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Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Leon Klein · Andrew Foong · Tor Fjelde · Bruno Mlodozeniec · Marc Brockschmidt · Sebastian Nowozin · Frank Noe · Ryota Tomioka

Wed Dec 13 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #106
in Poster Session 3 »
*Molecular dynamics* (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium properties, which requires sampling from an equilibrium distribution such as the Boltzmann distribution. However, many important processes, such as binding and folding, occur over timescales of milliseconds or beyond, and cannot be efficiently sampled with conventional MD.Furthermore, new MD simulations need to be performed for each molecular system studied.We present *Timewarp*, an enhanced sampling method which uses a normalising flow as a proposal distribution in a Markov chain Monte Carlo method targeting the Boltzmann distribution. The flow is trained offline on MD trajectories and learns to make large steps in time, simulating the molecular dynamics of $10^{5} - 10^{6} \textrm{fs}$.Crucially, Timewarp is *transferable* between molecular systems: once trained, we show that it generalises to unseen small peptides (2-4 amino acids) at all-atom resolution, exploring their metastable states and providing wall-clock acceleration of sampling compared to standard MD.Our method constitutes an important step towards general, transferable algorithms for accelerating MD.

Author Information

Leon Klein (Freie Universität Berlin)
Andrew Foong (University of Cambridge)

PhD student from Malaysia working on Bayesian deep learning at the Computational and Biological Learning lab at the Cambridge University Engineering Department.

Tor Fjelde (University of Cambridge)
Bruno Mlodozeniec (University of Cambridge)
Marc Brockschmidt (Google Brain)
Sebastian Nowozin (DeepMind)
Frank Noe (FU Berlin)
Ryota Tomioka (Microsoft Research AI4Science)

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