RNAGenScape: Property-guided Optimization and Interpolation of mRNA Sequences with Manifold Langevin Dynamics

• URL: http://arxiv.org/abs/2510.24736v1
• Date: Tue, 14 Oct 2025 19:55:41 GMT
• Title: RNAGenScape: Property-guided Optimization and Interpolation of mRNA Sequences with Manifold Langevin Dynamics
• Authors: Danqi Liao, Chen Liu, Xingzhi Sun, Dié Tang, Haochen Wang, Scott Youlten, Srikar Krishna Gopinath, Haejeong Lee, Ethan C. Strayer, Antonio J. Giraldez, Smita Krishnaswamy,
• Abstract summary: RNAGenScape is a property-guided manifold Langevin dynamics framework that iteratively updates mRNA sequences within a learned latent manifold.<n>It supports property-guided optimization and smooth design between sequences, while remaining robust under scarce and undersampled data.<n> RNAGenScape establishes a paradigm for controllable mRNA design and latent space exploration in mRNA sequence modeling.
• Score: 14.76553653030291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: mRNA design and optimization are important in synthetic biology and therapeutic development, but remain understudied in machine learning. Systematic optimization of mRNAs is hindered by the scarce and imbalanced data as well as complex sequence-function relationships. We present RNAGenScape, a property-guided manifold Langevin dynamics framework that iteratively updates mRNA sequences within a learned latent manifold. RNAGenScape combines an organized autoencoder, which structures the latent space by target properties for efficient and biologically plausible exploration, with a manifold projector that contracts each step of update back to the manifold. RNAGenScape supports property-guided optimization and smooth interpolation between sequences, while remaining robust under scarce and undersampled data, and ensuring that intermediate products are close to the viable mRNA manifold. Across three real mRNA datasets, RNAGenScape improves the target properties with high success rates and efficiency, outperforming various generative or optimization methods developed for proteins or non-biological data. By providing continuous, data-aligned trajectories that reveal how edits influence function, RNAGenScape establishes a scalable paradigm for controllable mRNA design and latent space exploration in mRNA sequence modeling.

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