In the Mol2Vec approach , the key concept is to view the molecule as a "sentence" comprised of "words" corresponding to substructures of predefined size (Figure ). Unlike in fingerprint approaches, these "words" are not assigned with arbitrary numbers but, instead, each is represented as a 300-dimensional vector reflecting colocations with other "words". Statistical information describing the so-called "corpus" and reflecting colocations between large numbers of chemical "words" is derived from some comprehensive collection of organic molecules, here, ~19 million molecules from the Zinc database . A molecule "sentence" is then a union of vectors describing its constituent "words". Effectively, Mol2Vec not only recognizes the counts of specific fragments in molecules, but also their mutual molecular "contexts".