{"type":"ScholarlyArticle","title":"TTS-Design: Test-Time Compute Scaling for Structure-Guided Protein Design","authors":[{"name":"Zizhe Jin","slug":"zizhe-jin","given":"Zizhe","family":"Jin","affiliation":""},{"name":"Yizhen Zheng","slug":"yizhen-zheng","given":"Yizhen","family":"Zheng","affiliation":""},{"name":"Huan Yee Koh","slug":"huan-yee-koh","given":"Huan Yee","family":"Koh","affiliation":""},{"name":"Jiaxin Ju","slug":"jiaxin-ju","given":"Jiaxin","family":"Ju","affiliation":""},{"name":"Jiapu Wang","slug":"jiapu-wang","given":"Jiapu","family":"Wang","affiliation":""},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":""}],"abstract":"Generating protein sequences that reliably fold into target structures is a central challenge in computational biology and protein design. Progress in protein inverse folding (PIF), however, is fundamentally constrained by the scarcity of high-quality structural data, which limits the effectiveness of existing models. In this work, we propose TTS-Design, a test-time compute scaling framework that enhances protein sequence design without retraining models or relying on larger training data. For TTS-Design, its core design is a dedicated inverse folding reward model named IF-RM, which quantitatively evaluates structure–sequence compatibility and enables effective selection among multiple candidate sequences at inference time. By integrating IF-RM into existing PIF models, TTS-Design leverages additional computation during inference stage to explore and refine candidate solutions. Extensive experiments on the CATH 4.2 and CATH 4.3 demonstrate that TTS-Design consistently improves sequence recovery and structural reliability across different backbone models, without retraining or increasing model size. Case studies on real-world proteins further confirm its practical effectiveness. Overall, our results highlight test-time compute scaling as a promising and effective paradigm for advancing protein inverse folding and other scientific problems under data-limited scenarios.","doi":"10.24963/ijcai.2026/616","url":"https://ijcai.org/proceedings/2026/616","pdf":"https://ijcai.org/proceedings/2026/0616.pdf","markdown":"https://ijcai.org/proceedings/2026/616.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/616","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":616,"pages":{"first":5531,"last":5539},"keywords":["Machine Learning: Sequence and graph learning","Multidisciplinary Topics and Applications: Bioinformatics","AI: Machine Learning","Multidisciplinary Topics and Applications: Health and medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}