Seokjun Kang,Daehwan Lee,Gyeongjo Hwang,Kiwon Lee,Mingeun Kang
{"title":"ASOptimizer: optimizing chemical diversity of antisense oligonucleotides through deep learning.","authors":"Seokjun Kang,Daehwan Lee,Gyeongjo Hwang,Kiwon Lee,Mingeun Kang","doi":"10.1093/nar/gkaf392","DOIUrl":null,"url":null,"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/.","PeriodicalId":19471,"journal":{"name":"Nucleic Acids Research","volume":"76 1","pages":""},"PeriodicalIF":16.6000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nucleic Acids Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/nar/gkaf392","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
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/.
期刊介绍:
Nucleic Acids Research (NAR) is a scientific journal that publishes research on various aspects of nucleic acids and proteins involved in nucleic acid metabolism and interactions. It covers areas such as chemistry and synthetic biology, computational biology, gene regulation, chromatin and epigenetics, genome integrity, repair and replication, genomics, molecular biology, nucleic acid enzymes, RNA, and structural biology. The journal also includes a Survey and Summary section for brief reviews. Additionally, each year, the first issue is dedicated to biological databases, and an issue in July focuses on web-based software resources for the biological community. Nucleic Acids Research is indexed by several services including Abstracts on Hygiene and Communicable Diseases, Animal Breeding Abstracts, Agricultural Engineering Abstracts, Agbiotech News and Information, BIOSIS Previews, CAB Abstracts, and EMBASE.