Molecular dynamics simulations are well suited for studying molecular recognition. A key challenge is the derivation of meaningful descriptors from the raw MD coordinates.
ROBUST provides a set of tools to calculate physics based descriptors from molecular dynamics simulations.
Along with the transformers we provide a number of examples cases.
- python ≥ 3.6
- numpy
- pandas
- scipy
- scikit-learn
- voronota
- Schrodinger ≥ v.18.1
The Schrodinger Software Suite is required to process molecular dynamic simulations.
For examples, the descriptors have been precomputed thus the examples do not require Schrodinger.
A series of transformers to calculate and parse descriptors from molecular dynamics simulations. Transformers were developed for use with Schrodingers Protein-Ligand Database however, they can also be called from the command line or imported in python.
All transformers can be run from the commandline; For a list of arguments simply run:
$SCHRODINGER/run python transformer.py --help
All transformers can be imported into a python script or jupyter notebook. An example is shown in the examples/DHFR/calculate_descriptors.ipynb .
All transformers can be incorporated into a PLDB pipeline. For details on our in-house analysis pipeline contact Judy Huang.
Development of a predictive model for inhibitor affinity of drug resistant HIV-1 protease variants using pMD and ROBUST
Referece paper: Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning
Application of pMD and ROBUST to identify Trimethoprim resistant dihydrofolate reductase variants
Reference paper:Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning
Florian Leidner:
Judy Huang
Celia Schiffer: