This runs of x86 ONLY for the moment.

Build the conda enviroment using the yml file or just run stuff and figure out how to make it work (it's not rocket science ;)

You can run end to end with

mpiexec -np 8 python runner.py input/john_smiles_kinasei.smi

You can alter how those runs peform using the policy file.

In the future, this workflow will be moved to Radical/ENTK, but for now this works.

Python scripts to generate an MD-ready model from smiles strings and run simple free energy calculations

There are two command line executables.

• Takes a smiles and pdb, generates conformers, docks, and scores the ligand.
• The output is a set of simulation-ready structures (ligand, apo, and complex) and a file called metrics.csv, which has the docking score and associated uncertainties. Most uncertainties are 0 right now. There are other auxiliary files that are saved in the output directory.
• Dependencies: OpenEye, Ambertools, Ambermini, docopt

• Parameterizes the ligand using either OpenEye or Amber.

• This command should only be run after docking.py.
• Input is a path to the directory where the input coordinates and parameters are saved. This should be the output path from the docking.py command.
• Also takes the nanosecond length of the simulation. 0 corresponds to an energy minimization.
• Output adds to the metrics.csv file
• Dependencies: OpenMM, numpy, pymbar, docopt

• Uses an alchemical method to calculate the absolute binding free energy of a ligand.
• User enters the number of lambda windows and the length of simulation at each window.
• The windows run in series but this could be parallelized.
• Applying constraints should increase the convergence of the system

To get the four metrics for a smiles, including a 5 ns simulation, pick a smiles and call

python docking.py -s $SMILES -i "input/receptor.oeb" -o "test"
python param.py -i "test"
python mmgbsa.py -p "test" -n 0
python mmgbsa.py -p "test" -n 5
python alchem.py -i "test" -l 6 -n 5