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Proceedings of the AAAI Conference on Artificial Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research Modulation-Based Backdoors: Leveraging Amplitude and Frequency Patterns to Attack Speaker Recognition Learning Structurally Stabilized Representations for Lossless DNA Storage ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation Toward Multimodal Fake News Detection by Multi-perspective Rationale Generation and Verification RTMol: Rethinking Molecule-text Alignment in a Round-trip View Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment Transferring Causal Driving Patterns for Generalizable Traffic Simulation with Diffusion-Based Distillation TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction SIDE: Surrogate Conditional Data Extraction from Diffusion Models DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics Light but Sharp: SlimSTAD for Real-Time Action Detection from Sensor Data VFCionX: Bridging Large and Small Models for Robust Vulnerability-Fixing Commit Identification T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving DensiCrafter: Physically-Constrained Generation and Fabrication of Self-Supporting Hollow Structures Topology-Enhanced and Label Correlation-Aware Model for Protein-Protein Interaction Prediction InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning Unveiling the Attribute Misbinding Threat in Identity-Preserving Models
RMSAGen: Integrating Multiple Sequence Alignment for Func...
Jiyue Jiang, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Jiyue Jiang The Chinese University of Hong Kong
  • Yanyu Chen The Chinese University of Hong Kong
  • Qingchuan Zhang The Chinese University of Hong Kong
  • Jiayi Li The Chinese University of Hong Kong
  • Xiangyu Shi The Chinese University of Hong Kong
  • Chang Zhou The Chinese University of Hong Kong
  • Ziqian Lin The Chinese University of Hong Kong
  • Jiuming Wang The Chinese University of Hong Kong
  • Dongchen He The Chinese University of Hong Kong
  • Liang Hong The Chinese University of Hong Kong
  • Qintong Li The University of Hong Kong
  • Pengan Chen The Chinese University of Hong Kong
  • Jiayang Chen The Chinese University of Hong Kong
  • Xinrui Zhang The Chinese University of Hong Kong
  • Jiao Yuan Guangzhou National Laboratory Guangzhou Medical University
  • Tianqing Zhang Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Yu Li The Chinese University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v40i1.37012

Abstract

Biological sequences, including RNAs and proteins, share similarities with natural languages, enabling the application of advanced language models to various biological tasks. However, due to its flexibility and lack of experimental data, RNA is a particularly challenging biological ``language'' compared to other biological sequences like proteins. RNA multiple sequence alignments (MSAs), which align evolutionarily related RNA sequences, can greatly enhance RNA biology modeling, as evidenced by their significant roles in structure prediction and function annotation. This raises the question of whether RNA MSAs can also benefit RNA design, which remains unexplored. This paper introduces RMSAGen, a model comprising RMSA-Encoder and RMSA-Decoder, that leverages MSAs to design functional RNA sequences. RMSA-Encoder effectively extracts MSA features, enhancing performance in functional prediction and solvent accessibility prediction tasks and supporting RMSA-Decoder in accurate RNA generation. RMSAGen can design RNA sequences that effectively bind to target RNA-binding proteins, and the design performance improves with an increasing number of sequences. In addition, the ribozymes designed with structural features by RMSAGen show strong computational metrics and exhibit biological activity during gel electrophoresis. These results highlight the effectiveness of RMSAGen, establishing it as a powerful tool and a new direction for RNA design.

How to Cite

Jiang, J., Chen, Y., Zhang, Q., Li, J., Shi, X., Zhou, C., … Li, Y. (2026). RMSAGen: Integrating Multiple Sequence Alignment for Function RNA Design. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 489–497. https://doi.org/10.1609/aaai.v40i1.37012

Issue

Section

AAAI Technical Track on Application Domains I

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