Molecular dynamics simulation has become capable of resolving conformational states, ligand-binding pathways, kinetic events, allosteric coupling and binding thermodynamics at resolutions that increasingly intersect with medicinal-chemistry decisions. Yet greater trajectory length, algorithmic sophistication or apparent molecular detail does not by itself establish that a simulated observation is sufficiently reliable, mechanistically interpretable or transferable to support compound selection. This Critical Review examines the inferential steps separating molecular trajectories from therapeutic decisions. Particular attention is given to conformational-state discovery, binding and unbinding mechanisms, allostery, free-energy estimation, membrane and solvent representation, and integration with structural and biophysical experiments. Across these domains, three recurring limitations emerge: simulated observables depend on representation and sampling choices; different computational outputs answer non-equivalent pharmacological questions; and agreement within a computational workflow is not equivalent to external validation or downstream therapeutic relevance. The review therefore develops a proposed evidence architecture in which trajectory-derived states, kinetic quantities and thermodynamic estimates advance toward compound decisions only through question-specific validation gates. Rather than treating molecular dynamics as a single predictive technology, this framework positions it as a family of conditional inference tools whose usefulness depends on matching the simulated observable, uncertainty structure and validation design to the medicinal-chemistry decision being made.
IJPRAS
