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arXiv Machine Learning By Jixin Tang, Ji Guo, Jun Zhao

MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design

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MORAD is an online reinforcement learning framework that trains a single shared policy for backbone‑conditioned RNA diffusion models, enabling structure‑conditioned RNA inverse design across diverse target backbones. It integrates backbone‑conditioned group sampling, group‑relative reward normalization, and a forward‑process diffusion objective, jointly optimizing six measurements—including tertiary geometry, base pairing, ensemble behavior, and sequence composition—by mapping each to a bounded desirability score and combining them with a weighted geometric mean. Compared to a tertiary‑structure‑only reward, MORAD improves 10 of 11 evaluation metrics, achieving significant gains in GDT‑TS, pairing MCC, and normalized ensemble defect, and surpasses existing methods such as RiboDiffusion, gRNAde, and RDesign on tertiary structural accuracy, secondary‑structure pairing, and thermodynamic quality.

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