Preference Optimization for Molecular 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 w...

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Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Park, Ryan, Theisen, Ryan, Sahni, Navriti, Patek, Marcel, Cichońska, Anna, Rahman, Rayees
Format: Article
Language:English
Subjects:
Chemists
Optimization
Online Access:Get full text
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Summary: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.
ISSN:2331-8422