def main():
global WRITER, THRESHOLD
global PDB_PATH
parser = argparse.ArgumentParser(
description='SMoG2016 - Docking calculation.')
utils.add_default_io_args(parser)
parser.add_argument(
'--no-gzip',
action='store_true',
help='Do not compress the output (STDOUT is never compressed')
parser.add_argument('-pdb', '--pdb_file', help="PDB file for scoring")
parser.add_argument('-t',
'--threshold',
help="The maximum score to allow",
default=None)
parser.add_argument('--thin', action='store_true', help='Thin output mode')
args = parser.parse_args()
utils.log("SMoG2016 Args: ", args)
smog_path = "/usr/local/SMoG2016_Rev1/"
if args.threshold:
THRESHOLD = float(args.threshold)
else:
THRESHOLD = None
PDB_PATH = "/tmp/pdb_file.pdb"
# Now copy it to prot_pdb.pdb -> silly SMOG bug requires underscore in the filename!
shutil.copy(args.pdb_file, PDB_PATH)
# Open up the input file
input, suppl = utils.default_open_input(args.input, args.informat)
# Open the ouput file
output, WRITER, output_base = utils.default_open_output(
args.output, "SMoG2016", args.outformat, compress=not args.no_gzip)
# Cd to the route of the action
# TODO - can this be done without changing dir? It gives problems in finding the input files and in writing the metrics
cwd = os.getcwd()
os.chdir(smog_path)
# Iterate over the molecules
# TODO - restore parallel processing, but need to ensure the order of molecules is preserved
pool = ThreadPool(1)
pool.map(run_dock, suppl)
# Close the file
WRITER.close()
os.chdir(cwd)
if args.meta:
utils.write_metrics(
output_base, {
'__InputCount__': COUNTER,
'__OutputCount__': SUCCESS,
'SMoG2016': COUNTER
})
utils.log("SMoG2016 complete")
def main():
global PDB_PATH, WRITER, THRESHOLD
parser = argparse.ArgumentParser(
description='SMoG2016 - Docking calculation.')
utils.add_default_io_args(parser)
parser.add_argument(
'--no-gzip',
action='store_true',
help='Do not compress the output (STDOUT is never compressed')
parser.add_argument('-pdb', '--pdb_file', help="PDB file for scoring")
parser.add_argument('-t',
'--threshold',
type=float,
help="The maximum score to allow",
default=None)
parser.add_argument(
'--threads',
type=int,
help="Number of threads to used. Default is the number of cores",
default=None)
parser.add_argument('--thin', action='store_true', help='Thin output mode')
args = parser.parse_args()
utils.log("PLI Args: ", args)
# Open up the input file
input, suppl = utils.default_open_input(args.input, args.informat)
# Open the ouput file
output, WRITER, output_base = utils.default_open_output(
args.output,
"plip",
args.outformat,
compress=not args.no_gzip,
thinOutput=args.thin)
PDB_PATH = args.pdb_file
if args.threshold:
THRESHOLD = args.threshold
# Iterate over the molecules
# WARNING - if using parallel processing the order of molecules is not preserved. Set args.threads to 1 to ensure this.
pool = ThreadPool(args.threads if args.
threads is not None else multiprocessing.cpu_count())
pool.map(run_dock, suppl)
pool.close()
pool.join()
# Close the file
WRITER.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': COUNTER,
'__OutputCount__': SUCCESS,
'PLI': COUNTER
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit Sdf2Json')
parser.add_argument('-i', '--input', help="Input SD file, if not defined the STDIN is used")
parser.add_argument('-o', '--output', help="Base name for output json file (no extension). If not defined then SDTOUT is used for the structures and output is used as base name of the other files.")
parser.add_argument('--exclude', help="Optional list of fields (comma separated) to exclude from the output.")
args = parser.parse_args()
utils.log("Screen Args: ", args)
if args.input:
if args.input.lower().endswith(".sdf"):
base = args.input[:-4]
elif args.input.lower().endswith(".sdf.gz"):
base = args.input[:-7]
else:
base = "json"
utils.log("Base:", base)
input,output,suppl,writer,output_base = utils.default_open_input_output(args.input, "sdf", args.output, base, "json")
if args.exclude:
excludes = args.exclude.split(",")
utils.log("Excluding", excludes)
else:
excludes = None
i=0
count = 0
for mol in suppl:
i +=1
if mol is None: continue
if excludes:
for exclude in excludes:
if mol.HasProp(exclude): mol.ClearProp(exclude)
writer.write(mol)
count += 1
utils.log("Converted", count, " molecules")
writer.flush()
writer.close()
input.close()
output.close()
utils.write_metrics(output_base, {'__InputCount__':i, '__OutputCount__':count, 'RDKitSdf2Json':count})
return count
def split(input, informat, fieldName, outputBase, writeMetrics):
"""Splits the input into separate files. The name of each file and the file the each record is written to
is determined by the fieldName parameter
"""
input, suppl = utils.default_open_input(input, informat)
i = 0
written = 0
writers = {}
outputs = []
filenames = []
for mol in suppl:
i += 1
if mol is None: continue
if not mol.HasProp(fieldName):
utils.log("Skipping molecule", i, "- did not contain field",
fieldName)
continue
value = mol.GetProp(fieldName)
if value:
s = str(value)
if writers.has_key(s):
writer = writers[s]
else:
name = outputBase + s
output, writer = utils.default_open_output_sdf(
name, outputBase, False, False)
filenames.append(name + '.sdf')
outputs.append(output)
writers[s] = writer
writer.write(mol)
written += 1
utils.log("Generated", len(writers), "outputs from", i, "records")
input.close()
for k in writers:
writers[k].close()
for o in outputs:
o.close()
if writeMetrics:
utils.write_metrics(outputBase, {
'__InputCount__': i,
'__OutputCount__': written,
'Splitter': i
})
return filenames
def main():
parser = argparse.ArgumentParser(description='Open3DAlign with RDKit')
parser.add_argument('query', help='query molfile')
parser.add_argument(
'--qmolidx',
help="Query molecule index in SD file if not the first",
type=int,
default=1)
parser.add_argument(
'-t',
'--threshold',
type=float,
help='score cuttoff relative to alignment of query to itself')
parser.add_argument(
'-n',
'--num',
default=0,
type=int,
help=
'number of conformers to generate, if None then input structures are assumed to already be 3D'
)
parser.add_argument('-a',
'--attempts',
default=0,
type=int,
help='number of attempts to generate conformers')
parser.add_argument('-r',
'--rmsd',
type=float,
default=1.0,
help='prune RMSD threshold for excluding conformers')
parser.add_argument(
'-e',
'--emin',
type=int,
default=0,
help=
'energy minimisation iterations for generated confomers (default of 0 means none)'
)
utils.add_default_io_args(parser)
args = parser.parse_args()
utils.log("o3dAlign Args: ", args)
qmol = utils.read_single_molecule(args.query, index=args.qmolidx)
qmol = Chem.RemoveHs(qmol)
qmol2 = Chem.Mol(qmol)
source = "conformers.py"
datasetMetaProps = {
"source": source,
"description": "Open3DAlign using RDKit " + rdBase.rdkitVersion
}
clsMappings = {"O3DAScore": "java.lang.Float"}
fieldMetaProps = [{
"fieldName": "O3DAScore",
"values": {
"source": source,
"description": "Open3DAlign alignment score"
}
}]
if args.num > 0:
# we generate the conformers so will add energy info
clsMappings["EnergyDelta"] = "java.lang.Float"
clsMappings["EnergyAbs"] = "java.lang.Float"
fieldMetaProps.append({
"fieldName": "EnergyDelta",
"values": {
"source": source,
"description": "Energy difference to lowest energy conformer"
}
})
fieldMetaProps.append({
"fieldName": "EnergyAbs",
"values": {
"source": source,
"description": "Absolute energy"
}
})
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input,
args.informat,
args.output,
'o3dAlign',
args.outformat,
valueClassMappings=clsMappings,
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
pyO3A = rdMolAlign.GetO3A(qmol2, qmol)
perfect_align = pyO3A.Align()
perfect_score = pyO3A.Score()
utils.log('Perfect score:', perfect_align, perfect_score,
Chem.MolToSmiles(qmol, isomericSmiles=True), qmol.GetNumAtoms())
i = 0
count = 0
total = 0
for mol in suppl:
if mol is None: continue
if args.num > 0:
mol.RemoveAllConformers()
conformerProps, minEnergy = conformers.process_mol_conformers(
mol, i, args.num, args.attempts, args.rmsd, None, None, 0)
mol = Chem.RemoveHs(mol)
count += doO3Dalign(i,
mol,
qmol,
args.threshold,
perfect_score,
writer,
conformerProps=conformerProps,
minEnergy=minEnergy)
else:
mol = Chem.RemoveHs(mol)
count += doO3Dalign(i, mol, qmol, args.threshold, perfect_score,
writer)
i += 1
total += mol.GetNumConformers()
input.close()
writer.flush()
writer.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RDKitO3DAlign': total
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit conformers')
parser.add_argument('-n',
'--num',
type=int,
default=1,
help='number of conformers to generate')
parser.add_argument('-a',
'--attempts',
type=int,
default=0,
help='number of attempts')
parser.add_argument('-r',
'--rmsd',
type=float,
default=1.0,
help='prune RMSD threshold')
parser.add_argument(
'-c',
'--cluster',
type=str.lower,
choices=['rmsd', 'tdf'],
help='Cluster method (RMSD or TFD). If None then no clustering')
parser.add_argument(
'-t',
'--threshold',
type=float,
help='cluster threshold (default of 2.0 for RMSD and 0.3 for TFD)')
parser.add_argument(
'-e',
'--emin',
type=int,
default=0,
help='energy minimisation iterations (default of 0 means none)')
utils.add_default_io_args(parser)
parser.add_argument(
'--smiles',
help=
'input structure as smiles (incompatible with using files or stdin for input)'
)
args = parser.parse_args()
if not args.threshold:
if args.cluster == 'tfd':
args.threshold = 0.3
else:
args.threshold = 2.0
utils.log("Conformers Args: ", args)
source = "conformers.py"
datasetMetaProps = {
"source": source,
"description":
"Conformer generation using RDKit " + rdBase.rdkitVersion
}
clsMappings = {
"RMSToCentroid": "java.lang.Float",
"EnergyDelta": "java.lang.Float",
"EnergyAbs": "java.lang.Float",
"ConformerNum": "java.lang.Integer",
"ClusterCentroid": "java.lang.Integer",
"ClusterNum": "java.lang.Integer",
"StructureNum": "java.lang.Integer"
}
fieldMetaProps = [{
"fieldName": "RMSToCentroid",
"values": {
"source": source,
"description": "RMS distance to the cluster centroid"
}
}, {
"fieldName": "EnergyDelta",
"values": {
"source": source,
"description": "Energy difference to lowest energy structure"
}
}, {
"fieldName": "EnergyAbs",
"values": {
"source": source,
"description": "Absolute energy"
}
}, {
"fieldName": "ConformerNum",
"values": {
"source": source,
"description": "Conformer number"
}
}, {
"fieldName": "ClusterCentroid",
"values": {
"source": source,
"description": "Conformer number of the cluster centroid"
}
}, {
"fieldName": "ClusterNum",
"values": {
"source": source,
"description": "Cluster number"
}
}, {
"fieldName": "StructureNum",
"values": {
"source": source,
"description": "Structure number this conformer was generated from"
}
}]
if args.smiles:
mol = Chem.MolFromSmiles(args.smiles)
suppl = [mol]
input = None
output, writer, output_base = utils.default_open_output(
args.output,
'conformers',
args.outformat,
valueClassMappings=clsMappings,
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
else:
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input,
args.informat,
args.output,
'conformers',
args.outformat,
valueClassMappings=clsMappings,
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
# OK, all looks good so we can hope that things will run OK.
# But before we start lets write the metadata so that the results can be handled.
#if args.meta:
# t = open(output_base + '_types.txt', 'w')
# t.write(field_StructureNum + '=integer\n')
# t.write(field_StructureNum + '=integer\n')
# t.write(field_ConformerNum + '=integer\n')
# t.write(field_EnergyAbs + '=double\n')
# t.write(field_EnergyDelta + '=double\n')
# if args.emin > 0:
# t.write(field_MinimizationConverged + '=boolean\n')
# if args.cluster:
# t.write(field_RMSToCentroid + '=double\n')
# t.write(field_ClusterNum + '=integer\n')
# t.write(field_ClusterCentroid + '=integer\n')
# t.flush()
# t.close()
i = 0
count = 0
for mol in suppl:
if mol is None: continue
m = Chem.AddHs(mol)
conformerPropsDict, minEnergy = process_mol_conformers(
m, i, args.num, args.attempts, args.rmsd, args.cluster,
args.threshold, args.emin)
m = Chem.RemoveHs(m)
write_conformers(m, i, conformerPropsDict, minEnergy, writer)
count = count + m.GetNumConformers()
i += 1
if input:
input.close()
writer.flush()
writer.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RDKitConformer': count
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit Butina Cluster')
parser.add_argument(
'-t',
'--threshold',
type=float,
default=0.7,
help='similarity clustering threshold (1.0 means identical)')
parser.add_argument('-d',
'--descriptor',
type=str.lower,
choices=list(descriptors.keys()),
default='rdkit',
help='descriptor or fingerprint type (default rdkit)')
parser.add_argument('-m',
'--metric',
type=str.lower,
choices=list(metrics.keys()),
default='tanimoto',
help='similarity metric (default tanimoto)')
parser.add_argument(
'-n',
'--num',
type=int,
help='maximum number to pick for diverse subset selection')
parser.add_argument(
'-e',
'--exclude',
type=float,
default=0.9,
help=
'threshold for excluding structures in diverse subset selection (1.0 means identical)'
)
parser.add_argument(
'--fragment-method',
choices=['hac', 'mw'],
default='hac',
help=
'Approach to find biggest fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight)'
)
parser.add_argument(
'--output-fragment',
action='store_true',
help='Output the biggest fragment rather than the original molecule')
parser.add_argument(
'-f',
'--field',
help='field to use to optimise diverse subset selection')
group = parser.add_mutually_exclusive_group()
group.add_argument(
'--min',
action='store_true',
help='pick lowest value specified by the --field option')
group.add_argument(
'--max',
action='store_true',
help='pick highest value specified by the --field option')
utils.add_default_io_args(parser)
parser.add_argument('-q',
'--quiet',
action='store_true',
help='Quiet mode')
parser.add_argument('--thin', action='store_true', help='Thin output mode')
args = parser.parse_args()
utils.log("Cluster Args: ", args)
descriptor = descriptors[args.descriptor]
if descriptor is None:
raise ValueError('Invalid descriptor name ' + args.descriptor)
if args.field and not args.num:
raise ValueError(
'--num argument must be specified for diverse subset selection')
if args.field and not (args.min or args.max):
raise ValueError(
'--min or --max argument must be specified for diverse subset selection'
)
# handle metadata
source = "cluster_butina.py"
datasetMetaProps = {
"source": source,
"description": "Butina clustering using RDKit " + rdBase.rdkitVersion
}
clsMappings = {"Cluster": "java.lang.Integer"}
fieldMetaProps = [{
"fieldName": "Cluster",
"values": {
"source": source,
"description": "Cluster number"
}
}]
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input,
args.informat,
args.output,
'cluster_butina',
args.outformat,
thinOutput=args.thin,
valueClassMappings=clsMappings,
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
### generate fingerprints
#mols = [x for x in suppl if x is not None]
#fps = [descriptor(x) for x in mols]
mols = []
fps = []
errs = mol_utils.fragmentAndFingerprint(
suppl,
mols,
fps,
descriptor,
fragmentMethod=args.fragment_method,
outputFragment=args.output_fragment,
quiet=args.quiet)
input.close()
### do clustering
utils.log("Clustering with descriptor", args.descriptor, "metric",
args.metric, "and threshold", args.threshold)
clusters, dists, matrix = ClusterFps(fps, args.metric,
1.0 - args.threshold)
utils.log("Found", len(clusters), "clusters")
### generate diverse subset if specified
if args.num:
utils.log("Generating diverse subset")
# diverse subset selection is specified
finalClusters = SelectDiverseSubset(mols, clusters, dists, args.num,
args.field, args.max, args.exclude,
args.quiet)
else:
finalClusters = clusters
utils.log("Found", len(finalClusters), "clusters")
lookup = ClustersToMap(finalClusters)
if not args.quiet:
utils.log("Final Clusters:", finalClusters)
### write the results
i = 0
result_count = 0
for mol in mols:
if lookup.has_key(i):
if args.thin:
utils.clear_mol_props(mol, ["uuid"])
cluster = lookup[i]
mol.SetIntProp(field_Cluster, cluster)
writer.write(mol)
result_count += 1
i += 1
writer.flush()
writer.close()
output.close()
if args.meta:
status_str = str(result_count) + ' results from ' + str(
len(finalClusters)) + ' clusters'
utils.write_metrics(
output_base, {
'__StatusMessage__': status_str,
'__InputCount__': i,
'__OutputCount__': result_count,
'RDKitCluster': i
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit Butina Cluster Matrix')
utils.add_default_input_args(parser)
parser.add_argument('-o', '--output', help="Base name for output file (no extension). If not defined then SDTOUT is used for the structures and output is used as base name of the other files.")
parser.add_argument('-of', '--outformat', choices=['tsv', 'json'], default='tsv', help="Output format. Defaults to 'tsv'.")
parser.add_argument('--meta', action='store_true', help='Write metadata and metrics files')
parser.add_argument('-t', '--threshold', type=float, default=0.7, help='Similarity clustering threshold (1.0 means identical)')
parser.add_argument('-mt', '--matrixThreshold', type=float, default=0.5, help='Threshold for outputting values (1.0 means identical)')
parser.add_argument('-d', '--descriptor', type=str.lower, choices=list(cluster_butina.descriptors.keys()), default='rdkit', help='descriptor or fingerprint type (default rdkit)')
parser.add_argument('-m', '--metric', type=str.lower, choices=list(cluster_butina.metrics.keys()), default='tanimoto', help='similarity metric (default tanimoto)')
parser.add_argument('-q', '--quiet', action='store_true', help='Quiet mode')
args = parser.parse_args()
utils.log("Cluster Matrix Args: ", args)
descriptor = cluster_butina.descriptors[args.descriptor]
if descriptor is None:
raise ValueError('Invalid descriptor name ' + args.descriptor)
input,suppl = utils.default_open_input(args.input, args.informat)
# handle metadata
source = "cluster_butina_matrix.py"
datasetMetaProps = {"source":source, "description": "Butina clustering using RDKit " + rdBase.rdkitVersion}
clsMappings = {
"Cluster1": "java.lang.Integer",
"Cluster2": "java.lang.Integer",
"ID1": "java.lang.String",
"ID2": "java.lang.String",
"M1": "java.lang.String",
"M2": "java.lang.String",
"Similarity": "java.lang.Float"
}
fieldMetaProps = [{"fieldName":"Cluster", "values": {"source":source, "description":"Cluster number"}}]
fieldNames = collections.OrderedDict()
fieldNames['ID1'] = 'ID1'
fieldNames['ID2'] ='ID2'
fieldNames['Cluster1'] = 'Cluster1'
fieldNames['Cluster2'] = 'Cluster2'
fieldNames['Similarity'] = 'Similarity'
fieldNames['M1'] = 'M1'
fieldNames['M2'] = 'M2'
writer,output_base = utils.create_simple_writer(args.output, 'cluster_butina_matrix', args.outformat, fieldNames,
valueClassMappings=clsMappings, datasetMetaProps=datasetMetaProps, fieldMetaProps=fieldMetaProps)
### generate fingerprints
mols = [x for x in suppl if x is not None]
fps = [descriptor(x) for x in mols]
input.close()
### do clustering
utils.log("Clustering with descriptor", args.descriptor, "metric", args.metric, "and threshold", args.threshold)
clusters, dists, matrix, = cluster_butina.ClusterFps(fps, args.metric, 1.0 - args.threshold)
utils.log("Found", len(clusters), "clusters")
MapClusterToMols(clusters, mols)
if not args.quiet:
utils.log("Clusters:", clusters)
writer.writeHeader()
size = len(matrix)
#utils.log("len(matrix):", size)
count = 0
for i in range(size ):
#utils.log("element",i, "has length", len(matrix[i]))
writer.write(create_values(mols, i, i, 1.0))
count += 1
for j in range(len(matrix[i])):
#utils.log("writing",i,j)
dist = matrix[i][j]
if dist > args.matrixThreshold:
# the matrix is the lower left segment without the diagonal
x = j
y = i + 1
writer.write(create_values(mols, x, y, dist))
writer.write(create_values(mols, y, x, dist))
count += 2
writer.write(create_values(mols, size, size, 1.0))
writer.writeFooter()
writer.close()
if args.meta:
utils.write_metrics(output_base, {'__InputCount__':i, '__OutputCount__':count, 'RDKitCluster':i})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit screen')
group = parser.add_mutually_exclusive_group()
group.add_argument(
'--qsmiles',
help='query structure as smiles (incompatible with -qmolfile arg)')
group.add_argument(
'--qmolfile',
help=
'query structure as filename in molfile format (incompatible with -qsmiles arg)'
)
parser.add_argument('--simmin',
type=float,
default=0.7,
help='similarity lower cutoff (1.0 means identical)')
parser.add_argument('--simmax',
type=float,
default=1.0,
help='similarity upper cutoff (1.0 means identical)')
parser.add_argument('-d',
'--descriptor',
type=str.lower,
choices=list(descriptors.keys()),
default='rdkit',
help='descriptor or fingerprint type (default rdkit)')
parser.add_argument('-m',
'--metric',
type=str.lower,
choices=list(metrics.keys()),
default='tanimoto',
help='similarity metric (default tanimoto)')
parser.add_argument(
'-f',
'--fragment',
choices=['hac', 'mw'],
help=
'Find single fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight )'
)
parser.add_argument('--hacmin', type=int, help='Min heavy atom count')
parser.add_argument('--hacmax', type=int, help='Max heavy atom count')
parser.add_argument('--mwmin', type=float, help='Min mol weight')
parser.add_argument('--mwmax', type=float, help='Max mol weight')
utils.add_default_io_args(parser)
parser.add_argument('--thin', action='store_true', help='Thin output mode')
parser.add_argument('-q',
'--quiet',
action='store_true',
help='Quiet mode')
args = parser.parse_args()
utils.log("Screen Args: ", args)
descriptor = descriptors[args.descriptor.lower()]
metric = metrics[args.metric.lower()]
if args.qsmiles:
query_rdkitmol = Chem.MolFromSmiles(args.qsmiles)
elif args.qmolfile:
query_rdkitmol = Chem.MolFromMolFile(args.qmolfile)
else:
raise ValueError('No query structure specified')
query_fp = descriptor(query_rdkitmol)
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input,
args.informat,
args.output,
'screen',
args.outformat,
thinOutput=args.thin)
i = 0
count = 0
for mol in suppl:
i += 1
if mol is None: continue
if args.fragment:
mol = filter.fragment(mol, args.fragment, quiet=args.quiet)
if not filter.filter(mol,
minHac=args.hacmin,
maxHac=args.hacmax,
minMw=args.mwmin,
maxMw=args.mwmax,
quiet=args.quiet):
continue
target_fp = descriptor(mol)
sim = metric(query_fp, target_fp)
if sim >= args.simmin and sim <= args.simmax:
count += 1
if not args.quiet:
utils.log(i, sim)
mol.SetDoubleProp(field_Similarity, sim)
writer.write(mol)
utils.log("Found", count, "similar molecules")
writer.flush()
writer.close()
input.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RDKitScreen': i
})
return count
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit filter')
parser.add_argument(
'-f',
'--fragment',
choices=['hac', 'mw'],
help=
'Find single fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight )'
)
parser.add_argument('--hacmin', type=int, help='Min heavy atom count')
parser.add_argument('--hacmax', type=int, help='Max heavy atom count')
parser.add_argument('--mwmin', type=float, help='Min mol weight')
parser.add_argument('--mwmax', type=float, help='Max mol weight')
parser.add_argument('-l',
'--limit',
type=int,
help='Limit output to this many records')
parser.add_argument(
'-c',
'--chunksize',
type=int,
help=
'Split output into chunks of size c. Output will always be files. Names like filter1.sdf.gz, filter2.sdf.gz ...'
)
parser.add_argument(
'-d',
'--digits',
type=int,
default=0,
help=
'When splitting zero pad the file name to this many digits so that they are in sorted order. Names like filter001.sdf.gz, filter002.sdf.gz ...'
)
parser.add_argument(
'--no-gzip',
action='store_true',
help='Do not compress the output (STDOUT is never compressed')
# WARNING: thin output is not appropriate when using --fragment
parser.add_argument('--thin', action='store_true', help='Thin output mode')
parser.add_argument(
'-q',
'--quiet',
action='store_true',
help='Quiet mode - suppress reporting reason for filtering')
utils.add_default_io_args(parser)
args = parser.parse_args()
utils.log("Filter Args: ", args)
input, suppl = utils.default_open_input(args.input, args.informat)
if args.chunksize:
chunkNum = 1
if args.output:
output_base = args.output
else:
output_base = 'filter'
output_base_chunk = output_base + str(chunkNum).zfill(args.digits)
output, writer, output_base_chunk = utils.default_open_output(
output_base_chunk,
output_base_chunk,
args.outformat,
compress=not args.no_gzip)
else:
output, writer, output_base_chunk = utils.default_open_output(
args.output, "filter", args.outformat, compress=not args.no_gzip)
output_base = output_base_chunk
utils.log("Writing to " + output_base_chunk)
i = 0
count = 0
chunkNum = 1
for mol in suppl:
if args.limit and count >= args.limit:
break
i += 1
if mol is None: continue
if args.fragment:
mol = fragment(mol, args.fragment, quiet=args.quiet)
if not filter(mol,
minHac=args.hacmin,
maxHac=args.hacmax,
minMw=args.mwmin,
maxMw=args.mwmax,
quiet=args.quiet):
continue
if args.chunksize:
if count > 0 and count % args.chunksize == 0:
writer.close()
output.close()
chunkNum += 1
output_chunk_base = output_base + str(chunkNum).zfill(
args.digits)
utils.log("Writing to " + output_chunk_base)
output, writer, output_chunk_base = utils.default_open_output(
output_chunk_base,
output_chunk_base,
args.outformat,
compress=not args.no_gzip)
count += 1
writer.write(mol)
utils.log("Filtered", i, "down to", count, "molecules")
if args.chunksize:
utils.log("Wrote", chunkNum, "chunks")
if (args.digits > 0 and len(str(chunkNum)) > args.digits):
utils.log(
"WARNING: not enough digits specified for the number of chunks"
)
writer.flush()
writer.close()
input.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RDKitFilter': i
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit screen')
group = parser.add_mutually_exclusive_group()
group.add_argument(
'--qsmiles',
help=
'filename of query structures as smiles (incompatible with --sdf and --qjson args)'
)
group.add_argument(
'--qsdf',
help=
'filename of query structures as sdfile (incompatible with --smiles and --qjson args)'
)
group.add_argument(
'--qjson',
help=
'filename of query structures as MoleculeObject JSON (incompatible with --qsmiles and --qsdf args)'
)
parser.add_argument('--qsmilesTitleLine',
action='store_true',
help='the smiles file has a title line')
parser.add_argument('--qsmilesDelimiter',
default='\t',
help='delimiter for smiles file (default is tab)')
parser.add_argument(
'--qsmilesColumn',
type=int,
default=0,
help='column in smiles file with the smiles (default is first column)')
parser.add_argument(
'--qsmilesNameColumn',
type=int,
default=1,
help='column in smiles file with ID (default is second column)')
parser.add_argument(
'--qprop',
help=
'property name in query molecules to report. If not defined (or property is not present) '
+
'then name property is not written. JSON format uses the UUID as default'
)
parser.add_argument('--simmin',
type=float,
default=0.7,
help='similarity lower cutoff (1.0 means identical)')
parser.add_argument('--simmax',
type=float,
default=1.0,
help='similarity upper cutoff (1.0 means identical)')
parser.add_argument('-d',
'--descriptor',
type=str.lower,
choices=list(descriptors.keys()),
default='rdkit',
help='descriptor or fingerprint type (default rdkit)')
parser.add_argument('-m',
'--metric',
type=str.lower,
choices=list(metrics.keys()),
default='tanimoto',
help='similarity metric (default tanimoto)')
parser.add_argument(
'-f',
'--fragment',
choices=['hac', 'mw'],
help=
'Find single fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight )'
)
parser.add_argument('--hacmin', type=int, help='Min heavy atom count')
parser.add_argument('--hacmax', type=int, help='Max heavy atom count')
parser.add_argument('--mwmin', type=float, help='Min mol weight')
parser.add_argument('--mwmax', type=float, help='Max mol weight')
utils.add_default_io_args(parser)
parser.add_argument('--thin', action='store_true', help='Thin output mode')
parser.add_argument('-q',
'--quiet',
action='store_true',
help='Quiet mode')
args = parser.parse_args()
utils.log("Screen Args: ", args)
descriptor = descriptors[args.descriptor.lower()]
metric = metrics[args.metric.lower()]
propName = args.qprop
if args.qsmiles:
queryMolsupplier = utils.default_open_input_smiles(
args.qsmiles,
delimiter=args.qsmilesDelimiter,
smilesColumn=args.qsmilesColumn,
nameColumn=args.qsmilesNameColumn,
titleLine=args.qsmilesTitleLine)
queryInput = None
elif args.qsdf:
queryInput, queryMolsupplier = utils.default_open_input_sdf(args.qsdf)
elif args.qjson:
queryInput, queryMolsupplier = utils.default_open_input_json(
args.qjson, lazy=False)
if not propName:
propName = "uuid"
else:
raise ValueError('No query structure specified')
queryFps = {}
utils.log("Preparing query fingerprints")
count = 0
for q in queryMolsupplier:
count += 1
if q:
queryFps[q] = descriptor(q)
else:
utils.log("WARNING: Failed to parse Molecule", count)
if queryInput:
queryInput.close()
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input, args.informat, args.output, 'screen_multi', args.outformat)
# OK, all looks good so we can hope that things will run OK.
# But before we start lets write the metadata so that the results can be handled.
#if args.meta:
# t = open(output_base + '_types.txt', 'w')
# t.write(field_Similarity + '=integer\n')
# t.flush()
# t.close()
i = 0
count = 0
for mol in suppl:
i += 1
if mol is None: continue
if args.fragment:
mol = filter.fragment(mol, args.fragment, quiet=args.quiet)
if not filter.filter(mol,
minHac=args.hacmin,
maxHac=args.hacmax,
minMw=args.mwmin,
maxMw=args.mwmax,
quiet=args.quiet):
continue
targetFp = descriptor(mol)
idx = 0
hits = 0
bestScore = 0
bestName = None
for queryMol in queryFps:
idx += 1
sim = metric(queryFps[queryMol], targetFp)
if propName:
name = str(queryMol.GetProp(propName))
else:
name = None
if sim >= args.simmin and sim <= args.simmax:
hits += 1
if not args.quiet:
utils.log(i, idx, sim)
if sim > bestScore:
bestScore = sim
bestIdx = idx
if name:
bestName = name
if name:
mol.SetDoubleProp(field_Similarity + "_" + name, sim)
else:
mol.SetDoubleProp(
field_Similarity + "_" + str(idx) + "_Score", sim)
if hits > 0:
count += 1
mol.SetDoubleProp(field_Similarity + "_BestScore", bestScore)
if bestName:
mol.SetProp(field_Similarity + "_BestName", bestName)
else:
mol.SetIntProp(field_Similarity + "_BestIndex", bestIdx)
mol.SetIntProp(field_Similarity + "_Count", hits)
writer.write(mol)
utils.log("Found", count, "similar molecules")
writer.flush()
writer.close()
input.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RDKitScreen': count
})
return count
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
inputs = 0
totalCount = 0
totalErrors = 0
for mol in suppl:
inputs += 1
if mol:
count, errors = generate_conformers(inputs, mol, args.num, ref_mol,
WRITER, args.core_smi)
totalCount += count
totalErrors += errors
input.close()
WRITER.close()
if totalErrors > 0:
utils.log("WARNING:", totalErrors, "conformers failed to generate")
# write metrics
if args.meta:
metrics = {
'__InputCount__': inputs,
'__OutputCount__': totalCount,
'RDKitConstrainedConformer': totalCount
}
if totalErrors > 0:
metrics['__ErrorCount__'] = totalErrors
utils.write_metrics(output_base, metrics)
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit Butina Cluster')
parser.add_argument('-t', '--threshold', type=float, default=0.0, help='similarity threshold (1.0 means identical)')
parser.add_argument('-d', '--descriptor', type=str.lower, choices=list(descriptors.keys()), default='morgan2', help='descriptor or fingerprint type (default rdkit)')
parser.add_argument('-q', '--quiet', action='store_true', help='Quiet mode')
parser.add_argument('-n', '--num', type=int, help='maximum number to pick for diverse subset selection')
parser.add_argument('-s', '--seed-molecules', help='optional file containing any seed molecules that have already been picked')
parser.add_argument('--fragment-method', choices=['hac', 'mw'], default='hac', help='Approach to find biggest fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight)')
parser.add_argument('--output-fragment', action='store_true', help='Output the biggest fragment rather than the original molecule')
utils.add_default_io_args(parser)
args = parser.parse_args()
utils.log("MaxMinPicker Args: ", args)
descriptor = descriptors[args.descriptor]
if descriptor is None:
raise ValueError('No descriptor specified')
if not args.num and not args.threshold:
raise ValueError('--num or --threshold arguments must be specified, or both')
# handle metadata
source = "max_min_picker.py"
datasetMetaProps = {"source":source, "description": "MaxMinPicker using RDKit " + rdBase.rdkitVersion}
### generate fingerprints
fps = []
mols = []
errors = 0
# first the initial seeds, if specified
firstPicks = []
num_seeds = 0
if args.seed_molecules:
seedsInput,seedsSuppl = utils.default_open_input(args.seed_molecules, None)
start = time.time()
errors += mol_utils.fragmentAndFingerprint(seedsSuppl, mols, fps, descriptor, fragmentMethod=args.fragment_method, outputFragment=args.output_fragment, quiet=args.quiet)
end = time.time()
seedsInput.close()
num_seeds = len(fps)
utils.log("Read", len(fps), "fingerprints for seeds in", end-start, "secs,", errors, "errors")
firstPicks = list(range(num_seeds))
# now the molecules to pick from
input,output,suppl,writer,output_base = utils.default_open_input_output(args.input, args.informat, args.output, 'cluster_butina',
args.outformat, datasetMetaProps=datasetMetaProps)
# reset the mols list as we don't need the seeds, only the candidates
mols = []
start = time.time()
errs = mol_utils.fragmentAndFingerprint(suppl, mols, fps, descriptor, fragmentMethod=args.fragment_method, outputFragment=args.output_fragment, quiet=args.quiet)
end = time.time()
errors += errs
input.close()
num_fps = len(fps)
num_candidates = num_fps - num_seeds
utils.log("Read", num_candidates, "fingerprints for candidates in", end-start, "secs,", errs, "errors")
if not args.num:
num_to_pick = num_candidates
elif args.num > num_candidates:
num_to_pick = num_candidates
utils.log("WARNING: --num argument (", args.num, ") is larger than the total number of candidates (", num_candidates, ") - resetting to", num_candidates)
else:
num_to_pick = args.num
### do picking
utils.log("MaxMinPicking with descriptor", args.descriptor, "and threshold", args.threshold, ",", num_seeds, "seeds,", num_candidates, "candidates", num_fps, "total")
start = time.time()
picks, thresh = performPick(fps, num_to_pick + num_seeds, args.threshold, firstPicks)
end = time.time()
num_picks = len(picks)
utils.log("Found", num_picks, "molecules in", end-start, "secs, final threshold", thresh)
utils.log("Picks:", list(picks[num_seeds:]))
del fps
# we want to return the results in the order they were in the input so first we record the order in the pick list
indices = {}
i = 0
for idx in picks[num_seeds:]:
indices[idx] = i
i += 1
# now do the sort
sorted_picks = sorted(picks[num_seeds:])
# now write out the mols in the correct order recording the value in the pick list as the PickIndex property
i = 0
for idx in sorted_picks:
mol = mols[idx - num_seeds] # mols array only contains the candidates
mol.SetIntProp("PickIndex", indices[idx] + 1)
writer.write(mol)
i += 1
utils.log("Output", i, "molecules")
writer.flush()
writer.close()
output.close()
if args.meta:
metrics = {}
status_str = "{} compounds picked. Final threshold was {}.".format(i, thresh)
if errors > 0:
metrics['__ErrorCount__'] = errors
status_str = status_str + " {} errors.".format(errors)
metrics['__StatusMessage__'] = status_str
metrics['__InputCount__'] = num_fps
metrics['__OutputCount__'] = i
metrics['RDKitMaxMinPicker'] = num_picks
utils.write_metrics(output_base, metrics)
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit filter')
parser.add_argument('-f', '--fragment', choices=['hac', 'mw'], help='Find single fragment if more than one (hac = biggest by heavy atom count, mw = biggest by mol weight )')
parser.add_argument('--hacmin', type=int, help='Min heavy atom count')
parser.add_argument('--hacmax', type=int, help='Max heavy atom count')
parser.add_argument('--mwmin', type=float, help='Min mol weight')
parser.add_argument('--mwmax', type=float, help='Max mol weight')
parser.add_argument('-l', '--limit', type=int, help='Limit output to this many records')
parser.add_argument('-c', '--chunksize', type=int, help='Split output into chunks of size c. Output will always be files. Names like filter01.sdf.gz, filter02.sdf.gz ...')
# WARNING: thin output is not appropriate when using --fragment
parser.add_argument('--thin', action='store_true', help='Thin output mode')
parser.add_argument('-q', '--quiet', action='store_true', help='Quiet mode - suppress reporting reason for filtering')
utils.add_default_io_args(parser)
args = parser.parse_args()
utils.log("Filter Args: ", args)
input,suppl = utils.default_open_input(args.input, args.informat)
if args.output:
output_base = args.output
else:
output_base = 'filter'
# OK, all looks good so we can hope that things will run OK.
# But before we start lets write the metadata so that the results can be handled.
#t = open(output_base + '_types.txt', 'w')
#t.write(field_Similarity + '=integer\n')
#t.flush()
#t.close()
if args.chunksize:
chunkNum = 1
output = gzip.open(output_base + str(chunkNum) + '.sdf.gz','w+')
elif args.output:
output = gzip.open(output_base + '.sdf.gz','w+')
else:
output = sys.stdout
writer = Chem.SDWriter(output)
i=0
count = 0
chunkCount = 1
for mol in suppl:
if args.limit and count >= args.limit:
break
i +=1
if mol is None: continue
if args.fragment:
mol = fragment(mol, args.fragment, quiet=args.quiet)
if not filter(mol, minHac=args.hacmin, maxHac=args.hacmax, minMw=args.mwmin, maxMw=args.mwmax, quiet=args.quiet):
continue
if args.chunksize:
if count > 0 and count % args.chunksize == 0:
writer.close()
output.close()
chunkCount += 1
output = gzip.open(output_base + str(chunkCount) + '.sdf.gz','w+')
writer = Chem.SDWriter(output)
count += 1
writer.write(mol)
utils.log("Filtered", i, "down to", count, "molecules")
if args.chunksize:
utils.log("Wrote", chunkCount, "chunks")
writer.flush()
writer.close()
input.close()
output.close()
if args.meta:
utils.write_metrics(output_base, {'__InputCount__':i, '__OutputCount__':count, 'RDKitFilter':i})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(
description='RDKit molecule standardiser / enumerator')
utils.add_default_io_args(parser)
parser.add_argument('-et',
'--enumerate_tauts',
action='store_true',
help='Enumerate all tautomers')
parser.add_argument('-es',
'--enumerate_stereo',
action='store_true',
help='Enumerate all stereoisomers')
parser.add_argument(
'-st',
'--standardize',
action='store_true',
help='Standardize molecules. Cannot be true if enumerate is on.')
parser.add_argument('-stm',
'--standardize_method',
default="molvs",
choices=STANDARD_MOL_METHODS.keys(),
help="Chose the method to standardize.")
args = parser.parse_args()
if args.standardize and args.enumerate_tauts:
raise ValueError("Cannot Enumerate Tautomers and Standardise")
if args.standardize and args.enumerate_stereo:
raise ValueError("Cannot Enumerate Stereo and Standardise")
if args.standardize:
getStandardMolecule = STANDARD_MOL_METHODS[args.standardize_method]
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input, args.informat, args.output, 'sanify', args.outformat)
i = 0
count = 0
errors = 0
for mol in suppl:
i += 1
if mol is None: continue
if args.standardize:
# we keep the original UUID as there is still a 1-to-1 relationship between the input and outputs
oldUUID = mol.GetProp("uuid")
inputCanSmiles = Chem.MolToSmiles(mol,
isomericSmiles=True,
canonical=True)
try:
std = getStandardMolecule(mol)
outputCanSmiles = Chem.MolToSmiles(std,
isomericSmiles=True,
canonical=True)
if oldUUID:
std.SetProp("uuid", oldUUID)
#utils.log("Standardized", i, inputCanSmiles, ">>", outputCanSmiles)
if inputCanSmiles == outputCanSmiles:
std.SetProp("Standardised", "False")
else:
std.SetProp("Standardised", "True")
except:
errors += 1
utils.log("Error standardizing", sys.exc_info()[0])
std = mol
std.SetProp("Standardised", "Error")
count = write_out([std], count, writer)
else:
# we want a new UUID generating as we are generating new molecules
parentUuid = mol.GetProp("uuid")
results = []
results.append(mol)
if args.enumerate_tauts:
utils.log("Enumerating tautomers")
results = enumerateTautomers(mol)
if args.enumerate_stereo:
utils.log("Enumerating steroisomers")
mols = results
results = []
for m in mols:
enumerated = enumerateStereoIsomers(m)
results.extend(enumerated)
for m in results:
m.ClearProp("uuid")
m.SetIntProp("SourceMolNum", i)
if parentUuid:
m.SetProp("SourceMolUUID", parentUuid)
count = write_out(results, count, writer)
utils.log("Handled " + str(i) + " molecules, resulting in " + str(count) +
" outputs")
writer.flush()
writer.close()
input.close()
output.close()
if args.meta:
utils.write_metrics(
output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'__ErrorCount__': errors,
'RDKitSanify': count
})
return count
# Get the molecules
input, suppl = utils.default_open_input(args.input, args.informat)
output, WRITER, output_base = utils.default_open_output(
args.output,
"const_conf_gen",
args.outformat,
valueClassMappings=clsMappings,
datasetMetaProps=datasetMetaProps,
fieldMetaProps=fieldMetaProps)
inputs = 0
total = 0
for mol in suppl:
inputs += 1
if mol:
total += generate_conformers(inputs, mol, args.num, ref_mol,
WRITER, args.core_smi)
input.close()
WRITER.close()
# write metrics
if args.meta:
utils.write_metrics(
output_base, {
'__InputCount__': inputs,
'__OutputCount__': total,
'RDKitConstrainedConformer': total
})
def main():
### command line args defintions #########################################
### Define the reactions available
poised_filter = True
if poised_filter == True:
from poised_filter import Filter
filter_to_use = Filter()
parser = argparse.ArgumentParser(description='RDKit rxn process')
utils.add_default_io_args(parser)
parser.add_argument('-q',
'--quiet',
action='store_true',
help='Quiet mode')
parser.add_argument('-m',
'--multi',
action='store_true',
help='Output one file for each reaction')
parser.add_argument('-r',
'--reaction',
choices=filter_to_use.poised_reactions.keys(),
help='Name of reaction to be run')
parser.add_argument('-rl',
'--reagent_lib',
help="Input SD file, if not defined the STDIN is used")
parser.add_argument(
'-rlf',
'--reagent_lib_format',
choices=['sdf', 'json'],
help="Input format. When using STDIN this must be specified.")
args = parser.parse_args()
utils.log("Screen Args: ", args)
if not args.output and args.multi:
raise ValueError(
"Must specify output location when writing individual result files"
)
input, suppl = utils.default_open_input(args.input, args.informat)
reagent_input, reagent_suppl = utils.default_open_input(
args.reagent_lib, args.reagent_lib_format)
output, writer, output_base = utils.default_open_output(
args.output, "rxn_maker", args.outformat)
i = 0
count = 0
if args.multi:
dir_base = os.path.dirname(args.output)
writer_dict = filter_to_use.get_writers(dir_base)
else:
writer_dict = None
dir_base = None
for mol in suppl:
i += 1
if mol is None: continue
# Return a dict/class here - indicating which filters passed
count = filter_to_use.perform_reaction(mol, args.reaction,
reagent_suppl, writer, count)
utils.log("Created", count, "molecules from a total of ", i,
"input molecules")
writer.flush()
writer.close()
if input:
input.close()
if output:
output.close()
# close the individual writers
if writer_dict:
for key in writer_dict:
writer_dict[key].close()
if args.meta:
utils.write_metrics(
output_base, {
'__InputCount__': i,
'__OutputCount__': count,
'RxnSmartsFilter': count
})
def main():
### command line args defintions #########################################
parser = argparse.ArgumentParser(description='RDKit rxn smarts filter')
utils.add_default_io_args(parser)
parser.add_argument('-q', '--quiet', action='store_true', help='Quiet mode')
parser.add_argument('-m', '--multi', action='store_true', help='Output one file for each reaction')
parser.add_argument('--thin', action='store_true', help='Thin output mode')
args = parser.parse_args()
utils.log("Screen Args: ", args)
if not args.output and args.multi:
raise ValueError("Must specify output location when writing individual result files")
### Define the filter chooser - lots of logic possible
# SMARTS patterns are defined in poised_filter.py. Currently this is hardcoded.
# Should make this configurable so that this can be specified by the user at some stage.
poised_filter = True
if poised_filter == True:
from poised_filter import Filter
filter_to_use = Filter()
rxn_names = filter_to_use.get_rxn_names()
utils.log("Using", len(rxn_names), "reaction filters")
# handle metadata
source = "rxn_smarts_filter.py"
datasetMetaProps = {"source":source, "description": "Reaction SMARTS filter"}
clsMappings = {}
fieldMetaProps = []
for name in rxn_names:
# this is the Java class type for an array of MoleculeObjects
clsMappings[name] = "[Lorg.squonk.types.MoleculeObject;"
fieldMetaProps.append({"fieldName":name, "values": {"source":source, "description":"Sythons from " + name + " reaction"}})
input, output, suppl, writer, output_base = utils.default_open_input_output(
args.input, args.informat, args.output,
'rxn_smarts_filter', args.outformat, thinOutput=args.thin, valueClassMappings=clsMappings, datasetMetaProps=datasetMetaProps, fieldMetaProps=fieldMetaProps)
i = 0
count = 0
if args.multi:
dir_base = os.path.dirname(args.output)
writer_dict = filter_to_use.get_writers(dir_base)
else:
writer_dict = None
dir_base = None
for mol in suppl:
i += 1
if mol is None: continue
# Return a dict/class here - indicating which filters passed
filter_pass = filter_to_use.pass_filter(mol)
utils.log("Found", str(len(filter_pass)), "matches")
if filter_pass:
props = {}
count += 1
for reaction in filter_pass:
molObjList = []
# Write the reaction name as a newline separated list of the synthons to the mol object
# this is used in SDF output
mol.SetProp(reaction, "\n".join(filter_pass[reaction]))
# now write to the props is a way that can be used for the JSON output
for smiles in filter_pass[reaction]:
# generate a dict that generates MoleculeObject JSON
mo = utils.generate_molecule_object_dict(smiles, "smiles", None)
molObjList.append(mo)
props[reaction] = molObjList
if args.multi:
writer_dict[reaction].write(mol)
writer_dict[reaction].flush()
# write the output.
# In JSON format the props will override values set on the mol
# In SDF format the props are ignored so the values in the mol are used
writer.write(mol, props)
writer.flush()
utils.log("Matched", count, "molecules from a total of", i)
if dir_base:
utils.log("Individual SD files found in: " + dir_base)
writer.flush()
writer.close()
if input:
input.close()
if output:
output.close()
# close the individual writers
if writer_dict:
for key in writer_dict:
writer_dict[key].close()
if args.meta:
utils.write_metrics(output_base, {'__InputCount__': i, '__OutputCount__': count, 'RxnSmartsFilter': count})
