code stringlengths 3 6.57k |
|---|
merge_boxes(box, merge_box) |
new_boxes.append(merged) |
set(mapping[box]) |
union(set(mapping[merge_box]) |
outputs.append(box) |
BindingPocketFinder(object) |
find_pockets(self, protein_file, ligand_file) |
ConvexHullPocketFinder(BindingPocketFinder) |
__init__(self, pad=5) |
find_all_pockets(self, protein_file) |
is (N, 3) |
rdkit_util.load_molecule(protein_file) |
get_all_boxes(coords, self.pad) |
find_pockets(self, protein_file, ligand_file) |
get_all_boxes(protein_coords, self.pad) |
boxes_to_atoms(protein_coords, boxes) |
merge_overlapping_boxes(mapping, boxes) |
np.zeros((len(atoms) |
enumerate(atoms) |
pocket_coords.append(coords) |
RFConvexHullPocketFinder(BindingPocketFinder) |
__init__(self, pad=5) |
ConvexHullPocketFinder(pad) |
tempfile.mkdtemp() |
print("About to download trained model.") |
TODO(rbharath) |
split() |
call(("tar -zxvf pocket_random_refined_RF.tar.gz") |
split() |
call(("mv pocket_random_refined_RF %s" % (self.base_dir) |
split() |
os.path.join(self.base_dir, "pocket_random_refined_RF") |
SklearnModel(model_dir=self.model_dir) |
self.model.reload() |
BindingPocketFeaturizer() |
CircularFingerprint(size=1024) |
find_pockets(self, protein_file, ligand_file) |
TODO(rbharath) |
ligand_file.endswith(".sdf") |
ValueError("Only .sdf ligand files can be featurized.") |
os.path.basename(ligand_file) |
split(".") |
ob.OBConversion() |
obConversion.SetInAndOutFormats(str("sdf") |
str("mol2") |
ob.OBMol() |
obConversion.ReadFile(ob_mol, str(ligand_file) |
obConversion.WriteFile(ob_mol, str(ligand_mol2) |
Chem.MolFromMol2File(str(ligand_mol2) |
self.ligand_featurizer.featurize([mol]) |
len(pockets) |
np.zeros((n_pockets, n_pocket_features+n_ligand_features) |
NumpyDataset(X=features) |
self.model.predict(dataset) |
np.squeeze(self.model.predict_proba(dataset) |
range(len(pockets) |
TODO(rbharath) |
active_pockets.append(pocket) |
active_pocket_coords.append(pocket_coords[pocket_ind]) |
TODO(LESWING) |
ValueError("Karl Implement") |
LoadTest(test.TestCase) |
cycle(self, obj) |
tempfile.mkdtemp(prefix=self.get_temp_dir() |
save.save(obj, path, signatures={}) |
load.load(path) |
test_structure_import(self) |
tracking.Checkpointable() |
tracking.Checkpointable() |
tracking.Checkpointable() |
tracking.Checkpointable() |
self.cycle(root) |
self.assertIs(imported.dep_three, imported.dep_two.dep) |
self.assertIsNot(imported.dep_one, imported.dep_two) |
test_variables(self) |
tracking.Checkpointable() |
variables.Variable(1., trainable=True) |
variables.Variable(2., trainable=False) |
self.cycle(root) |
self.assertEquals(imported.v1.numpy() |
self.assertTrue(imported.v1.trainable) |
self.assertEquals(imported.v2.numpy() |
self.assertFalse(imported.v2.trainable) |
test_capture_variables(self) |
tracking.Checkpointable() |
variables.Variable(2.) |
tensor_spec.TensorSpec(None, dtypes.float32) |
self.cycle(root) |
self.assertEqual(4., imported.f(constant_op.constant(2.) |
numpy() |
imported.weights.assign(4.0) |
self.assertEqual(8., imported.f(constant_op.constant(2.) |
numpy() |
_make_asset(self, contents) |
tempfile.mktemp(prefix=self.get_temp_dir() |
open(filename, "w") |
f.write(contents) |
test_assets(self) |
self._make_asset("contents 1") |
self._make_asset("contents 2") |
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