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However, the time to design, produce, and screen high-performance RNA sensors remains a critical challenge. The fundamental rules governing RNA\u2013RNA interactions\u2014specifically the structure-function relationships that determine sensor performance\u2014remain poorly understood. Here, we present a method enabling versatile in-silico RNA-targeting analysis (VISTA), a machine learning-guided framework for the rapid design of RNA sensors. VISTA integrates biophysical modeling of both sensor and target RNAs with a partial least squares discriminant analysis machine learning framework. Using high-throughput experimental measurements with sequence-structure feature extraction to train predictive models, we capture the key determinants of RNA sensor performance. By using toehold switches as a model RNA sensor, we find that Toehold-VISTA successfully designs RNA sensors with improved performance against SARS-CoV-2 RNA. These findings establish a broadly applicable, target-aware design strategy for accelerating RNA sensor engineering across biotechnology and diagnostic applications.<\/jats:p>","DOI":"10.1093\/nar\/gkag097","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:43:51Z","timestamp":1769604231000},"source":"Crossref","is-referenced-by-count":5,"title":["Toehold-VISTA: a machine learning approach to decipher programmable RNA sensor-target interactions"],"prefix":"10.1093","volume":"54","author":[{"given":"James M","family":"Robson","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Boston University , Boston , MA 02215 ,","place":["United States"]},{"name":"Biological Design Center, Boston University , Boston , MA 02215 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2058-1204","authenticated-orcid":false,"given":"Alexander 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