Work

ASOptimizer: optimizing chemical diversity of antisense oligonucleotides through deep learning

Year: 2025

Type: article

Abstract: Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene expression. However, designing ASOs manually is resource-intensive and time-consuming. To address this, we introduce a user-friendly web server for ASOptimizer, a deep learning-based computational framework for optimizing ASO sequences and chemical modifications. Given a user-provided ASO sequence, the web server systematically explores modification sites within the nucleic acid and returns a ranked list of promising modification patterns. With an intuitive interface requiring no expertise in deep learning tools, the platform makes ASOptimizer easily accessible to the broader research community. The web server is freely available at https://asoptimizer.s-core.ai/.

Source: Nucleic Acids Research

DOI: 10.1093/nar/gkaf392

Authors: Seokjun Kang, Daehwan Lee, Gyeongjo Hwang, Kiwon Lee, Mingeun Kang

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: RNA Interference and Gene Delivery

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,992

Funders: Maryland Society of Surveyors, National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea