  Identification of experimentally acquired mass spectra of unknown compounds
presents a~particular challenge because reliable spectral databases do not
cover the potential chemical space with sufficient density. Therefore machine
learning based \emph{de-novo} methods, which derive molecular structure
directly from its mass spectrum gained attention recently. We present a~novel
method in this family, addressing a~specific usecase of GC-EI-MS spectra, which
is particularly hard due to lack of additional information from the first stage
of MS/MS experiments, on which the previously published methods rely. We
analyze strengths and drawbacks or our approach and discuss future directions.
