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Life sciences · Preprint
arXiv · August 18, 2026
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MCTH is a novel computational planning framework that combines Monte Carlo Tree Search with pretrained folding and inverse-folding models to perform sequence-structure co-design across multiple biomolecular modalities. The work demonstrates proof-of-concept through in silico evaluation but lacks experimental validation, peer review, and any clinical or real-world validation data.
Preprint. Intervention: MCTH framework: Monte Carlo Tree Search applied to hallucinated states from pretrained folding and inverse-folding models with optional biophysical control. Compared with: Simpler sampling and cycling strategies in matched-budget experiments.
Adaptive Monte Carlo Tree Search search improves over simpler sampling and cycling strategies in matched-budget experiments Framework demonstrates transfer to held-out AlphaFold3 and Chai-1 model evaluations Unified planning layer operates across protein-RNA, protein-DNA, protein-protein, and protein-ligand design modalities
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This is a methodological preprint presenting a computational framework for biomolecular design without peer review, experimental validation on real molecules, or clinical outcome data.
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Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heterogeneous complex data and sharper geometric and chemical constraints. We introduce MCTH (Monte Carlo Tree Hallucination), an inference-only framework that casts all-atom sequence-structure co-design as uncertainty-aware planning over hallucinated states from pretrained folding and inverse-folding models, with optional biophysical control within the same decision loop. MCTH treats these models as frozen black-box operators and uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence and uncertainty, as well as cross-expert consensus/disagreement when multiple predictors are available. Across protein-RNA, protein-DNA, protein-protein, and protein-ligand design, matched-budget experiments show that adaptive search improves over simpler sampling and cycling strategies, while held-out AlphaFold3 and Chai-1 evaluations demonstrate transfer beyond the search-time oracle. MCTH provides a shared planning layer across modalities while allowing task-specific folding, inverse-folding, and biophysical modules, requiring no fine-tuning or backpropagation through component models.
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