Monte Carlo Tree Diffusion with Multiple Experts for Protein Design

• URL: http://arxiv.org/abs/2509.15796v1
• Date: Fri, 19 Sep 2025 09:24:42 GMT
• 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,
• Abstract summary: We propose MCTD-ME, which integrates masked diffusion models with tree search to enable multi-token planning and efficient exploration.<n>Unlike autoregressive planners, MCTD-ME uses biophysical-enhanced diffusion denoising as the rollout engine.<n>The framework is model-agnostic and applicable beyond inverse folding, including de novo protein engineering and multi-objective molecular generation.
• Score: 50.056670856059014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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 predictive-entropy UCT to expert ensembles. On the inverse folding task (CAMEO and PDB benchmarks), MCTD-ME outperforms single-expert and unguided baselines in both sequence recovery (AAR) and structural similarity (scTM), with gains increasing for longer proteins and benefiting from multi-expert guidance. More generally, the framework is model-agnostic and applicable beyond inverse folding, including de novo protein engineering and multi-objective molecular generation.

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