MLOct 18, 2023
Preference Optimization for Molecular Language ModelsRyan Park, Ryan Theisen, Navriti Sahni et al.
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.