Moirain: Multimodal Alignment for RNA Sequence Generation

Researchers introduced Moirain, a suite of models for conditional RNA sequence generation using multimodal supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). The approach frames RNA design as a multi-stage alignment problem, beginning with large-scale pretraining on diverse RNA corpora to capture sequence plausibility. Target-specific generation is achieved by conditioning RNA synthesis on protein structural and sequential features via multimodal SFT. DPO is then used to refine the model, improving the frequency of successful interactions and authenticity of generated sequences for functional applications. The work addresses the challenge of designing RNA molecules that interact with specific proteins, a critical need in experimental and computational biology. The paper is available on arXiv under ID 2605.23961.