Jixin Tang

Structure-conditioned RNA inverse design previously either trained a separate policy per target backbone or relied on offline preference optimization, leaving online post-training of a reusable backbone-conditioned model largely unexplored. MORAD maps six design measurements to bounded desirability scores, combines them with a weighted geometric mean, and uses group-relative normalization to drive a forward-process diffusion update. On 153 evaluation targets it raises GDT-TS from 0.3300 to 0.3827 and pairing MCC from 0.6110 to 0.7295, while cutting normalized ensemble defect from 0.3906 to 0.2830.

Structure-conditioned RNA inverse design previously either trained a separate policy per target backbone or relied on offline preference optimization, leaving online post-training of a reusable backbone-conditioned model largely unexplored. MORAD maps six design measurements to bounded desirability scores, combines them with a weighted geometric mean, and uses group-relative normalization to drive a forward-process diffusion update. On 153 evaluation targets it raises GDT-TS from 0.3300 to 0.3827 and pairing MCC from 0.6110 to 0.7295, while cutting normalized ensemble defect from 0.3906 to 0.2830.