Regardless of the multiple examples shown in the previous section alluding to a highly organized embedding space in terms of chemical analogies, this structure may still be perceived as a coincidence similar to the analogies found in natural language. However, in contrast to NLP, we can explain the overall structure of the embedding space from chemical principles. We will show how it is possible to replicate the embedding space by introducing consecutively layered neighborhood composition to model the atom-embedding, which we call perturbational replicates. Moreover, we are able to explain the nearly constant nature of reaction vectors found in the previous Section. We shall also see how this leads naturally to the implicit syntactical relationships found in the previous Section, and how the replicates reveal a valid interpretation of decision-making of such models, shedding light on its "black box" nature.