{"type":"ScholarlyArticle","title":"MP2D: Constrained Monte Carlo Tree-Guided Diffusion for Multi-Objective Protein Sequence Design","authors":[{"name":"Zitai Kong","slug":"zitai-kong","given":"Zitai","family":"Kong","affiliation":""},{"name":"Yifan Dong","slug":"yifan-dong","given":"Yifan","family":"Dong","affiliation":""},{"name":"Yixuan Wu","slug":"yixuan-wu","given":"Yixuan","family":"Wu","affiliation":""},{"name":"Zhaokang Liang","slug":"zhaokang-liang","given":"Zhaokang","family":"Liang","affiliation":""},{"name":"Jian Wu","slug":"jian-wu","given":"Jian","family":"Wu","affiliation":""},{"name":"Hongxia Xu","slug":"hongxia-xu","given":"Hongxia","family":"Xu","affiliation":""}],"abstract":"Designing functional protein sequences that satisfy multiple desired properties is a core research focus of protein engineering. Prior methods struggle with inability or inefficiency when dealing with numerous, often conflicting, properties. We propose Multi-Property Protein Diffusion, (MP2D), a unified framework for multi-objective protein sequence optimization that integrates conditional discrete diffusion with constrained MCTS and global iterative refinement. MP2D formulates diffusion denoising as a constrained sequential decision-making process and employs MCTS to explore diverse denoising trajectories guided by Pareto-based rewards. A global iterative refinement strategy further enables repeated remasking and re-optimization of candidate sequences, while a dynamic Pareto constraint prevents candidate bloat and maintains balanced trade-offs across objectives. We evaluate MP2D on two challenging multi-objective protein design tasks: antimicrobial peptide and protein binder optimization, involving four to five conflicting properties. Experimental results demonstrate that MP2D consistently outperforms existing multi-objective baselines, achieving robust and balanced improvements across all objectives without retraining generative models. These results highlight MP2D as a practical and scalable solution for multi-objective functional protein design.","doi":"10.24963/ijcai.2026/617","url":"https://ijcai.org/proceedings/2026/617","pdf":"https://ijcai.org/proceedings/2026/0617.pdf","markdown":"https://ijcai.org/proceedings/2026/617.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/617","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":617,"pages":{"first":5540,"last":5548},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics","Multidisciplinary Topics and Applications: Life sciences"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}