Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a message-passing encoder-decoder model, are trained to reliably produce new sequences from a reference structure. However, when applied to peptides, these models are prone to generating repetitive sequences that do not fold into the reference structure. To address this, we fine-tune ProteinMPNN to produce diverse and structurally consistent peptide sequences via Direct Preference Optimization (DPO). We derive two enhancements to DPO: online diversity regularization and domain-specific priors. Additionally, we develop a new understanding on improving diversity in decoder models. When conditioned on OpenFold generated structures, our fine-tuned models achieve state-of-the-art structural similarity scores, improving base ProteinMPNN by at least 8%. Compared to standard DPO, our regularized method achieves up to 20% higher sequence diversity with no loss in structural similarity score.
@article{arxiv.2410.19471,
title = {Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization},
author = {Ryan Park and Darren J. Hsu and C. Brian Roland and Maria Korshunova and Chen Tessler and Shie Mannor and Olivia Viessmann and Bruno Trentini},
journal= {arXiv preprint arXiv:2410.19471},
year = {2026}
}