A generative-AI framework for target-Specific MicroRNAs towards RNAi-based drug design

MicroRNA (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the target messenger RNA (mRNA), whose versatility has inspired RNA-interference (RNAi)-based drug designs. However, off-target effects lead to unintended gene silencing and toxicity. Existing methods suffer from experimental data scarcity and fail to effectively integrate target specificity into designing de novo small interference RNAs (siRNA). To overcome the above challenges, we present SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR, a specificity-guided generative framework that synthesizes target-conditioned miRNAs. By training on a large experimental data containing 2.2M miRNA-mRNA pairs, SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR minimizes off-target effects with enhanced on-target potency. As a result, SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR-generated miRNAs bind more strongly to the target mRNAs than the observed miRNAs and much less so to off-target mRNAs. We tested SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR on mRNA targets for liver disease, for which 6 FDA-approved siRNA-based drugs were available. SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR recovers binding regions that correspond to FDA-approved siRNA drugs across 3 targets, and demonstrates greater structural specificity for on-target mRNAs than for off-target mRNAs. Together, SO_SCPLOWPECIC_SCPLOWMO_SCPLOWIC_SCPLOWR offers an AI solution to synthesize miRNA-inspired and target-specific siRNA sequences towards RNAi-based drug design. Code/data is available at https://anonymous.4open.science/r/SpeciMiR-278F