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REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge
Wanli Ma, Tianmeng Hu, Ke Li

REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge

Author / Organization:
Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

RNA molecules often function through multiple experimentally observed conformational states. A central challenge is to capture ensemble diversity without sacrificing structural fidelity: samples may collapse toward averaged representative structures or drift into unsupported conformational regions. We introduce REFLEX (RNA Ensemble generation calibrated by FLEXibility), a sequence-conditioned generator for RNA backbone-frame ensembles based on a heteroscedastic stochastic bridge on SE(3). During training, experimental B-factors calibrate residue-wise bridge widths, exposing each observed conformer through local perturbation neighborhoods with residue-specific scales while keeping constrained regions sharper. A learned flexibility condition guides the bridge-field network to recover target conformer-specific frames from these calibrated noisy states. At inference, REFLEX uses only the input sequence, requiring no MSA-subsampling heuristics. On a multi-conformer RNA benchmark, REFLEX achieves the best precision-recall trade-off for ensemble coverage across evaluation thresholds, while recovery-stress ablations indicate gains in target-frame recovery under stronger perturbations.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge
Author / Organization Wanli Ma, Tianmeng Hu, Ke Li
Subject & Discipline cs.AI
Categories Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-273183
Original Source Link Visit Source Page on arxiv.org »