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Project: ADMIRRAL
Description: Performs “dynamic” sampling where the input distribution can change over time and the sampling adapts itself to the new distribution.
DESCRIPTION:

Performs “dynamic” sampling where the input distribution can change over time and the sampling adapts itself to the new distribution.

IMPACT: Enables machine learning-based adaptive multiscale simulations for cancer biology.
INPUT DATA TYPE: NumPy Arrays
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
https://github.com/CBIIT/NCI-DOE-Collab-Pilot2-DynIm
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Project: ADMIRRAL
Description: Supports very large and multiscale simulations of molecular dynamic interactions between proteins (or their domains) with each other or with cell membranes. 
DESCRIPTION:

Supports very large and multiscale simulations of molecular dynamic interactions between proteins (or their domains) with each other or with cell membranes. 

IMPACT: Produces data like KRas4B Campaign 1 Trajectory data for use in models.
PRIMARY PUBLICATION: Machine Learning–driven Multiscale Modeling Reveals Lipid-dependent Dynamics of RAS Signaling Proteins
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
https://github.com/CBIIT/NCI-DOE-Collab-Pilot2-MuMMI
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