def worker(df,
path_root,
dbase_name,
target_name,
docking_only=False,
receptor_file=None):
struct = "input/"
docker, recept = interface_functions.get_receptr(
receptor_file=receptor_file)
for pos in range(int(rank), df.shape[0], int(world_size)):
pos, smiles, name = pos, df.iloc[pos, 0], df.iloc[pos, 1]
path = path_root + str(pos) + "/"
try:
score, res = interface_functions.RunDocking_A(
smiles,
struct,
path,
dbase_name,
target_name,
dock_obj=docker,
write=False,
recept=recept,
receptor_file=receptor_file,
name=name,
docking_only=True)
interface_functions.ParameterizeOE(path)
mscore = interface_functions.RunMinimization_(path,
path,
write=True,
gpu=True)
print(smiles, score, mscore)
if mscore < -500:
escore = interface_functions.RunMMGBSA_(path,
path,
gpu=True,
niter=5000) # 5n
print(smiles, score, mscore, escore)
except KeyboardInterrupt:
exit()
except subprocess.CalledProcessError as e:
print("Error rank", rank, e)
except IndexError as e:
print("Error rank", rank, e)
except RuntimeError as e:
print("Error rank", rank, e)
with open(path + "done.txt", 'w') as f:
f.write("t")
def worker(df):
struct = "input/"
start_pos = rank * 100
for pos in range(start_pos, 100 + start_pos):
path = "test" + str(pos) + "/"
smiles = df.iloc[pos, 0]
# pipline
comm.send([smiles], dest=0, tag=11)
r = comm.recv(source=0, tag=11)
print("Rank", rank, "should I run docking on", smiles, "?",
"\t my model says", bool(r))
# pipeline
if r:
print("Rank", rank, "running docking...")
score = interface_functions.RunDocking_(smiles, struct, path)
comm.send([smiles, score], dest=0, tag=11)
r = comm.recv(source=0, tag=11)
print("Rank", rank,
"should I run minimize, given the docking score", score, "?",
"\t my model says", bool(r))
# pipeline
if r:
print("Rank", rank, "running param and mini")
interface_functions.ParameterizeOE(path)
mscore = interface_functions.RunMinimization_(path, path)
comm.send([smiles, score, mscore], dest=0, tag=11)
r = comm.recv(source=0, tag=11)
print(
"Rank", rank,
"should I run mmgbsa for 1 ns given a energy minmization result of",
mscore, "?\t my model says", bool(r))
if r:
print("Rank", rank, "running simulation")
escore = interface_functions.RunMMGBSA_(path, path)
print("Rank", rank, "ran simulation and got", escore)
def worker2(df,
path_root,
dbase_name,
target_name,
docking_only=False,
receptor_file=None):
size = comm.Get_size()
struct = "input/"
#docker,recept = interface_functions.get_receptr(receptor_file=receptor_file)
for pos in range(rank, len(df), size):
pos = pos
path = df[pos]
try:
with open(path + "/metrics.csv", 'w') as f:
f.write("path\n")
f.write(path + "\n")
interface_functions.ParameterizeOE(path)
mscore = interface_functions.RunMinimization_(path,
path,
write=True,
gpu=True)
print("FINAL", mscore)
except KeyboardInterrupt:
exit()
except subprocess.CalledProcessError as e:
print("Error rank", rank, e)
except IndexError as e:
print("Error rank", rank, e)
except RuntimeError as e:
print("Error rank", rank, e)
with open(path + "done.txt", 'w') as f:
f.write("t")
"""INSPIRE Parameterization - Parameterize a docked ligand-protein system
Usage:
param.py -i=<PATH> [-a]
param.py (-h | --help)
param.py --version
Options:
-h --help Show this screen.
--version Show version.
-i=<PATH> Path to directory containing a PDBs of the ligand, receptor, and complex.
-a Parameterize the docked system with AMBER [default uses OpenEye].
"""
from docopt import docopt
from impress_md import interface_functions
import timeit
start = timeit.default_timer()
if __name__ == '__main__':
arguments = docopt(__doc__, version='INSPIRE Param 0.0.1')
path = arguments['-i']
if arguments['-a']:
interface_functions.ParameterizeAMBER(path)
else:
interface_functions.ParameterizeOE(path)
with open(f'{path}/param.log', "w+") as logf:
logf.write("Param time (sec): {}\n".format(timeit.default_timer() - start))