit empirical parameter distributions. A series of theoretical models predicting Mayr-type reactivity parameters were proposed in the past [30,31,32,33,34,35,36,37,38]. The predictive power of these models was assessed with respect to some summary statistic (e.g., mean absolute error or root-meansquare error).…”

Section: Model Dispersion Vs Measurement Uncertaintymentioning

confidence: 99%

Transforming Predictions into Testable Hypotheses: The Case of Polar Organic Reactivity

Proppe¹,

Kircher²

2021

Preprint

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Herbert Mayr’s research on reactivity scales tells a success story of how polar organic synthesis can be rationalized by a simple empirical relationship. In this work, we propose an extension to Mayr’s reactivity approach that is rooted in uncertainty quantification (UQ). It transforms the unique values of reactivity parameters (sN, N, E) into value distributions. Through uncertainty propagation, these distributions can be exploited to quantify the uncertainty of bimolecular rate constants. Our UQ-based extension serves three purposes. First, predictions of polar organic reactivity can be transformed into testable hypotheses, which increases the overall reliability of the method and guides the exploration of new research directions. Second, it is also possible to quantify the discriminability of two competing reactions, which is particularly important if subtle reactivity differences matter. Third, since rate constant uncertainty can also be quantified for reactions that have yet to be observed, new opportunities arise for benchmarking computational chemistry methods (benchmarking under uncertainty). We demonstrate the functionality and performance of the UQ-extended reactivity approach at the example of the 2001/12 reference data set released by Mayr and co-workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As a by-product of the new approach, we obtain revised reactivity parameters for the electrophiles and the nucleophiles of the reference set.

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Methyl Anion Affinities of the Canonical Organic Functional Groups

Cited by 25 publications

References 68 publications

Predicting experimental electrophilicities from quantum and topological descriptors: A machine learning approach

Predicting experimental electrophilicities from quantum and topological descriptors: A machine learning approach

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

Transforming Predictions into Testable Hypotheses: The Case of Polar Organic Reactivity

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