def __init__(self, protein=None, n_jobs=-1, version=1, spr=0, **kwargs):
self.protein = protein
self.n_jobs = n_jobs
self.version = version
self.spr = spr
model = randomforest(n_estimators=500,
oob_score=True,
n_jobs=n_jobs,
**kwargs)
if version == 1:
cutoff = 12
descriptors = close_contacts(protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 2:
cutoff = np.array([0, 2, 4, 6, 8, 10, 12])
descriptors = close_contacts(protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 3:
cutoff = 12
cc = close_contacts(protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
vina = autodock_vina_descriptor(protein)
descriptors = ensemble_descriptor((vina, cc))
super(rfscore, self).__init__(model,
descriptors,
score_title='rfscore_v%i' % self.version)
def __init__(self, protein = None, n_jobs = -1, version = 1, spr = 0, **kwargs):
self.protein = protein
self.n_jobs = n_jobs
self.version = version
self.spr = spr
model = randomforest(n_estimators = 500, oob_score = True, n_jobs = n_jobs, **kwargs)
if version == 1:
cutoff = 12
descriptors = close_contacts(protein, cutoff = cutoff, protein_types = protein_atomic_nums, ligand_types = ligand_atomic_nums)
elif version == 2:
cutoff = np.array([ 0, 2, 4, 6, 8, 10, 12])
descriptors = close_contacts(protein, cutoff = cutoff, protein_types = protein_atomic_nums, ligand_types = ligand_atomic_nums)
elif version == 3:
cutoff = 12
cc = close_contacts(protein, cutoff = cutoff, protein_types = protein_atomic_nums, ligand_types = ligand_atomic_nums)
vina = autodock_vina_descriptor(protein)
descriptors = ensemble_descriptor((vina, cc))
super(rfscore,self).__init__(model, descriptors, score_title = 'rfscore')
def __init__(self, protein=None, n_jobs=-1, version=1, spr=0, **kwargs):
self.protein = protein
self.n_jobs = n_jobs
self.version = version
self.spr = spr
if version == 1:
cutoff = 12
mtry = 6
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 2:
cutoff = np.array([0, 2, 4, 6, 8, 10, 12])
mtry = 14
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 3:
cutoff = 12
mtry = 6
cc = close_contacts_descriptor(protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
vina_scores = [
'vina_gauss1', 'vina_gauss2', 'vina_repulsion',
'vina_hydrophobic', 'vina_hydrogen', 'vina_num_rotors'
]
vina = oddt_vina_descriptor(protein, vina_scores=vina_scores)
descriptors = ensemble_descriptor((vina, cc))
model = randomforest(n_estimators=500,
oob_score=True,
n_jobs=n_jobs,
max_features=mtry,
bootstrap=True,
min_samples_split=6,
**kwargs)
super(rfscore, self).__init__(model,
descriptors,
score_title='rfscore_v%i' % self.version)
def test_ensemble_descriptor():
mols = list(oddt.toolkit.readfile('sdf', actives_sdf))[:10]
list(map(lambda x: x.addh(), mols))
rec = next(oddt.toolkit.readfile('pdb', receptor_pdb))
rec.protein = True
rec.addh()
desc1 = rfscore(version=1).descriptor_generator
desc2 = oddt_vina_descriptor()
ensemble = ensemble_descriptor((desc1, desc2))
ensemble.set_protein(rec)
assert len(ensemble) == len(desc1) + len(desc2)
# set protein
assert desc1.protein == rec
assert desc2.protein == rec
ensemble_scores = ensemble.build(mols)
scores1 = desc1.build(mols)
scores2 = desc2.build(mols)
assert_array_almost_equal(ensemble_scores, np.hstack((scores1, scores2)))
def __init__(self, protein=None, n_jobs=-1, version=1, spr=0, **kwargs):
"""Scoring function implementing RF-Score variants. It predicts the
binding affinity (pKi/d) of ligand in a complex utilizng simple
descriptors (close contacts of atoms <12A) with sophisticated
machine-learning model (random forest). The third variand supplements
those contacts with Vina partial scores. For futher details see RF-Score
publications v1[1]_, v2[2]_, v3[3]_.
Parameters
----------
protein : oddt.toolkit.Molecule object
Receptor for the scored ligands
n_jobs: int (default=-1)
Number of cores to use for scoring and training. By default (-1)
all cores are allocated.
version: int (default=1)
Scoring function variant. The deault is the simplest one (v1).
spr: int (default=0)
The minimum number of contacts in each pair of atom types in
the training set for the column to be included in training.
This is a way of removal of not frequent and empty contacts.
References
----------
.. [1] Ballester PJ, Mitchell JBO. A machine learning approach to
predicting protein-ligand binding affinity with applications to
molecular docking. Bioinformatics. 2010;26: 1169-1175.
doi:10.1093/bioinformatics/btq112
.. [2] Ballester PJ, Schreyer A, Blundell TL. Does a more precise
chemical description of protein-ligand complexes lead to more
accurate prediction of binding affinity? J Chem Inf Model. 2014;54:
944-955. doi:10.1021/ci500091r
.. [3] Li H, Leung K-S, Wong M-H, Ballester PJ. Improving AutoDock Vina
Using Random Forest: The Growing Accuracy of Binding Affinity
Prediction by the Effective Exploitation of Larger Data Sets.
Mol Inform. WILEY-VCH Verlag; 2015;34: 115-126.
doi:10.1002/minf.201400132
"""
self.protein = protein
self.n_jobs = n_jobs
self.version = version
self.spr = spr
if version == 1:
cutoff = 12
mtry = 6
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 2:
cutoff = np.array([0, 2, 4, 6, 8, 10, 12])
mtry = 14
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 3:
cutoff = 12
mtry = 6
cc = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
vina_scores = ['vina_gauss1',
'vina_gauss2',
'vina_repulsion',
'vina_hydrophobic',
'vina_hydrogen',
'vina_num_rotors']
vina = oddt_vina_descriptor(protein, vina_scores=vina_scores)
descriptors = ensemble_descriptor((vina, cc))
model = randomforest(n_estimators=500,
oob_score=True,
n_jobs=n_jobs,
max_features=mtry,
bootstrap=True,
min_samples_split=6,
**kwargs)
super(rfscore, self).__init__(model, descriptors,
score_title='rfscore_v%i' % self.version)
def __init__(self, protein=None, n_jobs=-1, version=1, spr=0, **kwargs):
"""Scoring function implementing RF-Score variants. It predicts the
binding affinity (pKi/d) of ligand in a complex utilizng simple
descriptors (close contacts of atoms <12A) with sophisticated
machine-learning model (random forest). The third variand supplements
those contacts with Vina partial scores. For futher details see RF-Score
publications v1[1]_, v2[2]_, v3[3]_.
Parameters
----------
protein : oddt.toolkit.Molecule object
Receptor for the scored ligands
n_jobs: int (default=-1)
Number of cores to use for scoring and training. By default (-1)
all cores are allocated.
version: int (default=1)
Scoring function variant. The deault is the simplest one (v1).
spr: int (default=0)
The minimum number of contacts in each pair of atom types in
the training set for the column to be included in training.
This is a way of removal of not frequent and empty contacts.
References
----------
.. [1] Ballester PJ, Mitchell JBO. A machine learning approach to
predicting protein-ligand binding affinity with applications to
molecular docking. Bioinformatics. 2010;26: 1169-1175.
doi:10.1093/bioinformatics/btq112
.. [2] Ballester PJ, Schreyer A, Blundell TL. Does a more precise
chemical description of protein-ligand complexes lead to more
accurate prediction of binding affinity? J Chem Inf Model. 2014;54:
944-955. doi:10.1021/ci500091r
.. [3] Li H, Leung K-S, Wong M-H, Ballester PJ. Improving AutoDock Vina
Using Random Forest: The Growing Accuracy of Binding Affinity
Prediction by the Effective Exploitation of Larger Data Sets.
Mol Inform. WILEY-VCH Verlag; 2015;34: 115-126.
doi:10.1002/minf.201400132
"""
self.protein = protein
self.n_jobs = n_jobs
self.version = version
self.spr = spr
if version == 1:
cutoff = 12
mtry = 6
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 2:
cutoff = np.array([0, 2, 4, 6, 8, 10, 12])
mtry = 14
descriptors = close_contacts_descriptor(
protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
elif version == 3:
cutoff = 12
mtry = 6
cc = close_contacts_descriptor(protein,
cutoff=cutoff,
protein_types=protein_atomic_nums,
ligand_types=ligand_atomic_nums)
vina_scores = [
'vina_gauss1', 'vina_gauss2', 'vina_repulsion',
'vina_hydrophobic', 'vina_hydrogen', 'vina_num_rotors'
]
vina = oddt_vina_descriptor(protein, vina_scores=vina_scores)
descriptors = ensemble_descriptor((vina, cc))
# elif version == 5:
# cutoff = np.array([0, 2, 4, 6, 8, 10, 12])
# mtry = 14
# descriptors = close_contacts_descriptor(
# protein,
# cutoff=cutoff,
# protein_types=protein_atomic_nums,
# ligand_types=ligand_atomic_nums)
model = randomforest(n_estimators=500,
oob_score=True,
n_jobs=n_jobs,
max_features=mtry,
bootstrap=True,
min_samples_split=6,
**kwargs)
super(rfscore, self).__init__(model,
descriptors,
score_title='rfscore_v%i' % self.version)
