Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design

2026-08-18

AI Summary

本文通过引入MCTH框架，利用生成幻觉和生物物理信息建模解决从头设计DNA/RNA等生物分子相互作用难题。

Abstract

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.

Problem

• Biomolecular Design
• De Novo Interaction
• DNA/RNA
• Geometric and Chemical Constraints

Innovation

• Monte Carlo Tree Hallucination
• uncertainty-aware planning
• biophysics-informed modeling
• adaptive search