(508b) Quantitative in silico Prediction of the Rate of Protodeboronation By a Mechanistic Density Functional Theory-Aided Algorithm

Boronic acids (BAs) have long been of interest to the chemical sciences community due to their irreplaceable role in communications, materials, and medicine. BAs also feature as the nucleophilic coupling partner in several key organic reactions including the Suzuki−Miyaura reaction, Chan−Evans−Lam coupling, Liebeskind−Srogl coupling, and oxidative Heck and can also undergo addition with carbonyls, imines, and enones. A reaction will not work if one of the coupling partners degrades at a significantly faster rate than the rate of the intended reaction, and protodeboronation is one of the most significant, if not the most significant, degradation pathways/side reactions in the aforementioned named organic reactions. Being able to accurately predict the rate of protodeboronation would therefore be hugely beneficial to anyone working with BAs, as it can help with reaction planning.

We have developed an algorithm for in silico prediction of the rate of protodeboronation of boronic acids as a function of pH [1]. Protodeboronation can proceed through 7 distinct pathways, though for any particular boronic acid only a subset of the mechanistic pathways are active [2]. The rate of each active mechanistic pathway is linearly correlated with its characteristic energy difference, which can be determined through energy calculations using Density Functional Theory (DFT). To the best of our knowledge, this is the first purpose-built algorithm to be published in the literature capable of making in silico predictions of the rate of protodeboronation. We validated the algorithm using leave-one-out cross-validation on a dataset of 50 boronic acids (469 data points from pH 0 to 14), which showed that the algorithm can explain a majority of the variability in the dataset (R2=0.65), with a Mean Absolute Error (MAE) of 0.86. The rate is measured on a log scale from -9 (22 years half-life) to +2 (7 milliseconds half-life), so MAE below 1 means that the algorithm is capable of predicting the rate of protodeboronation within an order of magnitude, i.e., whether the rate will be on the order of milliseconds, seconds, hours, days, weeks, or years. With such predictions, chemists performing reactions that feature boronic acids, such as Suzuki–Miyaura and Chan-Evans-Lam couplings, are able to better plan their reactions and predict when protodeboronation might become a problem, thus saving valuable time and materials.

Considering the abstract figure, the steps in the algorithmic protodeboronation prediction workflow can be summarised as follows:

1. DFT optimisation

The mechanistic pathway for determination of the characteristic energy difference is of the form transition state -> stable intermediate, and the energies of each of the molecular structures were determined using DFT with Gaussian (computational approach was M06L/6-311++G** in water) [3]. The DFT energy calculations for the transition states were transition state optimisations, while calculations for reaction intermediates were optimised to a local energy minimum.

2. Correlate energies

We found that there is a linear relationship between the maximum/saturated rate of a mechanism and the associated characteristic energy difference. Linear regression for the k₂ mechanism can be seen in the abstract figure. Using these linear relationships, the rate can be predicted for novel boronic acids.

3. Algorithmic amalgamation

The observed rate of reaction is the sum of the rate contributions from each active mechanism. The experimental rate measurements span from +2 (half-life of roughly 7 milliseconds) to -9 (half-life of roughly 22 years), and the predicted rate has been capped to fall within this region as well. Leave-one-out cross-validation (LOOCV) was used to assess the quality of the fit for each molecule, and the construction of a parity plot showed that the model was able to explain a majority of the variability (R2=0.65).

4. De novo prediction

Finally, the algorithm was used to predict in silico the rate of protodeboronation as a function of pH for 50 novel boronic acids of academic and industrial relevance. These predictions were made available in the supplementary information [1], which allows chemists running reactions featuring boronic acids to simply look up the predicted rate of protodeboronation for these molecules. The associated code can be found on GitHub, such that anyone can generate their own predictions. We also hope that this work may serve as inspiration as a method for predicting the rate of reaction when the mechanistic details about a reaction are known and can be simplified as a set of interacting linear equations.

[1] Wigh, D. S.; Tissot, M.; Pasau, P.; Goodman, J. M.; Lapkin, A. A. Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm. J. Phys. Chem. A 2023. https://doi.org/10.1021/acs.jpca.2c08250.

[2] Cox, P. Protodeboronation. Ph.D. thesis, The University of Edinburgh, 2016.

[3] Gaussian 16, Revision C.01, Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian, Inc., Wallingford CT, 2016.