merge conflice (771bad01) · Commits · 钟慕尧 / deepchem

README.md

+1 −1

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		@@ -59,7 +59,7 @@ git clone https://github.com/deepchem/deepchem.git # Clone deepchem source
		cd deepchem
		bash scripts/install_deepchem_conda.sh deepchem
		source activate deepchem
		yes \| pip install tensorflow-gpu==1.4.0 # If you want GPU support
		yes \| pip install tensorflow-gpu==1.5.0 # If you want GPU support
		python setup.py install # Manual install
		nosetests -a '!slow' -v deepchem --nologcapture # Run tests
		```

deepchem/dock/init.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -11,6 +11,5 @@ from deepchem.dock.pose_scoring import PoseScorer
		from deepchem.dock.pose_scoring import GridPoseScorer
		from deepchem.dock.docking import Docker
		from deepchem.dock.docking import VinaGridRFDocker
		from deepchem.dock.docking import VinaGridDNNDocker
		from deepchem.dock.binding_pocket import ConvexHullPocketFinder
		from deepchem.dock.binding_pocket import RFConvexHullPocketFinder

deepchem/dock/docking.py

+6 −4

Original line number	Diff line number	Diff line
		@@ -14,7 +14,7 @@ import os
		import tempfile
		from deepchem.data import DiskDataset
		from deepchem.models import SklearnModel
		from deepchem.models import TensorflowMultiTaskRegressor
		from deepchem.models import MultiTaskRegressor
		from deepchem.dock.pose_scoring import GridPoseScorer
		from deepchem.dock.pose_generation import VinaPoseGenerator
		from sklearn.ensemble import RandomForestRegressor
		@@ -71,6 +71,7 @@ class VinaGridRFDocker(Docker):
		return (score, (protein_docked, ligand_docked))


		'''
		class VinaGridDNNDocker(object):
		"""Vina pose-generation, DNN-models on grid-featurization of complexes."""

		@@ -88,14 +89,14 @@ class VinaGridDNNDocker(object):
		# Fit model on dataset
		pdbbind_tasks = ["-logKd/Ki"]
		n_features = 2052
		model = TensorflowMultiTaskRegressor(
		model = MultiTaskRegressor(
		len(pdbbind_tasks),
		n_features,
		logdir=self.model_dir,
		dropouts=[.25],
		learning_rate=0.0003,
		weight_init_stddevs=[.1],
		batch_size=64)
		batch_size=64,
		model_dir=self.model_dir)
		model.reload()

		self.pose_scorer = GridPoseScorer(model, feat="grid")
		@@ -116,3 +117,4 @@ class VinaGridDNNDocker(object):
		else:
		score = np.zeros((1,))
		return (score, (protein_docked, ligand_docked))
		'''

deepchem/dock/tests/test_docking.py

+4 −0

Original line number	Diff line number	Diff line
		@@ -34,6 +34,7 @@ class TestDocking(unittest.TestCase):
		return
		docker = dc.dock.VinaGridRFDocker(exhaustiveness=1, detect_pockets=True)

		'''
		@attr("slow")
		def test_vina_grid_dnn_docker_init(self):
		"""Test that VinaGridDNNDocker can be initialized."""
		@@ -44,6 +45,7 @@ class TestDocking(unittest.TestCase):
		if sys.version_info >= (3, 0):
		return
		docker = dc.dock.VinaGridDNNDocker(exhaustiveness=1, detect_pockets=True)
		'''

		@attr("slow")
		def test_vina_grid_rf_docker_dock(self):
		@@ -104,6 +106,7 @@ class TestDocking(unittest.TestCase):

		assert score.shape == (1,)

		'''
		@attr("slow")
		def test_vina_grid_dnn_docker_dock(self):
		"""Test that VinaGridDNNDocker can dock."""
		@@ -136,3 +139,4 @@ class TestDocking(unittest.TestCase):

		# Check returned files exist
		assert score.shape == (1,)
		'''

deepchem/feat/graph_features.py

+88 −68

Original line number	Diff line number	Diff line
		@@ -12,8 +12,8 @@ from deepchem.feat.mol_graphs import ConvMol, WeaveMol

		def one_of_k_encoding(x, allowable_set):
		if x not in allowable_set:
		raise Exception(
		"input {0} not in allowable set{1}:".format(x, allowable_set))
		raise Exception("input {0} not in allowable set{1}:".format(
		x, allowable_set))
		return list(map(lambda s: x == s, allowable_set))


		@@ -247,13 +247,25 @@ def find_distance(a1, num_atoms, canon_adj_list, max_distance=7):


		class ConvMolFeaturizer(Featurizer):

		name = ['conv_mol']

		def __init__(self):
		# Since ConvMol is an object and not a numpy array, need to set dtype to
		# object.
		def __init__(self, master_atom=False):
		"""
		Parameters
		----------
		master_atom: Boolean
		if true create a fake atom with bonds to every other atom.
		the initialization is the mean of the other atom features in
		the molecule. This technique is briefly discussed in
		Neural Message Passing for Quantum Chemistry
		https://arxiv.org/pdf/1704.01212.pdf


		Since ConvMol is an object and not a numpy array, need to set dtype to
		object.
		"""
		self.dtype = object
		self.master_atom = master_atom

		def _featurize(self, mol):
		"""Encodes mol as a ConvMol object."""
		@@ -264,10 +276,14 @@ class ConvMolFeaturizer(Featurizer):

		# Stack nodes into an array
		nodes = np.vstack(nodes)
		if self.master_atom:
		master_atom_features = np.expand_dims(np.mean(nodes, axis=0), axis=0)
		nodes = np.concatenate([nodes, master_atom_features], axis=0)

		# Get bond lists with reverse edges included
		edge_list = [(b.GetBeginAtomIdx(), b.GetEndAtomIdx())
		for b in mol.GetBonds()]
		edge_list = [
		(b.GetBeginAtomIdx(), b.GetEndAtomIdx()) for b in mol.GetBonds()
		]

		# Get canonical adjacency list
		canon_adj_list = [[] for mol_id in range(len(nodes))]
		@@ -275,11 +291,15 @@ class ConvMolFeaturizer(Featurizer):
		canon_adj_list[edge[0]].append(edge[1])
		canon_adj_list[edge[1]].append(edge[0])

		if self.master_atom:
		fake_atom_index = len(nodes) - 1
		for index in range(len(nodes) - 1):
		canon_adj_list[index].append(fake_atom_index)

		return ConvMol(nodes, canon_adj_list)


		class WeaveFeaturizer(Featurizer):

		name = ['weave_mol']

		def __init__(self, graph_distance=True, explicit_H=False):

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