  The characterization of interstellar chemical inventories provides valuable
insight into the chemical and physical processes in astrophysical sources. The
discovery of new interstellar molecules becomes increasingly difficult as the
number of viable species grows combinatorially, even when considering only the
most thermodynamically stable. In this work, we present a novel approach for
understanding and modeling interstellar chemical inventories by combining
methodologies from cheminformatics and machine learning. Using multidimensional
vector representations of molecules obtained through unsupervised machine
learning, we show that identification of candidates for astrochemical study can
be achieved through quantitative measures of chemical similarity in this vector
space, highlighting molecules that are most similar to those already known in
the interstellar medium. Furthermore, we show that simple, supervised learning
regressors are capable of reproducing the abundances of entire chemical
inventories, and predict the abundance of not yet seen molecules. As a
proof-of-concept, we have developed and applied this discovery pipeline to the
chemical inventory of a well-known dark molecular cloud, the Taurus Molecular
Cloud 1 (TMC-1); one of the most chemically rich regions of space known to
date. In this paper, we discuss the implications and new insights machine
learning explorations of chemical space can provide in astrochemistry.
