Binding Kinetics in Natural Product Pharmacology and the Importance of Association, Dissociation, Residence Time, and Target Engagement for Lead Selection

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Natural-product lead selection remains strongly influenced by equilibrium potency and affinity, yet these descriptors do not determine how rapidly target occupancy develops, how persistently a ligand remains bound, or whether binding survives the transition from purified systems to cellular and in vivo environments. This state-of-the-art review examines association rate, dissociation rate, residence time, kinetic selectivity, and target engagement as complementary dimensions of natural-product pharmacology. The evidence indicates that association can be shaped by desolvation, access routes, and intermediate binding states, whereas dissociation can reflect local interactions, conformational organization, and escape barriers. Direct natural-product kinetic evidence remains limited, but macrocyclic Gαq inhibitors demonstrate that closely related natural-product-derived structures can display distinct residence behavior. Target-engagement, thermal-profiling, chemical-proteomic, biophysical, live-cell, and computational approaches provide additional information but measure different observables and should not be treated as interchangeable kinetic evidence. The review proposes a kinetics-aware lead-selection logic in which temporal binding is considered only when it resolves a pharmacologically relevant uncertainty and remains compatible with selectivity, exposure, biological context, and functional duration. Longer residence is therefore not treated as intrinsically advantageous. Major limitations include sparse matched natural-product kinetic datasets, assay dependence, imperfect transfer between purified and biological systems, and uncertainty in computational rate estimation. Progress will require orthogonal measurement, explicit kinetic models, cross-target profiling, and prospective tests of whether kinetic information improves decisions beyond conventional potency- and exposure-based prioritization.