Chiba, S.; Kunitake, K.; Shirakaki, S.; Haque, U. S.; Wilton-Clark, H.; Shah, M. N. A.; Leckie, J. N.; Matsui, K.; Uno-Ono, F.; Yokota, T.; Aoki, Y.; Okuno, Y.

Antisense oligonucleotides (ASOs) for exon skipping are increasingly used to correct pathogenic splicing; however, rational target-region selection remains difficult because regulatory information is distributed across exons, introns, and splice junctions. Here we present eSkip2, a framework for prioritizing exon-skipping ASO target regions from joint exon--intron sequence context. eSkip2 combines transfer learning from a genome-pretrained foundation model with joint training on ASO activity and SNV-derived splicing perturbation data and can be adapted to a target locus without experimental ASO labels. Across multi-gene benchmarks spanning canonical exons, pseudoexons, cell types, chemistries, and exonic, intronic, and exon--intron-spanning targets, eSkip2 robustly prioritized active regions; in exon-confined comparisons, it showed improved overall performance compared with applicable existing models. It also supported prospective design of dual-targeting ASOs for DMD exon 46, where top-ranked candidates were enriched for active ASOs and yielded dose-dependent dystrophin restoration. eSkip2 narrows the experimental search space across diverse target architectures.