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About this paper
Xuefeng Liu, Mingxuan Cao, Songhao Jiang, Xiao Luo, Xiaotian Duan, Mengdi Wang, Tobin R. Sosnick, Jinbo Xu and 1 more
Monte Carlo Tree Diffusion with Multiple Experts (MCTD-ME) integrates masked diffusion models with Monte Carlo Tree Search and an ensemble of domain-specific experts to generate high-quality protein sequences. This approach improves performance across protein folding, inverse folding, and motif scaffolding, for instance, reducing backbone RMSD from 10.50 Å to 9.41 Å on the CAMEO benchmark for folding tasks.
Key insights
- Integrating diffusion models with MCTS allows for more effective planning and revision in protein design, surpassing the limitations of both autoregressive token-by-token generation and one-shot diffusion sampling.
- Orchestrating an ensemble of domain-specific experts within the MCTS framework enhances exploration diversity and robustness, leading to more biophysically plausible and high-quality designs.
- Intelligently guiding the search with structural confidence scores (pLDDT masking) and multi-expert consensus/disagreement (PH-UCT-ME) significantly improves the efficiency and quality of the protein design process.
Summary by alphaXiv.
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