RNADynBench is a new standardized benchmark for RNA molecular dynamics, comprising 2,585 high‑quality 100‑nanosecond all‑atom trajectories with carefully controlled data splits. RNADynNet is a unified model that simultaneously generates RNA trajectories and extracts dynamics fingerprints from a single conformer, leveraging coordinate denoising, alignment, and physical grounding. The model achieves strong agreement with MD data, achieving RMSF correlations of 0.875 and 0.766 on test sets, including a high‑flexibility challenge set.
By Yiming Huang, Lennart Bastian, Hanqun Cao, Luis Vollmers, Tolga Birdal
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.
By Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu
La‑Ribo is a generative framework that jointly designs RNA nucleotide sequences and their three‑dimensional structures using geometry‑latent flow matching. It preserves a sparse phosphate‑sugar‑base scaffold while encoding nucleotide identity and local conformation in residue‑wise latent variables, which a shared flow network generates before an RNA‑specific decoder reconstructs all heavy atoms. The authors also built a large, quality‑controlled corpus of 168,561 RNA structures—combining experimental data with predictions from three folding models—to expand supervision, and demonstrate that La‑Ribo outperforms baselines in designability and co‑designability across sampling budgets and refolding models, while also enabling scaffold‑conditioned inverse folding without extra training.
By Runze Ma, Will Hua, Shuangjia Zheng
REFLEX is a sequence‑conditioned generator that produces RNA backbone‑frame ensembles using a heteroscedastic stochastic bridge on SE(3). It calibrates residue‑wise bridge widths with experimental B‑factors, allowing each observed conformer to be accessed through local perturbation neighborhoods while maintaining sharpness in constrained regions. In inference, REFLEX requires only the input sequence and, on a multi‑conformer RNA benchmark, achieves the best precision‑recall trade‑off for ensemble coverage across evaluation thresholds, with ablation studies showing improved target‑frame recovery under stronger perturbations.
By Wanli Ma, Tianmeng Hu, Ke Li
Designing RNA molecules that bind to target protein structures requires generating both an RNA conformation and its bound complex geometry, without assuming a predefined binding site. Existing approac...
arXiv:2607. 15309v1 Announce Type: cross Abstract: Proteins function through coordinated motion across multiple spatial and temporal scales, underpinning processes such as ligand binding, allostery, and catalysis.
By Kaihui Cheng, Zhiqiang Cai, Peng Tu, Yisong Yao, Limei Han, Libo Wu, Siyu Zhu, Tzuhsiung Yang, Yuan Qi
RiboBridge is a new structure-first framework for designing RNA molecules that bind to target proteins without assuming a predefined binding site. It separates RNA conformational generation from protein–RNA assembly by first proposing candidate binding interfaces on the protein surface, generating canonical RNA conformations in a pretrained RNA latent space (RiboSphere), and then predicting interaction constraints to bridge the RNA and protein coordinate frames. The predicted interaction geometry is converted into a geometrically consistent protein-relative pose via rigid placement, and RiboBridge outperforms existing methods on a benchmark of 124 protein–RNA complexes, achieving higher fNAT and DockQ scores and lower RMSDs.
By Zhou Zhang, Hanqun Cao, Minghao Sun, Odin Zhang, Yang Zhang, Pheng Ann Heng, Wujun Li, Tianfan Fu
Eco‑RNA is an 80‑million‑parameter RNA foundation model that introduces adjustable inference depth via learned iterative refinement. The shared, input‑conditioned Transformer core repeatedly refines sequence representations, with the number of iterations chosen after training to control effective depth. On the RNAGym fitness benchmark, increasing iterations from one to four improves Spearman correlation from 0.242 to 0.286, and at four iterations Eco‑RNA outperforms the 40‑billion‑parameter Evo 2 model across all aggregate fitness metrics, while also excelling in non‑coding RNA classification, secondary‑structure prediction, and chemical probing profile prediction.
By Pengan Chen, Liang Hong, Xinyuan Liu, Han Huang, Ziqian Lin, Jiayang Chen, Yimin Fan, Jiyue Jiang, Yu Li
arXiv:2611.04287v2 Announce Type: replace-cross
Abstract: Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, an...
By Lisheng Zhang, Qingsong Li, Zinan Xie, Yanting Yang, Qikui Yang, Peng Lu, Zhican Fu, Wentao Cui, Runtong Hou, Zhengwei Xie
arXiv:2510.07589v2 Announce Type: replace-cross
Abstract: Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conve...
By Juan Viguera Diez, Mathias Schreiner, Simon Olsson
The paper introduces GOLD, a Geometric Optimized Latent Diffusion framework that jointly models RNA sequences and their side‑chain geometries in a unified latent space using a novel GeoVAE. By co‑diffusing sequence and structure and employing a gradient‑guided sampling step that enforces physical constraints, GOLD achieves superior performance in sequence recovery, secondary structure validity, and 3D foldability compared to existing inverse‑folding methods.
By Qi Si, Xuyang Liu, Penglei Wang, Shuo Su, Limei Han, Mengzhu Li, Xin Guo, Yuan Qi, Yuan Cheng
arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.
By Shentong Mo, Yatao Bian