@inproceedings{
park2023preference,
title={Preference Optimization for Molecular Language Models},
author={Ryan Park and Ryan Theisen and Rayees Rahman and Anna Cicho{\'n}ska and Marcel Patek and Navriti Sahni},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=EKt4NQZ47U}
}
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.
Preference Optimization for Molecular Language Models
Ryan Park Ryan Theisen Rayees Rahman Anna Cichońska Marcel Patek Navriti Sahni