[astro-ph.GA] Modern radio telescopes generate vast amounts of observational data, offering valuable insights into the molecular composition of interstellar sources. Identifying the molecules within these datasets typically involves time-consuming and labor-intensive manual analysis.

Automated Assignment and Prediction of Molecules in Astronomical Line Surveys Using Machine-Learning-Based Chemical Embeddings