RNASeek uses a 1.6B-parameter cross-phyla transcriptomic model for RNA function prediction and GRPO-guided design, yielding ribozymes at wild-type activity and 3′ UTRs beyond the training data
The authors present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus, using natural-language tokens for conditional prediction and sequence design; it captures species-specific transcript features and intron–exon boundaries in a zero-shot setting, can be fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability while revealing interpretable features such as loop flexibility, stem stability, and AU-rich motifs, and these functional predictors then serve as reward models for GRPO updates to the generation policy, producing faster-cleaving ribozymes and stability-enhancing 3′ UTRs under user-specified IUPAC constraints, with experimentally validated generated ribozymes reaching