DeepDriveMD

DeepDriveMD, Deep learning-driven Adaptive Molecular Simulations for Protein Folding, is a toolkit developed by Brookhaven National Laboratobry (BNL)/RADICAL Laboratory at Rutgers, in collaboration with Argonne National Laboratory.

This is a main repository of DeepDriveMD used in production runs. You can find the latest publication here:

H. Lee, M. Turilli, S. Jha, D. Bhowmik, H. Ma and A. Ramanathan, "DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding," 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), Denver, CO, USA, 2019, pp. 12-19, doi: 10.1109/DLS49591.2019.00007. https://ieeexplore.ieee.org/abstract/document/8945122

Adaptive Execution Development

This is an ongoing develeopment branch you can find the source code under src and data

Asynchronous Execution Development published at JSSPP

JSSPP folder holds source code and figures used in the paper

COVID-19 Development (Relocated under archive tag)

There is ongoing activity in associcated with the Covid-19 project. https://github.com/2019-ncovgroup/DrugWorkflows/tree/devel/workflow-2

Dependency

• OpenMM
  • swig 3+
  • numpy
  • cython
• tensorflow-gpu
  • keras
• MDAnalysis
  • scipy
  • numpy 1.16+
• scikit-learn
• Parmed
• pytables
• h5py

Systems

• fs-peptide/vhp: https://github.com/radical-collaboration/DeepDriveMD/tree/master/microscope/experiments
• ntl9: https://github.com/radical-collaboration/DeepDriveMD/tree/master/src/mdrun/ntl9

Experiment

• OLCF Summit Performance Analysis

RADICAL-Cybertools (RCT)

• EnTK documentation
• Use-case description