Computational pharmacology has become increasingly effective at predicting bound poses, relative affinities, and equilibrium free-energy differences, yet these endpoint quantities describe only part of molecular recognition. A ligand can reach the same apparent bound state through different association routes, populate distinct encounter or metastable complexes, cross different rate-limiting barriers, and leave through dissociation pathways that are not simply the reverse of association. These features matter because pharmacological engagement is inherently temporal. Two compounds with similar equilibrium stability can therefore differ substantially in association rate, dissociation rate, residence time, sensitivity to conformational gating, and susceptibility to mutation-dependent changes in kinetic behavior. This Mechanistic Perspective argues that next-generation in silico pharmacology should treat kinetic pathways as an additional level of molecular description rather than as a secondary annotation to endpoint stability. The proposed pathway-centred view separates trajectory generation, pathway identification, barrier interpretation, rate estimation, and experimental validation so that mechanistic plausibility is not confused with quantitatively validated kinetics. Evidence from enhanced sampling, milestoning, weighted-ensemble methods, accelerated molecular dynamics, pathway-search approaches, and data-driven reaction-coordinate learning shows that route-resolved kinetic modeling is increasingly feasible, but predictive performance remains strongly system- and method-dependent. The key opportunity is therefore not to replace thermodynamic calculations, but to integrate equilibrium and kinetic representations while preserving uncertainty, competing mechanisms, and validation boundaries. Such integration could make molecular simulation more informative for kinetic selectivity, resistance analysis, and medicinal-chemistry optimization without overstating current prospective predictive capability.
IJPRAS
