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arXiv · 2310.12304

Preference Optimization for Molecular Language Models

Ryan Park·Ryan Theisen·Navriti Sahni·Marcel Patek·Anna Cichońska·Rayees Rahman
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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.

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Ryan Park, Ryan Theisen, Navriti Sahni, Marcel Patek, Anna Cichońska, Rayees Rahman. 2023-10-18. Preference Optimization for Molecular Language Models. https://arxiv.org/abs/2310.12304

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