Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools

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 G₃, a superoxide probe with exceptional sensitivity for detecting early oxidative events. The framework's generality is further demonstrated across distinct systems, including tetrazine-bicyclononyne bioorthogonal chemistry and formaldehyde sensing. Together, these findings establish Sensight as a predictive and generalizable strategy for high-performance probe design, with broad implications for sensing, imaging, and even theranostics.