@inproceedings{jiang-etal-2026-utilizing,
title = "Utilizing Geometric Structural Features for Functional {RNA} Design",
author = "Jiang, Jiyue and
Chen, Yuhan and
Shi, Xiangyu and
Ye, Yihan and
Huang, Han and
Chen, Yanyu and
Shan, Yuheng and
He, Dongchen and
Yuan, Jiao and
Li, Yu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.141/",
pages = "2913--2923",
ISBN = "979-8-89176-462-0",
abstract = "RNA{'}s conformational flexibility enables diverse biological functions but makes structure-guided design difficult. Although precise control of 3D structure is essential, existing methods often overlook the molecular surface, which critically shapes intermolecular interactions and functional outcomes. To address this, we propose gRFold, a multimodal framework that incorporates molecular surface information as a core design constraint. gRFold employs two geometric encoders: a Frame-Averaged Geometric Surface Encoder (FA-GSE) to learn E(3)-invariant surface features, and an Equivariant Backbone Graph Perceptron (EBGP) to capture backbone topology. The structural representations are integrated via hierarchical cross-attention to guide autoregressive generation; at inference, candidates are re-ranked by agreement between their predicted folds and the target base-pairing pattern. Across three RNA inverse-folding benchmarks covering RNA-protein complexes, monomeric RNAs and independently curated RNA-Puzzles targets, gRFold achieves strong sequence recovery and structural fidelity under both secondary-structure and 3D refolding evaluations, and ablations show that the surface and backbone branches contribute complementary gains. These results highlight the potential of surface-aware geometric modeling for structure-guided RNA design."
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<abstract>RNA’s conformational flexibility enables diverse biological functions but makes structure-guided design difficult. Although precise control of 3D structure is essential, existing methods often overlook the molecular surface, which critically shapes intermolecular interactions and functional outcomes. To address this, we propose gRFold, a multimodal framework that incorporates molecular surface information as a core design constraint. gRFold employs two geometric encoders: a Frame-Averaged Geometric Surface Encoder (FA-GSE) to learn E(3)-invariant surface features, and an Equivariant Backbone Graph Perceptron (EBGP) to capture backbone topology. The structural representations are integrated via hierarchical cross-attention to guide autoregressive generation; at inference, candidates are re-ranked by agreement between their predicted folds and the target base-pairing pattern. Across three RNA inverse-folding benchmarks covering RNA-protein complexes, monomeric RNAs and independently curated RNA-Puzzles targets, gRFold achieves strong sequence recovery and structural fidelity under both secondary-structure and 3D refolding evaluations, and ablations show that the surface and backbone branches contribute complementary gains. These results highlight the potential of surface-aware geometric modeling for structure-guided RNA design.</abstract>
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%0 Conference Proceedings
%T Utilizing Geometric Structural Features for Functional RNA Design
%A Jiang, Jiyue
%A Chen, Yuhan
%A Shi, Xiangyu
%A Ye, Yihan
%A Huang, Han
%A Chen, Yanyu
%A Shan, Yuheng
%A He, Dongchen
%A Yuan, Jiao
%A Li, Yu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jiang-etal-2026-utilizing
%X RNA’s conformational flexibility enables diverse biological functions but makes structure-guided design difficult. Although precise control of 3D structure is essential, existing methods often overlook the molecular surface, which critically shapes intermolecular interactions and functional outcomes. To address this, we propose gRFold, a multimodal framework that incorporates molecular surface information as a core design constraint. gRFold employs two geometric encoders: a Frame-Averaged Geometric Surface Encoder (FA-GSE) to learn E(3)-invariant surface features, and an Equivariant Backbone Graph Perceptron (EBGP) to capture backbone topology. The structural representations are integrated via hierarchical cross-attention to guide autoregressive generation; at inference, candidates are re-ranked by agreement between their predicted folds and the target base-pairing pattern. Across three RNA inverse-folding benchmarks covering RNA-protein complexes, monomeric RNAs and independently curated RNA-Puzzles targets, gRFold achieves strong sequence recovery and structural fidelity under both secondary-structure and 3D refolding evaluations, and ablations show that the surface and backbone branches contribute complementary gains. These results highlight the potential of surface-aware geometric modeling for structure-guided RNA design.
%U https://aclanthology.org/2026.findings-emnlp.141/
%P 2913-2923
Markdown (Informal)
[Utilizing Geometric Structural Features for Functional RNA Design](https://aclanthology.org/2026.findings-emnlp.141/) (Jiang et al., Findings 2026)
ACL
- Jiyue Jiang, Yuhan Chen, Xiangyu Shi, Yihan Ye, Han Huang, Yanyu Chen, Yuheng Shan, Dongchen He, Jiao Yuan, and Yu Li. 2026. Utilizing Geometric Structural Features for Functional RNA Design. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2913–2923, Budapest, Hungary. Association for Computational Linguistics.