Accurately modeling RNase H-mediated antisense oligonucleotide efficacy
Year: 2026
Type: article
Abstract: Antisense oligonucleotides (ASOs) are a powerful class of drugs with the potential to treat a wide range of human diseases. However, the prediction of ASO efficacy remains challenging, as large-scale and costly experimental screens are typically required to identify optimal candidates for a specific therapeutic target. To address this challenge, we compiled ASO Atlas, a database comprising 188,521 RNase H-mediated ASO sequences targeting 334 unique genes with corresponding knockdown efficacy measurements extracted from published patents. Using ASO Atlas, we trained OligoAI, a deep learning model capable of jointly modelling RNA target context, ASO sequence, sugar and backbone chemistries, and dosage to predict in vitro efficacy. We experimentally validated OligoAI by targeting KCNT2, achieving a 5.72-fold reduction in screening effort compared to random selection. ASO Atlas provides the first systematic resource to rigorously evaluate hypotheses regarding key parameters in ASO design, including sequence composition, chemical modifications, and target region selection. Both ASO Atlas and OligoAI have been made freely accessible through an online web-tool with the aim of facilitating the accelerated optimisation of ASO design.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,900
Awards: TransNAT: Transforming delivery, safety and efficacy of nucleic acid therapeutics: from intracellular uptake to targeting brain and muscle., G1458145172, MRC Centre of Research Excellence in Therapeutic Genomics, Targeting UTR regulation as a gene-level therapeutic strategy for rare disease, Genomic Medicine and Statistics, Establishing a National Platform for the Development of Nucleic Acid Therapy for Rare Disease
OpenAlex ID: W4415713988