mRNAWM: A Latent World Model for Multi-Objective Full-Length mRNA Design

Designing full-length messenger RNA (mRNA) sequences requires balancing translation-related properties, RNA structure, sequence composition, and manufacturability while preserving the encoded protein. These objectives interact across the coding sequence (CDS) and untranslated regions (UTRs), meaning that locally beneficial edits do not necessarily yield globally optimal designs. We formulate full-length mRNA design as a long-horizon, multi-objective decision problem and introduce mRNAWM, a protein-conditioned latent world model for controllable mRNA optimization. mRNAWM encodes a full-length mRNA into a latent state and learns action-conditioned dynamics over protein-preserving sequence edits. By rolling these dynamics forward, the model predicts the multi-step consequences of alternative edit trajectories on mRNA properties and future utility. An uncertainty-aware model predictive controller plans over these predicted trajectories, while preference-independent dynamics allow the same world model to support different objective trade-offs. mRNAWM (Full) outperforms all external methods on seven of the eight primary metrics, while the CDS-only configuration achieves the strongest Kozak score. The full-mRNA configuration reaches a codon adaptation index (CAI) of $0.945$ and a full-mRNA minimum free energy per nucleotide (MFE/nt) of $-0.401$. It further reduces homopolymer burden by 14.5% and uridine–guanosine (UG)-rich sequence burden by 19.1% relative to the strongest baseline for each metric. These results position latent world-model planning as a practical strategy for controllable biological sequence engineering under multiple interacting objectives.