arXiv Machine Learning
RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning
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arXiv AI
Aug 19
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design
The paper introduces MCTH (Monte Carlo Tree Hallucination), an inference-only framework that performs all‑atom biomolecular sequence‑structure co‑design by treating pretrained folding and inverse‑folding models as black‑box operators. MCTH uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence, uncertainty, and cross‑expert consensus. Experiments across protein‑RNA, protein‑DNA, protein‑protein, and protein‑ligand design show that adaptive search outperforms simpler sampling strategies, and evaluations with AlphaFold3 and Chai‑1 demonstrate transferability beyond the search‑time oracle.
arXiv Machine Learning
Aug 6
RiboSphere: Learning Unified and Efficient Representations of RNA Structures
arXiv:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.
arXiv Machine Learning
Aug 21
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
arXiv:2506. 07459v4 Announce Type: replace Abstract: Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misalignment between supervised objectives and real design goals.
arXiv Machine Learning
Aug 31
RIBOSPAN: A Long-Context RNA Foundation Model for Versatile RNA Modeling
arXiv:2608.22849v2 Announce Type: replace Abstract: Full-length RNAs, particularly messenger RNAs, often exceed the context lengths used to pretrain existing RNA foundation models, limiting complete-...
arXiv AI
Jun 10
Flexible Flows for Biological Sequence Design
arXiv:2606. 10543v1 Announce Type: cross Abstract: Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints.
arXiv Machine Learning
Jun 8
Controllable Molecular Generative Foundation Models
arXiv:2605. 15354v2 Announce Type: replace Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability.