Every atom counts: predicting sites of reaction based on chemistry within two bonds

Abstract

How much chemistry can be described by looking only at each atom, its neighbours and its next-nearest neighbours? We present a method for predicting reaction sites based only on a simple, two-bond model. Machine learning classification models were trained and evaluated using atom-level labels and descriptors, including bond strength and connectivity. Despite limitations in covering only local chemical environments, the models achieved over 80% accuracy even with challenging datasets that cover a diverse chemical space. Whilst this simplistic model is necessarily incomplete, it describes a large amount of interesting chemistry.