  Machine learning techniques have been previously used to model and predict
column densities in the TMC-1 dark molecular cloud. In interstellar sources
further along the path of star formation, such as those where a protostar
itself has been formed, the chemistry is known to be drastically different from
that of largely quiescent dark clouds. To that end, we have tested the ability
of various machine learning models to fit the column densities of the molecules
detected in source B of the Class 0 protostellar system IRAS 16293-2422. By
including a simple encoding of isotopic composition in our molecular feature
vectors, we also examine for the first time how well these models can replicate
the isotopic ratios. Finally, we report the predicted column densities of the
chemically relevant molecules that may be excellent targets for
radioastronomical detection in IRAS 16293-2422B.
