From what I've seen, most use molecular fingerprints or cheminformatic descriptors such as RDKit provides. Google and Vijay Pande's group at Stanford had a recent publication on Molecular Graph Convolutions. I believe a lot of interesting research will come out in the area of molecular feature's over the next few years.

http://research.google.com/pubs/pub45548.html

People are working on such things! https://arxiv.org/abs/1610.02415

Wow! Thanks for the link.

Can we map the latent chemical space directly into a word space (can we ensure grammatical correctness? semantic correctness?)

> "We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation... We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space..."

Consider the set of all chemical structures composed of between 0-6 carbon atoms, 0-4 oxygen atoms, 0-12 hydrogen atoms, 0-4 nitrogen atoms, and 0-1 "functional groups" expressed in the SMILES chemical nomenclature; for instance Alanine could be represented as O=C(O)C(N)C; Serine as C([C@@H](C(=O)O)N)O

Can we create a correspondence function that takes a given SMILES chemical structure, such as O=C(O)C(N)C, and returns a sequence of english words that encodes the same structure, such that, read left to right:

  - each atom is represented by a noun or adjective that starts with the same letter(s) as the atomic symbol; (pronouns don't count)
  - double or triple bonds are represented with prepositions 
  - charge is represented numerically
  - branches are represented by subordinating conjunctions
  - verbs and articles and other parts of speech (non-subordinating conjunctions, pronouns) can be used freely for grammatical correctness

it's hard to select word strings that are semantically meaningful sentences, but haiku-like forms are easier.

So Alanine ( O=C(O)C(N)C ) might be:

  (1)  O-Adjective/Noun preposition proposition C-Adjective/Noun subordinating-conjunction O-Adjective/Noun; 
  (2)  C-Adjective/Noun, subordinating-conjunction N-Adjective/Noun; 
  (3)  C-Adjective/Noun.

  (1)  Oranges blossom under and above Cherries where the Orangutans roam; 
  (2)  Conscious, unless Nocturnal; 
  (3)  Change coming."

Clearly many interesting structures could be expressed with semantically-invalid sentences.

But conversely, how often are "interesting" sentences chemically pointless or invalid?

Could we bias sentence construction to be more interesting by constraining it with the semantic vector space of a big work of literature, such as phrases found in Infinite Jest or the collected works of Shakespeare?