Loading deepchem/dock/__init__.py +1 −0 Original line number Diff line number Diff line # flake8: noqa from deepchem.dock.pose_generation import PoseGenerator from deepchem.dock.pose_generation import VinaPoseGenerator from deepchem.dock.pose_generation import GninaPoseGenerator from deepchem.dock.docking import Docker from deepchem.dock.binding_pocket import ConvexHullPocketFinder deepchem/dock/pose_generation.py +169 −7 Original line number Diff line number Diff line Loading @@ -7,7 +7,7 @@ import os import tempfile import tarfile import numpy as np from subprocess import call from subprocess import call, Popen, PIPE from subprocess import check_output from typing import List, Optional, Tuple, Union Loading @@ -16,7 +16,7 @@ from deepchem.utils.data_utils import download_url, get_data_dir from deepchem.utils.typing import RDKitMol from deepchem.utils.geometry_utils import compute_centroid, compute_protein_range from deepchem.utils.rdkit_utils import load_molecule, write_molecule from deepchem.utils.vina_utils import load_docked_ligands, write_vina_conf from deepchem.utils.vina_utils import load_docked_ligands, write_vina_conf, write_gnina_conf, read_gnina_log logger = logging.getLogger(__name__) DOCKED_POSES = List[Tuple[RDKitMol, RDKitMol]] Loading Loading @@ -53,8 +53,8 @@ class PoseGenerator(object): centroid: np.ndarray, optional (default None) The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional (default None) A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 5 angstroms. A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 5 angstroms. exhaustiveness: int, optional (default 10) Tells pose generator how exhaustive it should be with pose generation. Loading @@ -79,6 +79,167 @@ class PoseGenerator(object): raise NotImplementedError class GninaPoseGenerator(PoseGenerator): """Use GNINA to generate binding poses. This class uses GNINA (a deep learning framework for molecular docking) to generate binding poses. It downloads the GNINA executable to DEEPCHEM_DATA_DIR (an environment variable you set) and invokes the executable to perform pose generation. GNINA uses pre-trained convolutional neural network (CNN) scoring functions to rank binding poses based on learned representations of 3D protein-ligand interactions. It has been shown to outperform AutoDock Vina in virtual screening applications [1]_. If you use the GNINA molecular docking engine, please cite the relevant papers: https://github.com/gnina/gnina#citation The primary citation for GNINA is [1]_. References ---------- .. [1] M Ragoza, J Hochuli, E Idrobo, J Sunseri, DR Koes. "Protein–Ligand Scoring with Convolutional Neural Networks." Journal of chemical information and modeling (2017). Notes ----- * GNINA requires CUDA >= 10.1 for fast CNN scoring. * Almost all dependencies are included in the most compatible way possible, which reduces performance. Build GNINA from source for production use. """ def __init__(self): """Initialize GNINA pose generator.""" data_dir = get_data_dir() if platform.system() == 'Linux': url = "https://github.com/gnina/gnina/releases/download/v1.0/gnina" filename = 'gnina' self.gnina_dir = data_dir self.gnina_cmd = os.path.join(self.gnina_dir, filename) else: raise ValueError( "Unknown operating system. Try using a cloud platform to run this code instead." ) if not os.path.exists(self.gnina_cmd): logger.info("GNINA not available. Downloading...") download_url(url, data_dir) downloaded_file = os.path.join(data_dir, filename) os.chmod(downloaded_file, 755) logger.info("Downloaded GNINA.") def generate_poses( self, molecular_complex: Tuple[str, str], centroid: Optional[np.ndarray] = None, box_dims: Optional[np.ndarray] = None, exhaustiveness: int = 10, num_modes: int = 9, num_pockets: Optional[int] = None, out_dir: Optional[str] = None, generate_scores: bool = True, **kwargs) -> Union[Tuple[DOCKED_POSES, List[float]], DOCKED_POSES]: """Generates the docked complex and outputs files for docked complex. Parameters ---------- molecular_complexes: Tuple[str, str] A representation of a molecular complex. This tuple is (protein_file, ligand_file). centroid: np.ndarray, optional (default None) The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional (default None) A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 4 angstroms. exhaustiveness: int (default 8) Tells GNINA how exhaustive it should be with pose generation. num_modes: int (default 9) Tells GNINA how many binding modes it should generate at each invocation. out_dir: str, optional If specified, write generated poses to this directory. generate_scores: bool, optional (default True) If `True`, the pose generator will return scores for complexes. This is used typically when invoking external docking programs that compute scores. kwargs: Any args supported by GNINA as documented https://github.com/gnina/gnina#usage Returns ------- Tuple[`docked_poses`, `scores`] or `docked_poses` Tuple of `(docked_poses, scores)` or `docked_poses`. `docked_poses` is a list of docked molecular complexes. Each entry in this list contains a `(protein_mol, ligand_mol)` pair of RDKit molecules. `scores` is an array of binding affinities (kcal/mol), CNN pose scores, and CNN affinities predicted by GNINA. """ if out_dir is None: out_dir = tempfile.mkdtemp() if not os.path.exists(out_dir): os.makedirs(out_dir) # Parse complex if len(molecular_complex) > 2: raise ValueError( "GNINA can only dock protein-ligand complexes and not more general molecular complexes." ) (protein_file, ligand_file) = molecular_complex # check filetypes if not protein_file.endswith('.pdb'): raise ValueError('Protein file must be in .pdb format.') if not ligand_file.endswith('.sdf'): raise ValueError('Ligand file must be in .sdf format.') protein_mol = load_molecule( protein_file, calc_charges=True, add_hydrogens=True) ligand_name = os.path.basename(ligand_file).split(".")[0] # Define locations of log and output files log_file = os.path.join(out_dir, "%s_log.txt" % ligand_name) out_file = os.path.join(out_dir, "%s_docked.pdbqt" % ligand_name) logger.info("About to call GNINA.") # Write GNINA conf file conf_file = os.path.join(out_dir, "conf.txt") write_gnina_conf( protein_filename=protein_file, ligand_filename=ligand_file, conf_filename=conf_file, num_modes=num_modes, exhaustiveness=exhaustiveness, **kwargs) # Run GNINA args = [ self.gnina_cmd, "--config", conf_file, "--log", log_file, "--out", out_file ] process = Popen(args, stdout=PIPE, stderr=PIPE) stdout, stderr = process.communicate() stdout_decoded = stdout.decode() # read output and log ligands, _ = load_docked_ligands(out_file) docked_complexes = [(protein_mol[1], ligand) for ligand in ligands] scores = read_gnina_log(log_file) if generate_scores: return docked_complexes, scores else: return docked_complexes class VinaPoseGenerator(PoseGenerator): """Uses Autodock Vina to generate binding poses. Loading Loading @@ -157,7 +318,7 @@ class VinaPoseGenerator(PoseGenerator): num_modes: int = 9, num_pockets: Optional[int] = None, out_dir: Optional[str] = None, generate_scores: bool = False generate_scores: Optional[bool] = False ) -> Union[Tuple[DOCKED_POSES, List[float]], DOCKED_POSES]: """Generates the docked complex and outputs files for docked complex. Loading @@ -168,7 +329,8 @@ class VinaPoseGenerator(PoseGenerator): ---------- molecular_complexes: Tuple[str, str] A representation of a molecular complex. This tuple is (protein_file, ligand_file). (protein_file, ligand_file). The protein should be a pdb file and the ligand should be an sdf file. centroid: np.ndarray, optional The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional Loading Loading @@ -263,7 +425,7 @@ class VinaPoseGenerator(PoseGenerator): centroids = centroids[:num_pockets] dimensions = dimensions[:num_pockets] # Prepare protein # Prepare ligand ligand_name = os.path.basename(ligand_file).split(".")[0] ligand_pdbqt = os.path.join(out_dir, "%s.pdbqt" % ligand_name) Loading deepchem/dock/tests/test_pose_generation.py +33 −0 Original line number Diff line number Diff line Loading @@ -23,6 +23,11 @@ class TestPoseGeneration(unittest.TestCase): """Test that VinaPoseGenerator can be initialized.""" dc.dock.VinaPoseGenerator() @unittest.skipIf(IS_WINDOWS, 'Skip the test on Windows') def test_gnina_initialization(self): """Test that GninaPoseGenerator can be initialized.""" dc.dock.GninaPoseGenerator() @unittest.skipIf(IS_WINDOWS, 'Skip the test on Windows') def test_pocket_vina_initialization(self): """Test that VinaPoseGenerator can be initialized.""" Loading Loading @@ -58,6 +63,34 @@ class TestPoseGeneration(unittest.TestCase): assert isinstance(protein, Chem.Mol) assert isinstance(ligand, Chem.Mol) @pytest.mark.slow def test_gnina_poses_and_scores(self): """Test that GninaPoseGenerator generates poses and scores This test takes some time to run, about 3 minutes on development laptop. """ # Let's turn on logging since this test will run for a while logging.basicConfig(level=logging.INFO) current_dir = os.path.dirname(os.path.realpath(__file__)) protein_file = os.path.join(current_dir, "1jld_protein.pdb") ligand_file = os.path.join(current_dir, "1jld_ligand.sdf") gpg = dc.dock.GninaPoseGenerator() with tempfile.TemporaryDirectory() as tmp: poses, scores = gpg.generate_poses( (protein_file, ligand_file), exhaustiveness=1, num_modes=1, out_dir=tmp) assert len(poses) == 1 assert len(scores) == 1 protein, ligand = poses[0] from rdkit import Chem assert isinstance(protein, Chem.Mol) assert isinstance(ligand, Chem.Mol) @pytest.mark.slow def test_vina_poses_no_scores(self): """Test that VinaPoseGenerator generates poses. Loading deepchem/utils/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -84,6 +84,8 @@ from deepchem.utils.pdbqt_utils import convert_protein_to_pdbqt from deepchem.utils.pdbqt_utils import convert_mol_to_pdbqt from deepchem.utils.vina_utils import write_vina_conf from deepchem.utils.vina_utils import write_gnina_conf from deepchem.utils.vina_utils import read_gnina_log from deepchem.utils.vina_utils import load_docked_ligands from deepchem.utils.vina_utils import prepare_inputs Loading deepchem/utils/test/data/gnina_log.txt 0 → 100644 +25 −0 Original line number Diff line number Diff line _ (_) __ _ _ __ _ _ __ __ _ / _` | '_ \| | '_ \ / _` | | (_| | | | | | | | | (_| | \__, |_| |_|_|_| |_|\__,_| __/ | |___/ gnina master:b4a640e Built Jan 28 2021. gnina is based on smina and AutoDock Vina. Please cite appropriately. WARNING: No GPU detected. CNN scoring will be slow. Recommend running with single model (--cnn crossdock_default2018) or without cnn scoring (--cnn_scoring=none). Commandline: /tmp/gnina --autobox_ligand 10gs_rec.pdb --config conf.txt --out docked.pdbqt --log log.txt Using random seed: 2128721554 mode | affinity | CNN | CNN | (kcal/mol) | pose score | affinity -----+------------+------------+---------- 1 -4.37 0.6392 4.336 2 -3.56 0.6202 4.162 Loading
deepchem/dock/__init__.py +1 −0 Original line number Diff line number Diff line # flake8: noqa from deepchem.dock.pose_generation import PoseGenerator from deepchem.dock.pose_generation import VinaPoseGenerator from deepchem.dock.pose_generation import GninaPoseGenerator from deepchem.dock.docking import Docker from deepchem.dock.binding_pocket import ConvexHullPocketFinder
deepchem/dock/pose_generation.py +169 −7 Original line number Diff line number Diff line Loading @@ -7,7 +7,7 @@ import os import tempfile import tarfile import numpy as np from subprocess import call from subprocess import call, Popen, PIPE from subprocess import check_output from typing import List, Optional, Tuple, Union Loading @@ -16,7 +16,7 @@ from deepchem.utils.data_utils import download_url, get_data_dir from deepchem.utils.typing import RDKitMol from deepchem.utils.geometry_utils import compute_centroid, compute_protein_range from deepchem.utils.rdkit_utils import load_molecule, write_molecule from deepchem.utils.vina_utils import load_docked_ligands, write_vina_conf from deepchem.utils.vina_utils import load_docked_ligands, write_vina_conf, write_gnina_conf, read_gnina_log logger = logging.getLogger(__name__) DOCKED_POSES = List[Tuple[RDKitMol, RDKitMol]] Loading Loading @@ -53,8 +53,8 @@ class PoseGenerator(object): centroid: np.ndarray, optional (default None) The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional (default None) A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 5 angstroms. A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 5 angstroms. exhaustiveness: int, optional (default 10) Tells pose generator how exhaustive it should be with pose generation. Loading @@ -79,6 +79,167 @@ class PoseGenerator(object): raise NotImplementedError class GninaPoseGenerator(PoseGenerator): """Use GNINA to generate binding poses. This class uses GNINA (a deep learning framework for molecular docking) to generate binding poses. It downloads the GNINA executable to DEEPCHEM_DATA_DIR (an environment variable you set) and invokes the executable to perform pose generation. GNINA uses pre-trained convolutional neural network (CNN) scoring functions to rank binding poses based on learned representations of 3D protein-ligand interactions. It has been shown to outperform AutoDock Vina in virtual screening applications [1]_. If you use the GNINA molecular docking engine, please cite the relevant papers: https://github.com/gnina/gnina#citation The primary citation for GNINA is [1]_. References ---------- .. [1] M Ragoza, J Hochuli, E Idrobo, J Sunseri, DR Koes. "Protein–Ligand Scoring with Convolutional Neural Networks." Journal of chemical information and modeling (2017). Notes ----- * GNINA requires CUDA >= 10.1 for fast CNN scoring. * Almost all dependencies are included in the most compatible way possible, which reduces performance. Build GNINA from source for production use. """ def __init__(self): """Initialize GNINA pose generator.""" data_dir = get_data_dir() if platform.system() == 'Linux': url = "https://github.com/gnina/gnina/releases/download/v1.0/gnina" filename = 'gnina' self.gnina_dir = data_dir self.gnina_cmd = os.path.join(self.gnina_dir, filename) else: raise ValueError( "Unknown operating system. Try using a cloud platform to run this code instead." ) if not os.path.exists(self.gnina_cmd): logger.info("GNINA not available. Downloading...") download_url(url, data_dir) downloaded_file = os.path.join(data_dir, filename) os.chmod(downloaded_file, 755) logger.info("Downloaded GNINA.") def generate_poses( self, molecular_complex: Tuple[str, str], centroid: Optional[np.ndarray] = None, box_dims: Optional[np.ndarray] = None, exhaustiveness: int = 10, num_modes: int = 9, num_pockets: Optional[int] = None, out_dir: Optional[str] = None, generate_scores: bool = True, **kwargs) -> Union[Tuple[DOCKED_POSES, List[float]], DOCKED_POSES]: """Generates the docked complex and outputs files for docked complex. Parameters ---------- molecular_complexes: Tuple[str, str] A representation of a molecular complex. This tuple is (protein_file, ligand_file). centroid: np.ndarray, optional (default None) The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional (default None) A numpy array of shape `(3,)` holding the size of the box to dock. If not specified is set to size of molecular complex plus 4 angstroms. exhaustiveness: int (default 8) Tells GNINA how exhaustive it should be with pose generation. num_modes: int (default 9) Tells GNINA how many binding modes it should generate at each invocation. out_dir: str, optional If specified, write generated poses to this directory. generate_scores: bool, optional (default True) If `True`, the pose generator will return scores for complexes. This is used typically when invoking external docking programs that compute scores. kwargs: Any args supported by GNINA as documented https://github.com/gnina/gnina#usage Returns ------- Tuple[`docked_poses`, `scores`] or `docked_poses` Tuple of `(docked_poses, scores)` or `docked_poses`. `docked_poses` is a list of docked molecular complexes. Each entry in this list contains a `(protein_mol, ligand_mol)` pair of RDKit molecules. `scores` is an array of binding affinities (kcal/mol), CNN pose scores, and CNN affinities predicted by GNINA. """ if out_dir is None: out_dir = tempfile.mkdtemp() if not os.path.exists(out_dir): os.makedirs(out_dir) # Parse complex if len(molecular_complex) > 2: raise ValueError( "GNINA can only dock protein-ligand complexes and not more general molecular complexes." ) (protein_file, ligand_file) = molecular_complex # check filetypes if not protein_file.endswith('.pdb'): raise ValueError('Protein file must be in .pdb format.') if not ligand_file.endswith('.sdf'): raise ValueError('Ligand file must be in .sdf format.') protein_mol = load_molecule( protein_file, calc_charges=True, add_hydrogens=True) ligand_name = os.path.basename(ligand_file).split(".")[0] # Define locations of log and output files log_file = os.path.join(out_dir, "%s_log.txt" % ligand_name) out_file = os.path.join(out_dir, "%s_docked.pdbqt" % ligand_name) logger.info("About to call GNINA.") # Write GNINA conf file conf_file = os.path.join(out_dir, "conf.txt") write_gnina_conf( protein_filename=protein_file, ligand_filename=ligand_file, conf_filename=conf_file, num_modes=num_modes, exhaustiveness=exhaustiveness, **kwargs) # Run GNINA args = [ self.gnina_cmd, "--config", conf_file, "--log", log_file, "--out", out_file ] process = Popen(args, stdout=PIPE, stderr=PIPE) stdout, stderr = process.communicate() stdout_decoded = stdout.decode() # read output and log ligands, _ = load_docked_ligands(out_file) docked_complexes = [(protein_mol[1], ligand) for ligand in ligands] scores = read_gnina_log(log_file) if generate_scores: return docked_complexes, scores else: return docked_complexes class VinaPoseGenerator(PoseGenerator): """Uses Autodock Vina to generate binding poses. Loading Loading @@ -157,7 +318,7 @@ class VinaPoseGenerator(PoseGenerator): num_modes: int = 9, num_pockets: Optional[int] = None, out_dir: Optional[str] = None, generate_scores: bool = False generate_scores: Optional[bool] = False ) -> Union[Tuple[DOCKED_POSES, List[float]], DOCKED_POSES]: """Generates the docked complex and outputs files for docked complex. Loading @@ -168,7 +329,8 @@ class VinaPoseGenerator(PoseGenerator): ---------- molecular_complexes: Tuple[str, str] A representation of a molecular complex. This tuple is (protein_file, ligand_file). (protein_file, ligand_file). The protein should be a pdb file and the ligand should be an sdf file. centroid: np.ndarray, optional The centroid to dock against. Is computed if not specified. box_dims: np.ndarray, optional Loading Loading @@ -263,7 +425,7 @@ class VinaPoseGenerator(PoseGenerator): centroids = centroids[:num_pockets] dimensions = dimensions[:num_pockets] # Prepare protein # Prepare ligand ligand_name = os.path.basename(ligand_file).split(".")[0] ligand_pdbqt = os.path.join(out_dir, "%s.pdbqt" % ligand_name) Loading
deepchem/dock/tests/test_pose_generation.py +33 −0 Original line number Diff line number Diff line Loading @@ -23,6 +23,11 @@ class TestPoseGeneration(unittest.TestCase): """Test that VinaPoseGenerator can be initialized.""" dc.dock.VinaPoseGenerator() @unittest.skipIf(IS_WINDOWS, 'Skip the test on Windows') def test_gnina_initialization(self): """Test that GninaPoseGenerator can be initialized.""" dc.dock.GninaPoseGenerator() @unittest.skipIf(IS_WINDOWS, 'Skip the test on Windows') def test_pocket_vina_initialization(self): """Test that VinaPoseGenerator can be initialized.""" Loading Loading @@ -58,6 +63,34 @@ class TestPoseGeneration(unittest.TestCase): assert isinstance(protein, Chem.Mol) assert isinstance(ligand, Chem.Mol) @pytest.mark.slow def test_gnina_poses_and_scores(self): """Test that GninaPoseGenerator generates poses and scores This test takes some time to run, about 3 minutes on development laptop. """ # Let's turn on logging since this test will run for a while logging.basicConfig(level=logging.INFO) current_dir = os.path.dirname(os.path.realpath(__file__)) protein_file = os.path.join(current_dir, "1jld_protein.pdb") ligand_file = os.path.join(current_dir, "1jld_ligand.sdf") gpg = dc.dock.GninaPoseGenerator() with tempfile.TemporaryDirectory() as tmp: poses, scores = gpg.generate_poses( (protein_file, ligand_file), exhaustiveness=1, num_modes=1, out_dir=tmp) assert len(poses) == 1 assert len(scores) == 1 protein, ligand = poses[0] from rdkit import Chem assert isinstance(protein, Chem.Mol) assert isinstance(ligand, Chem.Mol) @pytest.mark.slow def test_vina_poses_no_scores(self): """Test that VinaPoseGenerator generates poses. Loading
deepchem/utils/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -84,6 +84,8 @@ from deepchem.utils.pdbqt_utils import convert_protein_to_pdbqt from deepchem.utils.pdbqt_utils import convert_mol_to_pdbqt from deepchem.utils.vina_utils import write_vina_conf from deepchem.utils.vina_utils import write_gnina_conf from deepchem.utils.vina_utils import read_gnina_log from deepchem.utils.vina_utils import load_docked_ligands from deepchem.utils.vina_utils import prepare_inputs Loading
deepchem/utils/test/data/gnina_log.txt 0 → 100644 +25 −0 Original line number Diff line number Diff line _ (_) __ _ _ __ _ _ __ __ _ / _` | '_ \| | '_ \ / _` | | (_| | | | | | | | | (_| | \__, |_| |_|_|_| |_|\__,_| __/ | |___/ gnina master:b4a640e Built Jan 28 2021. gnina is based on smina and AutoDock Vina. Please cite appropriately. WARNING: No GPU detected. CNN scoring will be slow. Recommend running with single model (--cnn crossdock_default2018) or without cnn scoring (--cnn_scoring=none). Commandline: /tmp/gnina --autobox_ligand 10gs_rec.pdb --config conf.txt --out docked.pdbqt --log log.txt Using random seed: 2128721554 mode | affinity | CNN | CNN | (kcal/mol) | pose score | affinity -----+------------+------------+---------- 1 -4.37 0.6392 4.336 2 -3.56 0.6202 4.162
