siDiff: Efficacy-Guided Discrete Diffusion for siRNA Target-Site Discovery

High-quality experimental data for siRNA efficacy remain scarce, limiting the generalization of discriminative predictors to unseen genes. Although conventional pipelines enumerate all complementarity-valid target windows, prediction errors may concentrate top-ranked candidates within a few local regions while leaving other experimentally active regions under-ranked. Recovering such regions is important for therapeutic discovery, where a diverse portfolio of non-redundant candidates is needed to withstand downstream attrition caused by accessibility, off-target effects, chemical modification, and safety constraints. We propose siDiff, an efficacy-guided discrete diffusion framework for siRNA target-site discovery. siDiff first learns a target-conditioned distribution over functional siRNA duplexes and then uses efficacy guidance to reshape this learned distribution during sampling, increasing the probability of efficacy-favored sequence patterns and target regions without retraining the generator. Its coarse-to-fine sampler progressively reveals the siRNA sequence: position guidance prioritizes coordinates with high efficacy potential, while delayed base guidance steers nucleotide assignments only after sufficient sequence context has emerged. By conditioning each decision on an increasingly informative partial sequence, this progressive process captures combinatorial motifs and higher-order intra-siRNA dependencies more explicitly than single-pass candidate scoring. The guided active patterns consequently recover promising regions that may be under-ranked by discriminative predictors, while reverse-complement construction and spatial clustering preserve duplex validity and regional diversity. Experiments on the public Takayuki benchmark and three patent-derived targets demonstrate improved positional recovery and coverage of distinct active regions on unseen genes.