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
Metadata từ BioTender-max/icml2026-ai-bio (CC0-1.0). Phở không lưu trữ bản PDF; link trỏ về nguồn gốc.
Cùng chủ đề
Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang +4
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via…
FIDIA: Function-Informed Sequence Design via Inference-Aligned Policy Optimization
Minghan Li, Fengji Li, Yilin Tao, Yue Deng
Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly…
FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications
Kieran Didi, Sarah Alamdari, Alex Xijie Lu, Bruce James Wittmann +4
Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered…