  Chemical structure elucidation is a serious bottleneck in analytical
chemistry today. We address the problem of identifying an unknown chemical
threat given its mass spectrum and its chemical formula, a task which might
take well trained chemists several days to complete. Given a chemical formula,
there could be over a million possible candidate structures. We take a data
driven approach to rank these structures by using neural networks to predict
the presence of substructures given the mass spectrum, and matching these
substructures to the candidate structures. Empirically, we evaluate our
approach on a data set of chemical agents built for unknown chemical threat
identification. We show that our substructure classifiers can attain over 90%
micro F1-score, and we can find the correct structure among the top 20
candidates in 88% and 71% of test cases for two compound classes.
