ArticleNature communications2026
Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Chemical imaging probes enable the visualization of dynamic biology; however, engineering high sensitivity in live cells remains challenging. Here we present Sensight, a quantitative multivariate framework that integrates key photophysical and physicochemical descriptors to predict and optimize probe performance. Using a structurally diverse library, we identify five dominant parameters, define their optimal ranges, and unify them into a radar map representation with strong predictive power and intuitive visualization. This framework extends the structure-activity relationship analysis into imaging sensitivity, capturing complex structure-function relationships that shape probe behavior in live cells. Guided by Sensight, we design G
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