Electrophilic reactions of the dibenzo[a,d]tropylium ion

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

Methyl Anion Affinities of the Canonical Organic Functional Groups

Calculated methyl anion affinities are known to correlate with experimentally determined Mayr E parameters for individual organic functional group classes but not between neutral and cationic organic electrophiles. We demonstrate that methyl anion affinities calculated with a solvation model (MAA*) give a linear correlation with Mayr E parameters for a broad range of functional groups. Methyl anion affinities (MAA*), plotted on the log scale of Mayr E, provide insights into the full range of electrophilicity of organic functional groups. On the Mayr E scale, the electrophilicity toward the methyl anion spans 180 orders of magnitude.