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Co-Generative De Novo Functional Protein Design

Xinrui Chen, Yizhen Luo, Siqi Fan, Zaiqing Nie

ICML 2026 regular

Tóm tắt (nguồn: OpenReview · © tác giả)

*De novo* functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applications in biotechnology and medicine. Existing approaches adopt either direct function-to-sequence mapping or decoupled structure-sequence generation strategies but often fail to achieve functionality and foldability simultaneously. To address this, we propose **CodeFP**, a **Co**-generative protein language model for ***de** novo* **F**unctional **P**rotein design that simultaneously decodes sequence and structure tokens, thereby enabling superior simultaneous realization of functionality and foldability. CodeFP utilizes functional local structures to enrich functional semantic encodings, overcoming the suboptimal translation of flat encodings into structure tokens, while introducing auxiliary functional supervision to alleviate training ambiguity stemming from the one-to-many structure-to-token mapping. Extensive experiments show that CodeFP consistently achieves average improvements of 6.1\% in functional consistency and 3.2\% in foldability over the strongest baseline.

Từ khoá

function protein design co-generation

Metadata từ BioTender-max/icml2026-ai-bio (CC0-1.0). Phở không lưu trữ bản PDF; link trỏ về nguồn gốc.

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