From affinity to kinetics: SPR as the analytical backbone of AI-driven drug discovery

AI-driven drug discovery can now propose candidates faster than experimental triage can validate. However, many models still rely on static affinity or potency endpoints such as K D , IC 50 , or pEC50. This reliance creates a kinetic blind spot, as pharmacological durability often depends on dissociation kinetics and residence time. Surface plasmon resonance (SPR) can transform computational binding hypotheses into reusable, time-resolved learning signals. Here, we define the affinity-to-kinetics gap, link residence time to functional persistence, and analyze two AI-enabled workflows. In PRMT5 library compression, screening reduced 460,000 compounds to a single confirmed SPR binder (K D = 21.5 μM); in TNIK generative design, INS018_055 showed potent SPR affinity (K D = 4.32 nM). Across these cases, SPR functions as a quality-control filter and experimental binding validator rather than a terminal confirmation assay. We identify five requirements for AI-ready SPR data: raw sensorgram retention, consistent fitting, explicit QC labels, experimental provenance, and reusable data schemas. Treating sensorgrams, fit diagnostics, and QC labels as data infrastructure facilitates AI–SPR co-learning, providing a practical route to kinetic-aware, auditable, and reusable decision systems.