Loading deepchem/feat/smiles_featurizers.py +38 −11 Original line number Diff line number Diff line Loading @@ -52,6 +52,18 @@ def create_char_to_idx(filename, class SmilesToSeq(Featurizer): """ SmilesToSeq Featurizer takes a SMILES string, and turns it into a sequence. Details taken from https://arxiv.org/abs/1712.02734. SMILES strings smaller than a specified max length (max_len) are padded using the PAD token while those larger than the max length are not considered. Based on the paper, there is also the option to add extra padding (pad_len) on both sides of the string after length normalization. Using a character to index (char_to_idx) mapping, the SMILES characters are turned into indices and the resulting sequence of indices serves as the input for an embedding layer. """ def __init__(self, char_to_idx, max_len=250, pad_len=10, **kwargs): """ Loading @@ -72,7 +84,10 @@ class SmilesToSeq(Featurizer): def to_seq(self, smile): """Turns list of smiles characters into array of indices""" seq = [self.char_to_idx[character] for character in smile] out_of_vocab_idx = self.char_to_idx[OUT_OF_VOCAB_TOKEN] seq = [ self.char_to_idx.get(character, out_of_vocab_idx) for character in smile ] return np.array(seq) def remove_pad(self, characters): Loading Loading @@ -115,13 +130,27 @@ class SmilesToSeq(Featurizer): class SmilesToImage(Featurizer): """ SmilesToImage Featurizer takes a SMILES string, and turns it into an image. Details taken from https://arxiv.org/abs/1712.02734. The default size of for the image is 80 x 80. Two image modes are currently supported - std & engd. std is the gray scale specification, with atomic numbers as pixel values for atom positions and a constant value of 2 for bond positions. engd is a 4-channel specification, which uses atom properties like hybridization, valency, charges in addition to atomic number. Bond type is also used for the bonds. The coordinates of all atoms are computed, and lines are drawn between atoms to indicate bonds. For the respective channels, the atom and bond positions are set to the property values as mentioned in the paper. """ def __init__(self, img_size=80, res=0.5, embed=20, max_len=250, img_mode="std", img_spec="std", **kwargs): """ Parameters Loading @@ -130,22 +159,20 @@ class SmilesToImage(Featurizer): Size of the image tensor res: float, default 0.5 Displays the resolution of each pixel in Angstrom embded: int, default 20 #TODO max_len: int, default 250 Maximum allowed length of SMILES string img_mode: str, default std img_spec: str, default std Indicates the channel organization of the image tensor """ if img_mode not in ["std", "engd"]: if img_spec not in ["std", "engd"]: raise ValueError( "Image mode must be one of std or engd. {} is not supported".format( img_mode)) img_spec)) self.img_size = img_size self.max_len = max_len self.res = res self.img_mode = img_mode self.embed = embed self.img_spec = img_spec self.embed = int(dims * res / 2) super(SmilesToImage, self).__init__(**kwargs) def _featurize(self, mol): Loading @@ -162,7 +189,7 @@ class SmilesToImage(Featurizer): AllChem.Compute2DCoords(cmol) atom_coords = cmol.GetConformer(0).GetPositions() if self.img_mode == "std": if self.img_spec == "std": # Setup image img = np.zeros((self.img_size, self.img_size, 1)) # Compute bond properties Loading deepchem/feat/tests/test_smiles_featurizers.py +5 −5 Original line number Diff line number Diff line Loading @@ -32,10 +32,10 @@ class TestSmilesFeaturizers(TestCase): def test_reconstruct_from_seq(self): """Test SMILES reconstruction from features.""" smiles = "Cn1c(=O)c2c(ncn2C)n(C)c1=O" smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O"] from rdkit import Chem mol = Chem.MolFromSmiles(smiles) features = self.feat._featurize(mol) mols = [Chem.MolFromSmiles(smile) for smile in smiles] features = self.feat.featurize(mols) reconstructed_smile = self.feat.smiles_from_seq(features) assert_equals(smiles, reconstructed_smile) reconstructed_smile = self.feat.smiles_from_seq(features[0]) assert_equals(smiles[0], reconstructed_smile) Loading
deepchem/feat/smiles_featurizers.py +38 −11 Original line number Diff line number Diff line Loading @@ -52,6 +52,18 @@ def create_char_to_idx(filename, class SmilesToSeq(Featurizer): """ SmilesToSeq Featurizer takes a SMILES string, and turns it into a sequence. Details taken from https://arxiv.org/abs/1712.02734. SMILES strings smaller than a specified max length (max_len) are padded using the PAD token while those larger than the max length are not considered. Based on the paper, there is also the option to add extra padding (pad_len) on both sides of the string after length normalization. Using a character to index (char_to_idx) mapping, the SMILES characters are turned into indices and the resulting sequence of indices serves as the input for an embedding layer. """ def __init__(self, char_to_idx, max_len=250, pad_len=10, **kwargs): """ Loading @@ -72,7 +84,10 @@ class SmilesToSeq(Featurizer): def to_seq(self, smile): """Turns list of smiles characters into array of indices""" seq = [self.char_to_idx[character] for character in smile] out_of_vocab_idx = self.char_to_idx[OUT_OF_VOCAB_TOKEN] seq = [ self.char_to_idx.get(character, out_of_vocab_idx) for character in smile ] return np.array(seq) def remove_pad(self, characters): Loading Loading @@ -115,13 +130,27 @@ class SmilesToSeq(Featurizer): class SmilesToImage(Featurizer): """ SmilesToImage Featurizer takes a SMILES string, and turns it into an image. Details taken from https://arxiv.org/abs/1712.02734. The default size of for the image is 80 x 80. Two image modes are currently supported - std & engd. std is the gray scale specification, with atomic numbers as pixel values for atom positions and a constant value of 2 for bond positions. engd is a 4-channel specification, which uses atom properties like hybridization, valency, charges in addition to atomic number. Bond type is also used for the bonds. The coordinates of all atoms are computed, and lines are drawn between atoms to indicate bonds. For the respective channels, the atom and bond positions are set to the property values as mentioned in the paper. """ def __init__(self, img_size=80, res=0.5, embed=20, max_len=250, img_mode="std", img_spec="std", **kwargs): """ Parameters Loading @@ -130,22 +159,20 @@ class SmilesToImage(Featurizer): Size of the image tensor res: float, default 0.5 Displays the resolution of each pixel in Angstrom embded: int, default 20 #TODO max_len: int, default 250 Maximum allowed length of SMILES string img_mode: str, default std img_spec: str, default std Indicates the channel organization of the image tensor """ if img_mode not in ["std", "engd"]: if img_spec not in ["std", "engd"]: raise ValueError( "Image mode must be one of std or engd. {} is not supported".format( img_mode)) img_spec)) self.img_size = img_size self.max_len = max_len self.res = res self.img_mode = img_mode self.embed = embed self.img_spec = img_spec self.embed = int(dims * res / 2) super(SmilesToImage, self).__init__(**kwargs) def _featurize(self, mol): Loading @@ -162,7 +189,7 @@ class SmilesToImage(Featurizer): AllChem.Compute2DCoords(cmol) atom_coords = cmol.GetConformer(0).GetPositions() if self.img_mode == "std": if self.img_spec == "std": # Setup image img = np.zeros((self.img_size, self.img_size, 1)) # Compute bond properties Loading
deepchem/feat/tests/test_smiles_featurizers.py +5 −5 Original line number Diff line number Diff line Loading @@ -32,10 +32,10 @@ class TestSmilesFeaturizers(TestCase): def test_reconstruct_from_seq(self): """Test SMILES reconstruction from features.""" smiles = "Cn1c(=O)c2c(ncn2C)n(C)c1=O" smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O"] from rdkit import Chem mol = Chem.MolFromSmiles(smiles) features = self.feat._featurize(mol) mols = [Chem.MolFromSmiles(smile) for smile in smiles] features = self.feat.featurize(mols) reconstructed_smile = self.feat.smiles_from_seq(features) assert_equals(smiles, reconstructed_smile) reconstructed_smile = self.feat.smiles_from_seq(features[0]) assert_equals(smiles[0], reconstructed_smile)
