From João M. Damas
Researchers have been looking beyond traditional Molecular Dynamics (MD) for a long time now. While many have been using many forms of biased MD (for example, metadynamics), we have alternatively proposed Adaptive Molecular Dynamics 1,2 as a way to unbiasedly enhance the exploration of the phase space through on-the-fly learning of the explored space.
Since the beginning, HTMD 2 has provided an Adaptive MD protocol, which automatically manages all the process of analyzing the explored space and spawning of new MD simulations. An explanation of how Adaptive MD works and examples on how to use it can be found in the cited papers and in the online documentation of HTMD. One of the limitations of Adaptive MD has been the access to computing resources to take advantage of its high-throughput nature.
However, with the advent of cloud computing, resources are more readily available in an elastic manner that suits different needs. We have developed AceCloud 3 to run MD simulations on AWS and, together with HTMD, it can be used to run Adaptive MD simulations on the cloud. By using a system example for the generators (initial simulations), one can use the following script to quickly run an Adaptive MD job using HTMD and AceCloud:
1. S. Doerr and G. De Fabritiis. On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations. Journal of Chemical Theory and Computation, 10(5):2064–2069, 2014.
2. S. Doerr, M. J. Harvey, Frank Noé, and G. De Fabritiis. HTMD: High-throughput molecular dynamics for molecular discovery. Journal of Chemical Theory and Computation, 12(4):1845–1852, 2016.
3. M. J. Harvey and G. De Fabritiis. AceCloud: Molecular dynamics simulations in the cloud. Journal of Chemical Information and Modeling, 55(5):909–914,2015.