Preference Optimization for Molecular Language Models
Ryan Park Ryan Theisen Navriti Sahni Marcel Patek Anna Cichońska Rayees Rahman
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.