Source code for oddt.scoring.descriptors
import numpy as np
from scipy.spatial.distance import cdist as distance
[docs]def atoms_by_type(atom_dict, types, mode = 'atomic_nums'):
"""Returns atom dictionaries based on given criteria. Currently we have 3 types of atom selection criteria:
* atomic numbers ['atomic_nums']
* Sybyl Atom Types ['atom_types_sybyl']
* AutoDock4 atom types ['atom_types_ad4'] (http://autodock.scripps.edu/faqs-help/faq/where-do-i-set-the-autodock-4-force-field-parameters)
Parameters
----------
atom_dict: oddt.toolkit.Molecule.atom_dict
Atom dictionary as implemeted in oddt.toolkit.Molecule class
types: array-like
List of atom types/numbers wanted.
Returns
-------
out: dictionary of shape=[len(types)]
A dictionary of queried atom types (types are keys of the dictionary). Values are of oddt.toolkit.Molecule.atom_dict type.
"""
if mode == 'atomic_nums':
return {num: atom_dict[atom_dict['atomicnum'] == num] for num in set(types)}
elif mode == 'atom_types_sybyl':
return {t: atom_dict[atom_dict['atomtype'] == t] for t in set(types)}
elif mode == 'atom_types_ad4':
# all AD4 atom types are capitalized
types = [t.upper() for t in types]
out = {}
for t in set(types):
if t == 'HD':
out[t] = atom_dict[atom_dict['atomicnum'] == 1 & atom_dict['isdonorh']]
elif t == 'C':
out[t] = atom_dict[atom_dict['atomicnum'] == 6 & ~atom_dict['isaromatic']]
elif t == 'CD': # not canonical AD4 type, although used by NNscore, with no description. properies assumed by name
out[t] = atom_dict[atom_dict['atomicnum'] == 6 & ~atom_dict['isdonor']]
elif t == 'A':
out[t] = atom_dict[atom_dict['atomicnum'] == 6 & atom_dict['isaromatic']]
elif t == 'N':
out[t] = atom_dict[atom_dict['atomicnum'] == 7 & ~atom_dict['isacceptor']]
elif t == 'NA':
out[t] = atom_dict[atom_dict['atomicnum'] == 7 & atom_dict['isacceptor']]
elif t == 'OA':
out[t] = atom_dict[atom_dict['atomicnum'] == 8 & atom_dict['isacceptor']]
elif t == 'F':
out[t] = atom_dict[atom_dict['atomicnum'] == 9]
elif t == 'MG':
out[t] = atom_dict[atom_dict['atomicnum'] == 12]
elif t == 'P':
out[t] = atom_dict[atom_dict['atomicnum'] == 15]
elif t == 'SA':
out[t] = atom_dict[atom_dict['atomicnum'] == 16 & atom_dict['isacceptor']]
elif t == 'S':
out[t] = atom_dict[atom_dict['atomicnum'] == 16 & ~atom_dict['isacceptor']]
elif t == 'CL':
out[t] = atom_dict[atom_dict['atomicnum'] == 17]
elif t == 'CA':
out[t] = atom_dict[atom_dict['atomicnum'] == 20]
elif t == 'MN':
out[t] = atom_dict[atom_dict['atomicnum'] == 25]
elif t == 'FE':
out[t] = atom_dict[atom_dict['atomicnum'] == 26]
elif t == 'CU':
out[t] = atom_dict[atom_dict['atomicnum'] == 29]
elif t == 'ZN':
out[t] = atom_dict[atom_dict['atomicnum'] == 30]
elif t == 'BR':
out[t] = atom_dict[atom_dict['atomicnum'] == 35]
elif t == 'I':
out[t] = atom_dict[atom_dict['atomicnum'] == 53]
else:
raise ValueError('Unsopported atom type: %s' % t)
return out
[docs]class close_contacts(object):
def __init__(self, protein = None, cutoff = 4, mode = 'atomic_nums', ligand_types = None, protein_types = None, aligned_pairs = False):
"""Close contacts descriptor which tallies atoms of type X in certain cutoff from atoms of type Y.
Parameters
----------
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
cutoff: int (default=4)
Cutoff for atoms in Angstroms
mode: string (default='atomic_nums')
Method of atoms selection, as used in `atoms_by_type`
ligand_types: array
List of ligand atom types to use
protein_types: array
List of protein atom types to use
aligned_pairs: bool (default=False)
Flag indicating should permutation of types should be done, otherwise the atoms are treated as aligned pairs.
"""
self.cutoff = cutoff
self.protein = protein
self.ligand_types = ligand_types
self.protein_types = protein_types if protein_types else ligand_types
self.aligned_pairs = aligned_pairs
self.mode = mode
[docs] def build(self, ligands, protein = None, single = False):
"""Builds descriptors for series of ligands
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
single: bool (default=False)
Flag indicating if the ligand is single.
"""
if protein is None:
protein = self.protein
if single and type(ligands) is not list:
ligands = [ligands]
# prot_dict = atoms_by_type(protein.atom_dict, self.protein_types, self.mode)
desc_size = len(self.ligand_types) if self.aligned_pairs else len(self.ligand_types)*len(self.protein_types)
out = np.zeros(desc_size, dtype=int)
for mol in ligands:
# mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types, self.mode)
if self.aligned_pairs:
#desc = np.array([(distance(prot_dict[str(prot_type)]['coords'], mol_dict[str(mol_type)]['coords']) <= self.cutoff).sum() for mol_type, prot_type in zip(self.ligand_types, self.protein_types)], dtype=int)
# this must be LAZY!
desc = np.array([(distance(atoms_by_type(protein.atom_dict, [prot_type], self.mode)[prot_type]['coords'], atoms_by_type(mol.atom_dict, [mol_type], self.mode)[mol_type]['coords']) <= self.cutoff).sum() for mol_type, prot_type in zip(self.ligand_types, self.protein_types)], dtype=int)
else:
desc = np.array([(distance(atoms_by_type(protein.atom_dict, [prot_type], self.mode)[prot_type]['coords'], atoms_by_type(mol.atom_dict, [mol_type], self.mode)[mol_type]['coords']) <= self.cutoff).sum() for mol_type in self.ligand_types for prot_type in self.protein_types], dtype=int)
out = np.vstack((out, desc))
return out[1:]
def __reduce__(self):
return close_contacts, (None, self.cutoff, self.mode, self.ligand_types, self.protein_types, self.aligned_pairs)
[docs]class fingerprints(object):
def __init__(self, fp = 'fp2', toolkit = 'ob'):
self.fp = fp
self.exchange = False
#if toolkit == oddt.toolkit.backend:
# self.exchange = False
#else:
# self.exchange = True
# self.target_toolkit = __import__('toolkits.'+toolkit)
def _get_fingerprint(self, mol):
if self.exchange:
mol = self.target_toolkit.Molecule(mol)
return mol.calcfp(self.fp).raw
[docs] def build(self, mols, single = False):
if single:
mols = [mols]
out = None
for mol in mols:
fp = self._get_fingerprint(mol)
if out is None:
out = np.zeros_like(fp)
out = np.vstack((fp, out))
return out[1:]
def __reduce__(self):
return fingerprints, ()