  Molecular language modeling is an effective approach to generating novel
chemical structures. However, these models do not \emph{a priori} encode
certain preferences a chemist may desire. We investigate the use of fine-tuning
using Direct Preference Optimization to better align generated molecules with
chemist preferences. Our findings suggest that this approach is simple,
efficient, and highly effective.
