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Synthetic molecular dynamics (synMD) trajectories from learned generativemodels have been proposed as a useful addition to the biomolecular simulationtoolbox. The computational expense of explicitly integrating the equations ofmotion in molecular dynamics currently is a severe limit on the number andlength of trajectories which can be generated for complex systems. Approximate,but more computationally efficient, generative models can be used in place ofexplicit integration of the equations of motion, and can produce meaningfultrajectories at greatly reduced computational cost. Here, we demonstrate a verysimple synMD approach using a fine-grained Markov state model (MSM) with statesmapped to specific atomistic configurations, which provides an exactly solvablereference. We anticipate this simple approach will enable rapid, effectivetesting of enhanced sampling algorithms in highly non-trivial models for bothequilibrium and non-equilibrium problems. We demonstrate the use of a MSM togenerate atomistic synMD trajectories for the fast-folding miniproteinTrp-cage, at a rate of over 200 milliseconds per day on a standard workstation.We employ a non-standard clustering for MSM generation that appears to betterpreserve kinetic properties at shorter lag times than a conventional MSM. Wealso show a parallelizable workflow that backmaps discrete synMD trajectoriesto full-coordinate representations at dynamic resolution for efficientanalysis.

Simple synthetic molecular dynamics for efficient trajectory generation