Artificial intelligence has markedly expanded the ability to predict protein structures, protein–ligand complexes, binding poses, and molecular properties, yet much of AI-assisted molecular modeling still treats binding as a predominantly static structural problem. This assumption is limiting because molecular action emerges from distributions of conformational states, transitions among those states, time-dependent association and dissociation, and biological environments that can alter target accessibility, engagement, and selectivity. This conceptual architecture article examines how these information layers should be incorporated without replacing interpretable molecular modeling with untestable complexity. The analysis distinguishes four related but non-equivalent representational problems: geometrical fit, conformational-state occupancy, binding kinetics, and cellular context. It argues that richer modeling is useful only when each added layer changes a testable observation, comparison, or drug-design decision and when validation is matched to the level of inference being made. Conformational ensembles can reveal alternative pockets and ligand-dependent states, but they require experimental or physics-informed constraints. Binding kinetics can distinguish compounds with similar equilibrium affinity, but residence time is not inherently advantageous. Cellular measurements can expose target engagement and polypharmacology that isolated complexes cannot represent, yet cellular engagement does not establish therapeutic efficacy. A dynamic molecular-modeling architecture should therefore preserve these layers as distinct sources of evidence while connecting them selectively through falsifiable cross-level relationships. The resulting approach reframes AI-assisted molecular modeling from  prediction of a best-fitting complex toward conditional  inference about molecular behavior across structure, time, and biological context.