# Preference Optimization for Molecular Language Models

**Authors:** Ryan Park, Ryan Theisen, Navriti Sahni, Marcel Patek, Anna Cichońska, Rayees Rahman  
**arXiv:** [2310.12304](https://arxiv.org/abs/2310.12304)  
**Date:** 2023-10-18  
**Summary page:** https://www.summarizepaper.com/fr/paper/2310.12304v1/

## 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.