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

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.