ter needs to be compared with mean distance between neighboring embeddings (1.79) and the average distance between embeddings of the same class (alkenes: 9.46). Additionally, the Table results for both the linear model mentioned before, and an added non-linearity (non-linear 1) and one with additional extra linearity (non-linear 2). For the non-linear regression, we used a neural network that consists of a single linear layer which inputs neighborhood embedding feature space (D embedding × D elements ), and outputs 128 features, the same size as the embedding space, which was then followed by a tanh non-linearity, that is