Equilibrium affinity remains a central descriptor of molecular recognition, yet it cannot by itself explain how rapidly a ligand reaches its target, which conformational states enable binding, how the complex reorganizes after encounter, which pathways control dissociation, or how long target engagement persists under changing biological conditions. This limitation is increasingly consequential for computational pharmaceutical science because models trained primarily on affinity labels may learn endpoint compatibility while overlooking the temporal and conformational behavior that connects molecular structure to pharmacological action. This Original Molecular-Modeling Perspective Article develops a proposed reframing of learned molecular recognition around a Dynamic Recognition Profile. The profile retains equilibrium affinity as one evidentiary component while separately representing conformational ensembles, recognition mechanisms, association and dissociation pathways, kinetic rate parameters, residence time, perturbation responses, pharmacological context, uncertainty, and provenance. The synthesis distinguishes conformational selection from induced fit without treating them as mutually exclusive, separates thermodynamic stability from kinetic barriers, and argues that trajectory, assay, structural, and perturbational evidence should remain distinguishable when incorporated into learned models. It further proposes that evaluation should move beyond one predictive error measure toward tests of ensemble fidelity, pathway reproducibility, kinetic calibration, perturbation sensitivity, transfer across chemical and target domains, and bounded decision usefulness. The contribution is conceptual rather than empirically validated. It does not establish that kinetic optimization is universally advantageous, that simulated pathways are mechanistically definitive, or that residence time alone determines efficacy, selectivity, or dosing. Reframing recognition as conditional behavior may nevertheless support more scientifically interpretable models and more disciplined translation from molecular prediction to pharmaceutical reasoning.