Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation
The design of RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Despite recent progress in natural language modeling and deep learning-based protein design, there remains significant room to improve the frequency of successful interactions and the authenticity of generated sequences for functional applications. In this work, we frame conditional RNA sequence generation as a multi-stage alignment problem, introducing Moirain: a suite of models optimized via multimodal supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). Our approach begins with large-scale pretraining on diverse RNA corpora to capture the...
| Authors | Roman Klypa, Alberto Bietti, Sergei Grudinin |
|---|---|
| Primary category | q-bio.BM |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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