Paper ID: 2310.12304

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.

Submitted: Oct 18, 2023