ASO-RASAR: A Read-Across Framework for Predicting Antisense Oligonucleotide Gapmer Activity Across Target Genes.

Authors

Seokyoung Hwang, Min Ju Lee, Junpyo Gong, In Guk Park, Minkyu Kim, Jayhyun Cho, Junseo Kang, Uijae Kim, Yeonjin Lee, Sein Park, Jooeun Park, Yoojin Shim, Yanlin Li, Kyuho Park, Sun Hee Jin, Min Won Ki, Seungchan An, Minsoo Noh

Published in

Journal of chemical information and modeling. Jun 27, 2026. Epub Jun 27, 2026.

Abstract

The development of predictive sequence-activity models capable of reliably identifying optimal antisense oligonucleotide (ASO) candidates across diverse target genes remains a central challenge in ASO drug discovery. Here, we curated a patent-derived data set of 59,273 gapmer ASOs (20-mer 5-10-5 MOE and 16-mer 3-10-3 cET) across 30 human genes and benchmarked gene-specific and cross-target prediction models using comprehensive sequence and target-context descriptors. Gene-specific models achieved strong predictive performance, with genomic context and sequence motifs as the most informative features. However, cross-target models evaluated by leave-one-gene-out cross-validation failed to generalize to new target genes, revealing that ASO activity is governed primarily by target-specific determinants. To address this limitation in data-scarce settings, we developed ASO-RASAR, a read-across sequence-activity relationship model that transfers predictive information from data-rich to data-poor target genes. In simulated low-data scenarios, the best ASO-RASAR strategy improved median AUPRC over standard QSAR baselines by up to 22.5%. Experimental validation using WFDC1-targeting ASOs in human bone marrow-derived mesenchymal stem cells supported that ASO-RASAR prediction scores correlated strongly with knockdown efficiency (Pearson's r = 0.83). These findings establish ASO-RASAR as a practical approach for prioritizing ASO candidates against novel targets with limited experimental data.

PMID:
42364111
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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