Global geometry of chemical graph neural network representations in terms of chemical moieties

Abstract

The embedding vectors from a Graph Neural Network trained on quantum chemical data allow for a global geometric space with a Euclidean distance metric. Moieties that are close in chemical sense, are also close in Euclidean sense.