Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
#Monte Carlo Tree Diffusion #Protein Design #Multiple Experts #Machine Learning #Computational Biology #Amino Acid Sequences #Biophysical Modeling
📌 Key Takeaways
- Researchers developed MCTD-ME, a novel approach combining masked diffusion models with tree search for protein design
- The method addresses limitations in previous approaches by handling long-range dependencies and reducing search space
- MCTD-ME uses biophysical-fidelity-enhanced diffusion denoising and a novel multi-expert selection rule
- The framework demonstrates superior performance on protein design benchmarks and is model-agnostic
📖 Full Retelling
Researchers led by Xuefeng Liu and a team of eight collaborators from academic institutions have developed MCTD-ME (Monte Carlo Tree Diffusion with Multiple Experts), a novel approach for protein design that integrates masked diffusion models with tree search to enable multi-token planning and efficient exploration in the field of computational biology. The research paper, published on arXiv on February 23, 2026, addresses limitations in previous methods that struggled with long-range dependencies and impractically large search spaces when combining autoregressive language models with Monte Carlo Tree Search. The new methodology aims to improve the generation of amino acid sequences that fold into functional structures with desired properties, representing a significant advancement in computational protein design. MCTD-ME distinguishes itself from autoregressive planners by using biophysical-fidelity-enhanced diffusion denoising as the rollout engine, which allows for joint revision of multiple positions and scaling to large sequence spaces. The approach leverages experts of varying capacities to enrich exploration, guided by a pLDDT-based masking schedule that targets low-confidence regions while preserving reliable residues. The researchers also proposed a novel multi-expert selection rule called PH-UCT-ME, which extends Shannon-entropy-based UCT to expert ensembles with mutual information, enhancing the algorithm's ability to navigate complex protein design spaces. The MCTD-ME framework has demonstrated superior performance on the CAMEO and PDB benchmarks, excelling in protein design tasks such as inverse folding, folding, and conditional design challenges like motif scaffolding on lead optimization tasks. Importantly, the framework is model-agnostic, plug-and-play, and extensible to de novo protein engineering and beyond, suggesting broad applications in drug discovery, enzyme design, and other biotechnology fields where precise protein engineering is critical.
🏷️ Themes
Protein Design, Machine Learning, Computational Biology
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Original Source
--> Computer Science > Machine Learning arXiv:2509.15796 [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 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 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-entropy-based UCT to expert ensembles with mutual information. MCTD-ME achieves superior performance on the CAMEO and PDB benchmarks, excelling in protein design tasks such as inverse folding, folding, and conditional design challenges like motif scaffolding on lead optimization tasks. Our framework is model-agnostic, plug-and-play, and extensible to denovo protein engineering and beyond. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI q-bio.BM) Cite as: arXiv:2509.15796 [cs.LG] (or arXi...
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