Toward Rational Design of Reaction-Based Fluorescent Probes: A Reaction-Centric Perspective on Machine Learning in Chemical Sensing

Machine learning (ML) is increasingly effective for predicting photophysical properties and accelerating the discovery of fluorescent molecules. Yet, its impact on reaction-based fluorescent probe design remains limited. We argue that this limitation is not fully explained by model accuracy alone but also reflects a structural mismatch between the dominant learning paradigm and the actual sensing object. Most current approaches are molecule-centric, learning properties of isolated molecular states, whereas reaction-based probes are evaluated through analyte-triggered transformations between chemically linked states (P → P′). The key observables are therefore relational and context-dependent, including Δλem, ΔΦ, response mode, dynamic range, and condition-dependent selectivity. When learning targets are defined only at the single-state level, these transformation-induced observables are learned only indirectly. Using an illustrative stress test on representative published probes, we show that seemingly acceptable end point predictions can still lead to incorrect response-level conclusions. We therefore propose a reaction-centric perspective in which chemically defined transformations, together with mechanistic and assay context, are treated as the primary supervision unit. Building on advances in fluorophore prediction, reaction representation learning, literature extraction, and constrained generative modeling, we outline a practical roadmap centered on transformation-aware data sets, response-level benchmarks, mechanism- and environment-aware models, and closed-loop validation. This perspective provides a more physically grounded and decision-relevant foundation for rational design of next-generation fluorescent probes.