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Updated: Jun 29, 2026

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing
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ASO-RASAR: A Read-Across Framework for Predicting Antisense Oligonucleotide Gapmer Activity Across Target Genes.

Seokyoung Hwang1, Min Ju Lee1, Junpyo Gong1

  • 1College of Pharmacy, Natural Products Research Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu Seoul 08826, Republic of Korea.

Journal of Chemical Information and Modeling
|June 27, 2026
PubMed
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This summary is machine-generated.

Developing effective antisense oligonucleotide (ASO) drugs is challenging. A new model, ASO-RASAR, uses data from known targets to predict effective ASOs for new targets, improving drug discovery efficiency.

Area of Science:

  • Drug Discovery
  • Bioinformatics
  • Oligonucleotide Therapeutics

Background:

  • Predictive modeling for antisense oligonucleotide (ASO) drug discovery is crucial but faces challenges in generalizing across diverse target genes.
  • Existing sequence-activity relationship (QSAR) models often fail to predict ASO efficacy for novel targets due to target-specific determinants.

Purpose of the Study:

  • To develop a novel predictive model, ASO-RASAR, that transfers sequence-activity relationship information from data-rich to data-poor target genes.
  • To address limitations in ASO candidate prioritization for targets with scarce experimental data.

Main Methods:

  • Curated a dataset of 59,273 gapmer ASOs across 30 human genes.
  • Benchmarked gene-specific and cross-target prediction models using sequence and target-context descriptors.

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  • Developed and validated the ASO-RASAR read-across model for predicting ASO activity in low-data scenarios.

    • Main Results:

    • Gene-specific models showed strong performance, with genomic context and sequence motifs being key features.
    • Cross-target models demonstrated poor generalization to new genes, highlighting target-specific activity.
    • ASO-RASAR improved prediction performance by up to 22.5% in simulated low-data settings.
    • Experimental validation showed ASO-RASAR scores strongly correlated with knockdown efficiency (Pearson's r = 0.83).

    Conclusions:

    • ASO activity is primarily governed by target-specific factors, limiting generalizability of standard models.
    • ASO-RASAR provides a practical solution for prioritizing ASO candidates against novel targets with limited data.
    • This approach enhances the efficiency of ASO drug discovery by leveraging existing data effectively.