DBMD
Deep Boosted Molecular Dynamics (DBMD)
In DBMD, probabilistic Bayesian neural network models are implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD has been demonstrated on a wide range of model systems, including alanine dipeptide and the fast-folding protein and RNA structures. Based on Deep Learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations.
GitHub
An example听test听folder that contains the input topology and coordinate file for the folding simulation of the hairpin RNA with GCAA tetraloop is included in this repository. The following听python听modules must be installed to perform a DBMD simulation:
- 听(panda is also my favorite friend)
It is recommended to install these modules and run DBMD in OpenMM in an .
An example input file for an DBMD simulation can be found in听. A run script can be found in听. To run the听听folder, simply install all the necessary听python听modules and run the following commands:
sh runSimulation
Explanations for all parameters in the example input file can be found at the reference below. It is recommended to set up and run DBMD in OpenMM on NVIDIA GPUs to achieve the best possible speeds.
Reference
Do, H.N. and Miao, Y. (2023) Deep Boosted Molecular Dynamics (DBMD): 听The Journal of Physical Chemistry Letters, 14, 21, 4970-4982.