Adding abstract superclasses (0df22e3f) · Commits · 钟慕尧 / deepchem

deepchem/dock/init.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -5,8 +5,10 @@ from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		from deepchem.dock.pose_generation import PoseGenerator
		from deepchem.dock.pose_generation import VinaPoseGenerator
		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

+4 −4

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ from deepchem.feat import GridFeaturizer
		from deepchem.data import DiskDataset
		from deepchem.models import SklearnModel
		from deepchem.models import TensorflowMultiTaskRegressor
		from deepchem.dock.pose_scoring import PoseScorer
		from deepchem.dock.pose_scoring import GridPoseScorer
		from deepchem.dock.pose_generation import VinaPoseGenerator
		from sklearn.ensemble import RandomForestRegressor
		from subprocess import call
		@@ -27,7 +27,7 @@ class Docker(object):
		def dock(self, protein_file, ligand_file):
		raise NotImplementedError

		class VinaGridRFDocker(object):
		class VinaGridRFDocker(Docker):
		"""Vina pose-generation, RF-models on grid-featurization of complexes."""

		def __init__(self):
		@@ -43,7 +43,7 @@ class VinaGridRFDocker(object):
		model = SklearnModel(model_dir=self.model_dir)
		model.reload()

		self.pose_scorer = PoseScorer(model, feat="grid")
		self.pose_scorer = GridPoseScorer(model, feat="grid")
		self.pose_generator = VinaPoseGenerator()

		def dock(self, protein_file, ligand_file):
		@@ -73,7 +73,7 @@ class VinaGridDNNDocker(object):
		learning_rate=0.0003, weight_init_stddevs=[.1], batch_size=64)
		model.reload()

		self.pose_scorer = PoseScorer(model, feat="grid")
		self.pose_scorer = GridPoseScorer(model, feat="grid")
		self.pose_generator = VinaPoseGenerator()

		def dock(self, protein_file, ligand_file):

+8 −1

Original line number	Diff line number	Diff line
		@@ -16,6 +16,13 @@ import tempfile
		from deepchem.feat import hydrogenate_and_compute_partial_charges
		from subprocess import call

		class PoseGenerator(object):
		"""Abstract superclass for all pose-generation routines."""

		def generate_poses(self, protein_file, ligand_file, out_dir=None):
		"""Generates the docked complex and outputs files for docked complex."""
		raise NotImplementedError

		def write_conf(receptor_filename, ligand_filename, centroid, box_dims,
		conf_filename, exhaustiveness=None):
		"""Writes Vina configuration file to disk."""
		@@ -48,7 +55,7 @@ def get_molecule_data(pybel_molecule):
		return protein_centroid, protein_range


		class VinaPoseGenerator(object):
		class VinaPoseGenerator(PoseGenerator):

		def __init__(self, exhaustiveness=1):
		"""Initializes Vina Pose generation"""

+7 −0

Original line number	Diff line number	Diff line
		@@ -17,6 +17,13 @@ from deepchem.data import NumpyDataset
		from subprocess import call

		class PoseScorer(object):
		"""Abstract superclass for all scoring methods."""

		def score(self, protein_file, ligand_file):
		"""Returns a score for a protein/ligand pair."""
		raise NotImplementedError

		class GridPoseScorer(object):

		def __init__(self, model, feat="grid"):
		"""Initializes a pose-scorer."""

+2 −2

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		@@ -39,7 +39,7 @@ class TestPoseScoring(unittest.TestCase):
		print("About to fit model on core set")
		model.fit(self.core_dataset)

		pose_scorer = dc.dock.PoseScorer(model, feat="grid")
		pose_scorer = dc.dock.GridPoseScorer(model, feat="grid")

		def test_pose_scorer_score(self):
		"""Tests that scores are generated"""
		@@ -52,6 +52,6 @@ class TestPoseScoring(unittest.TestCase):
		print("About to fit model on core set")
		model.fit(self.core_dataset)

		pose_scorer = dc.dock.PoseScorer(model, feat="grid")
		pose_scorer = dc.dock.GridPoseScorer(model, feat="grid")
		score = pose_scorer.score(protein_file, ligand_file)
		assert score.shape == (1,)