Loading deepchem/dock/docking.py +63 −2 Original line number Diff line number Diff line Loading @@ -41,6 +41,69 @@ class Docker(object): (featurizer is None and scoring_model is not None)): raise ValueError( "featurizer/scoring_model must both be set or must both be None.") self.base_dir = tempfile.mkdtemp() self.pose_generator = pose_generator self.featurizer = featurizer self.scoring_model = scoring_model def dock(self, molecular_complex, centroid=None, box_dims=None, exhaustiveness=10, num_modes=9, num_pockets=None, out_dir=None, use_pose_generator_scores=False): """Generic docking function. This docking function uses this object's featurizer, pose generator, and scoring model to make docking predictions. This function is written in generic style so Parameters ---------- molecular_complex: Object Some representation of a molecular complex. exhaustiveness: int, optional (default 10) Tells pose generator how exhaustive it should be with pose generation. num_modes: int, optional (default 9) Tells pose generator how many binding modes it should generate at each invocation. num_pockets: int, optional (default None) If specified, `self.pocket_finder` must be set. Will only generate poses for the first `num_pockets` returned by `self.pocket_finder`. out_dir: str, optional (default None) If specified, write generated poses to this directory. use_pose_generator_scores: bool, optional (default False) If `True`, ask pose generator to generate scores. This cannot be `True` if `self.featurizer` and `self.scoring_model` are set since those will be used to generate scores in that case. Returns ------- A generator. If `use_pose_generator_scores==True` or `self.scoring_model` is set, then will yield tuples `(posed_complex, score)`. Else will yield `posed_complex`. """ if self.scoring_model is not None and use_pose_generator_scores: raise ValueError( "Cannot set use_pose_generator_scores=True when self.scoring_model is set (since both generator scores for complexes)." ) outputs = self.pose_generator.generate_poses( molecular_complex, centroid=centroid, box_dims=box_dims, exhaustiveness=exhaustiveness, num_modes=num_modes, num_pockets=num_pockets, out_dir=out_dir, generate_scores=use_pose_generator_scores) if use_pose_generator_scores: complexes, scores = outputs else: complexes = outputs # We know use_pose_generator_scores == False in this case if self.scoring_model is not None: Loading @@ -56,5 +119,3 @@ class Docker(object): else: for posed_complex in complexes: yield posed_complex score = np.zeros((1,)) return (score, (protein_docked, ligand_docked)) Loading
deepchem/dock/docking.py +63 −2 Original line number Diff line number Diff line Loading @@ -41,6 +41,69 @@ class Docker(object): (featurizer is None and scoring_model is not None)): raise ValueError( "featurizer/scoring_model must both be set or must both be None.") self.base_dir = tempfile.mkdtemp() self.pose_generator = pose_generator self.featurizer = featurizer self.scoring_model = scoring_model def dock(self, molecular_complex, centroid=None, box_dims=None, exhaustiveness=10, num_modes=9, num_pockets=None, out_dir=None, use_pose_generator_scores=False): """Generic docking function. This docking function uses this object's featurizer, pose generator, and scoring model to make docking predictions. This function is written in generic style so Parameters ---------- molecular_complex: Object Some representation of a molecular complex. exhaustiveness: int, optional (default 10) Tells pose generator how exhaustive it should be with pose generation. num_modes: int, optional (default 9) Tells pose generator how many binding modes it should generate at each invocation. num_pockets: int, optional (default None) If specified, `self.pocket_finder` must be set. Will only generate poses for the first `num_pockets` returned by `self.pocket_finder`. out_dir: str, optional (default None) If specified, write generated poses to this directory. use_pose_generator_scores: bool, optional (default False) If `True`, ask pose generator to generate scores. This cannot be `True` if `self.featurizer` and `self.scoring_model` are set since those will be used to generate scores in that case. Returns ------- A generator. If `use_pose_generator_scores==True` or `self.scoring_model` is set, then will yield tuples `(posed_complex, score)`. Else will yield `posed_complex`. """ if self.scoring_model is not None and use_pose_generator_scores: raise ValueError( "Cannot set use_pose_generator_scores=True when self.scoring_model is set (since both generator scores for complexes)." ) outputs = self.pose_generator.generate_poses( molecular_complex, centroid=centroid, box_dims=box_dims, exhaustiveness=exhaustiveness, num_modes=num_modes, num_pockets=num_pockets, out_dir=out_dir, generate_scores=use_pose_generator_scores) if use_pose_generator_scores: complexes, scores = outputs else: complexes = outputs # We know use_pose_generator_scores == False in this case if self.scoring_model is not None: Loading @@ -56,5 +119,3 @@ class Docker(object): else: for posed_complex in complexes: yield posed_complex score = np.zeros((1,)) return (score, (protein_docked, ligand_docked))
