# RNA-GPT: Multimodal Generative System for RNA Sequence Understanding

arXiv: https://arxiv.org/abs/2411.08900 · HTML: https://mlsanity.com/paper/2411.08900/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [InstructProtein: Aligning Human and Protein Language via Knowledge Instruction](https://mlsanity.com/paper/2310.03269/) (2023)
- [Modeling Protein Using Large-scale Pretrain Language Model](https://mlsanity.com/paper/2108.07435/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [Protein Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.17504/) (2025)
- [Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach](https://mlsanity.com/paper/2505.18882/) (2025)
- [Large Language Models in Bioinformatics: A Survey](https://mlsanity.com/paper/2503.04490/) (2025)
- [Designing RNAs with Language Models](https://mlsanity.com/paper/2602.12470/) (2026)
- [Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents](https://mlsanity.com/paper/2504.08739/) (2025)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)
- [Scaling-Aware Adapter for Structure-Grounded LLM Reasoning](https://mlsanity.com/paper/2602.02780/) (2026)

## Abstract (arXiv)

RNAs are essential molecules that carry genetic information vital for life, with profound implications for drug development and biotechnology. Despite this importance, RNA research is often hindered by the vast literature available on the topic. To streamline this process, we introduce RNA-GPT, a multi-modal RNA chat model designed to simplify RNA discovery by leveraging extensive RNA literature. RNA-GPT integrates RNA sequence encoders with linear projection layers and state-of-the-art large language models (LLMs) for precise representation alignment, enabling it to process user-uploaded RNA sequences and deliver concise, accurate responses. Built on a scalable training pipeline, RNA-GPT utilizes RNA-QA, an automated system that gathers RNA annotations from RNACentral using a divide-and-conquer approach with GPT-4o and latent Dirichlet allocation (LDA) to efficiently handle large datasets and generate instruction-tuning samples. Our experiments indicate that RNA-GPT effectively addresses complex RNA queries, thereby facilitating RNA research. Additionally, we present RNA-QA, a dataset of 407,616 RNA samples for modality alignment and instruction tuning, further advancing the potential of RNA research tools.