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[2509.15796] Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Summary
The paper presents MCTD-ME, a novel approach combining Monte Carlo Tree Search and masked diffusion models for efficient protein design, addressing challenges in long-range dependencies and search space size.
Why It Matters
This research is significant as it proposes a new method for protein design that enhances exploration and efficiency, potentially leading to advancements in biotechnology and medicine. By integrating multiple expert systems, it addresses limitations of existing methods, making it a valuable contribution to the field of machine learning and bioinformatics.
Key Takeaways
- MCTD-ME integrates masked diffusion models with Monte Carlo Tree Search for protein design.
- The method improves exploration efficiency by utilizing multiple experts.
- It addresses long-range dependency issues in protein folding tasks.
- MCTD-ME outperforms existing benchmarks in protein design challenges.
- The framework is model-agnostic and extensible for various applications in protein engineering.
Computer Science > Machine Learning arXiv:2509.15796 (cs) [Submitted on 19 Sep 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Monte Carlo Tree Diffusion with Multiple Experts for Protein Design Authors:Xuefeng Liu, Mingxuan Cao, Songhao Jiang, Xiao Luo, Xiaotian Duan, Mengdi Wang, Tobin R. Sosnick, Jinbo Xu, Rick Stevens View a PDF of the paper titled Monte Carlo Tree Diffusion with Multiple Experts for Protein Design, by Xuefeng Liu and 8 other authors View PDF HTML (experimental) Abstract:The goal of protein design is to generate amino acid sequences that fold into functional structures with desired properties. Prior methods combining autoregressive language models with Monte Carlo Tree Search (MCTS) struggle with long-range dependencies and suffer from an impractically large search space. We propose MCTD-ME, Monte Carlo Tree Diffusion with Multiple Experts, which integrates masked diffusion models with tree search to enable multi-token planning and efficient exploration under the guidance of multiple experts. Unlike autoregressive planners, MCTD-ME uses biophysical-fidelity-enhanced diffusion denoising as the rollout engine, jointly revising multiple positions and scaling to large sequence spaces. It further leverages experts of varying capacities to enrich exploration, guided by a pLDDT-based masking schedule that targets low-confidence regions while preserving reliable residues. We propose a novel multi-expert selection rule ( PH-UCT-ME) extends Shannon...