{
  "id": 4578,
  "url": "https://arxiv.org/abs/2605.10189v1",
  "title": "ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design",
  "summary": "Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein language models (PLMs) have enabled the generation of highly designable protein sequences, while preference alignment provides a promising way to steer designs toward desired functions and properties. Nevertheless, they often trigger catastrophic forgetting of pretrained knowledge, degrading basic designability and failing to balance multiple competin",
  "authors": "Yulin Zhang, He Cao, Zihao Jiang, Chenyi Zi, Zhipeng Zhou, Zijing Liu et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T08:38:51.000Z",
  "fetched_at": "2026-07-14T16:31:08.353Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4578",
  "original_url": "https://arxiv.org/abs/2605.10189v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}