in python/dgllife/utils/analysis.py [0:0]
def analyze_mols(smiles=None, mols=None, num_processes=1, path_to_export=None):
r"""Analyze a collection of molecules
The analysis will 1) filter out invalid molecules and record the valid ones;
2) record the number of molecules having each particular descriptor/element
(e.g. single bond). The descriptors/elements considered include:
If ``path_to_export`` is not None, we will export the analysis results to
the following files in ``path_to_export``:
* **valid_canonical_smiles.txt**: A file of canonical SMILES for valid molecules
* **summary.txt**: A file of all analysis results,
see the **Examples** section for more details. For summary, we either compute
mean/std of values or count the frequency that a value appears in molecules.
Parameters
----------
smiles : list of str, optional
SMILES strings for a collection of molecules. Can be omitted if mols is not None.
(Default: None)
mols : list of rdkit.Chem.rdchem.Mol objects, optional
RDKit molecule instances for a collection of molecules.
Can be omitted if smiles is not None. (Default: None)
num_processes : int, optional
Number of processes for data analysis. (Default: 1)
path_to_export : str, optional
The directory to export analysis results. If not None, we will export the analysis
results to local files in the specified directory. (Default: None)
Returns
-------
dict
Summary of the analysis results. For more details, see the **Examples** section.
Examples
--------
>>> from dgllife.utils import analyze_mols
>>> smiles = ['CCO', 'CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C', '1']
>>> # Analyze the results and save the results to the current directory
>>> results = analyze_mols(smiles, path_to_export='.')
>>> results
{'num_atoms': [3, 23], # Number of atoms in each molecule
'num_bonds': [2, 25], # Number of bonds in each molecule
'num_rings': [0, 3], # Number of rings in each molecule
'num_input_mols': 3, # Number of input molecules
'num_valid_mols': 2, # Number of valid molecules
'valid_proportion': 0.6666666666666666, # Proportion of valid molecules
'cano_smi': ['CCO', # Canonical SMILES for valid molecules
'CC1(C)S[C@@H]2[C@H](NC(=O)Cc3ccccc3)C(=O)N2[C@H]1C(=O)O'],
# The following items give the number of times each descriptor value appears in molecules
'atom_type_frequency': {'O': 2, 'C': 2, 'N': 1, 'S': 1},
'degree_frequency': {1: 2, 2: 2, 3: 1, 4: 1},
'total_degree_frequency': {2: 2, 4: 2, 1: 1, 3: 1},
'explicit_valence_frequency': {1: 2, 2: 2, 3: 1, 4: 1},
'implicit_valence_frequency': {1: 2, 2: 2, 3: 2, 0: 1},
'hybridization_frequency': {'SP3': 2, 'SP2': 1},
'total_num_h_frequency': {1: 2, 2: 2, 3: 2, 0: 1},
'formal_charge_frequency': {0: 2},
'num_radical_electrons_frequency': {0: 2},
'aromatic_atom_frequency': {False: 2, True: 1},
'chirality_tag_frequency': {'CHI_UNSPECIFIED': 2,
'CHI_TETRAHEDRAL_CCW': 1,
'CHI_TETRAHEDRAL_CW': 1},
'bond_type_frequency': {'SINGLE': 2, 'DOUBLE': 1, 'AROMATIC': 1},
'conjugated_bond_frequency': {False: 2, True: 1},
'bond_stereo_configuration_frequency': {'STEREONONE': 2},
'bond_direction_frequency': {'NONE': 2}}
"""
assert not ((smiles is None) and (mols is None)), \
'At least one of the smiles and mols should not be None'
assert num_processes >= 1, \
'Expect num_processes to be no smaller than 1, got {:d}'.format(num_processes)
general_items = ['num_atoms', 'num_bonds', 'num_rings']
atom_items = ['atom_type', 'degree', 'total_degree', 'explicit_valence',
'implicit_valence', 'hybridization', 'total_num_h', 'formal_charge',
'num_radical_electrons', 'aromatic_atom', 'chirality_tag']
bond_items = ['bond_type', 'conjugated_bond', 'bond_stereo_configuration', 'bond_direction']
# Holder of the analysis results
summary = dict()
for item in list(itertools.chain.from_iterable([general_items, atom_items, bond_items])):
summary[item] = []
# Check validity
if mols is None:
mols = []
for smi in smiles:
smi_mol = Chem.MolFromSmiles(smi)
if smi_mol is not None:
mols.append(smi_mol)
summary['num_input_mols'] = len(smiles)
summary['num_valid_mols'] = len(mols)
else:
summary['num_input_mols'] = len(mols)
mols_ = []
for smi_mol in mols:
if smi_mol is not None:
mols_.append(smi_mol)
mols = mols_
summary['num_valid_mols'] = len(mols)
summary['valid_proportion'] = summary['num_valid_mols'] / summary['num_input_mols']
# Get canonicalized SMILES
summary['cano_smi'] = [Chem.MolToSmiles(smi_mol) for smi_mol in mols]
if num_processes == 1:
summary_per_mol = []
for smi_mol in mols:
summary_per_mol.append(summarize_a_mol(smi_mol))
else:
with Pool(processes=num_processes) as pool:
summary_per_mol = pool.map(summarize_a_mol, mols)
for mol_summary in summary_per_mol:
for key in general_items:
summary[key].append(mol_summary[key])
for key in list(itertools.chain.from_iterable([atom_items, bond_items])):
summary[key].extend(list(mol_summary[key]))
for item in list(itertools.chain.from_iterable([atom_items, bond_items])):
summary[item + '_frequency'] = count_frequency(summary[item])
del summary[item]
if path_to_export is None:
return summary
# Export the analysis results to local files
with open(path_to_export + '/valid_canonical_smiles.txt', 'w') as file:
for smi in summary['cano_smi']:
file.write(smi + '\n')
with open(path_to_export + '/summary.txt', 'w') as file:
file.write('General statistics\n')
file.write('=' * 60 + '\n')
file.write('Number of input molecules: {}\n'.format(summary['num_input_mols']))
file.write('Number of valid molecules: {}\n'.format(summary['num_valid_mols']))
file.write('Percentage of valid molecules: {} %\n'.format(
summary['valid_proportion'] * 100))
file.write('Average number of atoms per molecule: {} +- {}\n'.format(
np.mean(summary['num_atoms']), np.std(summary['num_atoms'])))
file.write('Average number of bonds per molecule: {} +- {}\n'.format(
np.mean(summary['num_bonds']), np.std(summary['num_bonds'])))
file.write('Average number of rings per molecule: {} +- {}\n'.format(
np.mean(summary['num_rings']), np.std(summary['num_rings'])))
file.write('\n')
file.write('Atom statistics\n')
file.write('=' * 60 + '\n')
for item in atom_items:
file.write('{} frequency: {}\n'.format(item, summary[item + '_frequency']))
file.write('\n')
file.write('Bond statistics\n')
file.write('=' * 60 + '\n')
for item in bond_items:
file.write('{} frequency: {}\n'.format(item, summary[item + '_frequency']))
file.write('\n')
return summary