Enhanced Drift Sampling: Rare-Event Free-Energy Estimation with Tilted Drift Targets

Rare-state free-energy estimation remains exponentially expensive under direct equilibrium sampling, even when generative models provide approximately independent configurations. Enhanced Diffusion Sampling addresses this problem by steering pretrained diffusion models toward biased ensembles, but requires multistep reverse-SDE integration and Feynman--Kac correction weights. We introduce \emph{Enhanced Drift Sampling} (\method{}), which transfers biasing and reweighting to one-step drifting generators by tilting a reference bank with $w\propto e^{-b}$. We develop steering, adaptation, and multi-step refinement variants for umbrella, metadynamics, and linear biases. Exact thermodynamic reweighting requires exact biased windows; approximate drifted windows additionally require adequate bank coverage and proposal overlap. On a double well, an exact-tilt oracle reduces generated-sample requirements, whereas the tested finite-step protocol fails from a collapsed initialization. Umbrella variants reconstruct toy and alanine potentials of mean force, and tilted BioEmu ensembles reduce finite-sample error for the emulator's own rare-state probability, but do not remove its discrepancy from molecular-dynamics references.