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Structure-aware Reinforcement Learning for Protein Directed Evolution — arXiv:2609.39048

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39048
License (as archived): CC0-1.0
Archived: 2026-10-07T12:44:49.935Z
SHA-256 of the archived text: 31dad01c0e97e41058cb143009ce6f1eeb64b7a734ffd4386cd73373150b6b45
Stable URL of this page: https://metraipolis.com/documento/dc_0e4aa439bfef

How to cite

Structure-aware Reinforcement Learning for Protein Directed Evolution — arXiv:2609.39048 — arXiv (en), https://arxiv.org/abs/2609.39048 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0e4aa439bfef (Metraipolis archive dc_0e4aa439bfef, sha256 31dad01c0e97e41058cb143009ce6f1eeb64b7a734ffd4386cd73373150b6b45)

BibTeX

@misc{dc0e4aa439bfef,
  title = {Structure-aware Reinforcement Learning for Protein Directed Evolution — arXiv:2609.39048},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39048},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0e4aa439bfef; sha256 31dad01c0e97e41058cb143009ce6f1eeb64b7a734ffd4386cd73373150b6b45},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39048. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose StructEvo, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior state-of-the-art methods by 9.2% and 16.3% on two challenging optimization benchmarks, and further identifies an experimentally validated epistasis pattern in GFP, highlighting the importance of structural guidance for effective protein directed evolution.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.39048 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:14:14.007Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).