Alchemical Move: Chasing Rabbits, Trusting Turtles

Many molecular processes, including membrane permeation or protein-ligand unbinding, occur through spontaneous transitions between stable states separated by activation barriers. Such events are rare, when these barriers are high and occurring on timescales far longer than those accessible to standard molecular dynamics (MD) simulations. This makes the direct application of MD inefficient for studying rare transitions. Path sampling techniques, such as Transition Interface Sampling (TIS), address this challenge by focusing on reactive trajectories rather than full equilibrium simulations. TIS employs Monte Carlo moves, such as the shooting move, to generate new trajectories that satisfy detailed balance, ensuring each elementary process is in equilibrium with its reverse process. TIS can be used to estimate transition rate constants at a reasonable computational cost. However, TIS can become inefficient in multi-channel systems, where important alternative pathways may remain undersampled. In such cases, channels are often separated by high free-energy barriers in phase space, so small perturbations rarely allow a trajectory to hop from one channel to another. In this work, we introduce a novel approach, termed the alchemical move, designed to overcome this limitation. The method alternates between two forcefields: a low-level model that rapidly explores phase space and a high-level model that provides accurate dynamics. By swapping forcefield between these systems, the approach enhances the discovery of distinct transition pathways while reducing computational cost. The workflow comprises four steps: phase-point selection, forcefield swapping, acceptance evaluation, and path generation. Preliminary simulations show that alchemical moves produce crossing probabilities and path lengths consistent with those obtained using only the high-level forcefield as the reference. Ongoing work will extend validation to one- and two-dimensional model systems and to all-atom biomolecular simulations.