Machine learning-guided design of artificial microRNAs for targeted gene silencing

Abstract

Artificial microRNAs (amiRNAs) offer a powerful strategy for targeted gene silencing, but their rational design is limited by complex sequence-structure-processing relationships and the lack of tools capable of optimizing efficacy and specificity. To address this need, we developed miRarchitect, a web-based platform that uses machine learning to support the customizable design of amiRNAs. miRarchitect integrates neural network-guided target-site selection, siRNA insert design, and scaffold choice, utilizing large-scale data from human primary microRNAs (pri-miRNAs) and next-generation sequencing. The platform generates molecules that closely resemble endogenous pri-miRNAs and includes comprehensive off-target analysis to enhance specificity. Experimental validation targeting TMPRSS2 and ACE-2 confirmed precise processing, robust knockdown, and high specificity of miRarchitect-designed amiRNAs. In comparative benchmarking, miRarchitect consistently produced functional amiRNAs, whereas only half of the top candidates generated by other tools showed measurable activity. miRarchitect is freely available at https://rnadrug.ichb.pl/mirarchitect and provides an intuitive interface with an automated workflow for generating, ranking, and selecting candidate amiRNAs for research and therapeutic applications.