Poster
in
Workshop: Generative AI and Biology (GenBio@NeurIPS2023)
Preference Optimization for Molecular Language Models
Ryan Park · Ryan Theisen · Rayees Rahman · Anna Cichonska · Marcel Patek · Navriti Sahni
Keywords: [ chemistry ] [ molecules ] [ language models ]
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.