Fluorescence resonance energy transfer as a molecular ruler: Quantitative and semi-quantitative measurement approaches and emerging AI-enabled tools

Fluorescence Resonance Energy Transfer (FRET) is an optical tool for probing nanoscale molecular interactions and dynamics. Although widely used, reliable quantitative and semi-quantitative estimation of FRET efficiency remains challenging because different measurement modalities rely on distinct photophysical assumptions, correction steps, and analytical models. In this review, we summarize major strategies for FRET efficiency estimation, including intensity-based approaches (e.g., sensitized emission and acceptor photobleaching), lifetime-based approaches (FLIM-FRET and time-resolved FRET), and representative assay formats such as FRET two-hybrid analysis, along with spectral, single-molecule, two-photon, and super-resolution variants. For each category, we compare the underlying physical principles, typical error sources and corrections, representative modeling strategies, and practical applicability across experimental constraints. We further synthesize recent advances in optical imaging and computational modeling and propose a practical workflow for integrating artificial intelligence into FRET quantification, covering data quality control, automated region-of-interest selection, calibration and parameter inference, and validation aimed at reproducible results. Finally, we discuss emerging opportunities enabled by on-instrument analytics (Edge AI and TinyML) for low-latency acquisition and analysis, and outline future directions toward high-throughput, cross-scale imaging and standardized quantitative workflows.