Preference Optimization for Molecular Language Models

Ryan Park, Ryan Theisen, Rayees Rahman, Anna Cichońska, Marcel Patek, Navriti Sahni

Keywords: Molecules, Chemistry, Language Models

Abstract: 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.

Submission Number: 8