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Atom featurizers

AromaticityFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom based on its aromaticity.

Source code in bionemo/geometric/atom_featurizers.py 
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class AromaticityFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom based on its aromaticity."""

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 1

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of representing if atoms are aromatic as integers.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([int(mol.GetAtomWithIdx(a).GetIsAromatic()) for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of representing if atoms are aromatic as integers.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of representing if atoms are aromatic as integers.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([int(mol.GetAtomWithIdx(a).GetIsAromatic()) for a in _atom_indices], dtype=torch.int)

AtomicNumberFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its atomic number.

Source code in bionemo/geometric/atom_featurizers.py 
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class AtomicNumberFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its atomic number."""

    def __init__(self, dim_atomic_num: Optional[int] = None) -> None:
        """Initializes AtomicNumberFeaturizer class."""
        DIM_ATOMIC_NUM = 118
        self.dim_atomic_num = dim_atomic_num if dim_atomic_num else DIM_ATOMIC_NUM

    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return self.dim_atomic_num

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of integers representing atomic numbers of atoms.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([mol.GetAtomWithIdx(a).GetAtomicNum() for a in _atom_indices], dtype=torch.int)

__init__(dim_atomic_num=None)

Initializes AtomicNumberFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self, dim_atomic_num: Optional[int] = None) -> None:
    """Initializes AtomicNumberFeaturizer class."""
    DIM_ATOMIC_NUM = 118
    self.dim_atomic_num = dim_atomic_num if dim_atomic_num else DIM_ATOMIC_NUM

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of integers representing atomic numbers of atoms.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of integers representing atomic numbers of atoms.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([mol.GetAtomWithIdx(a).GetAtomicNum() for a in _atom_indices], dtype=torch.int)

n_dim()

Returns dimensionality of the computed features.

Source code in bionemo/geometric/atom_featurizers.py 
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def n_dim(self) -> int:
    """Returns dimensionality of the computed features."""
    return self.dim_atomic_num

AtomicRadiusFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its bond, covalent, and vdW radii.

Source code in bionemo/geometric/atom_featurizers.py 
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class AtomicRadiusFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its bond, covalent, and vdW radii."""

    def __init__(self) -> None:
        """Initializes AtomicRadiusFeaturizer class."""
        self.pt = Chem.GetPeriodicTable()
        self._min_val = torch.Tensor(
            [
                0.0,  # Bond radius
                0.28,  # Covalent radius
                1.2,  # van der Waals radius
            ]
        )

        self._max_val = torch.Tensor(
            [
                2.4,  # Bond radius
                2.6,  # Covalent radius
                3.0,  # van der Waals radius
            ]
        )

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 3

    @property
    def min_val(self) -> torch.tensor:
        """Returns minimum values for features: bond, covalent, and vdW radius."""
        return self._min_val

    @property
    def max_val(self) -> torch.tensor:
        """Returns maximum values for features: bond, covalent, and vdW radius."""
        return self._max_val

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
        """Computes bond radius, covalent radius, and van der Waals radius without normalization.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.Tensor of different atomic radii. Each atom is featurizer by bond radius, covalent radius, and van der Waals radius.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

        feats = []
        for aidx in _atom_indices:
            atomic_num = mol.GetAtomWithIdx(aidx).GetAtomicNum()
            feats.append([self.pt.GetRb0(atomic_num), self.pt.GetRcovalent(atomic_num), self.pt.GetRvdw(atomic_num)])

        return torch.Tensor(feats)

max_val property

Returns maximum values for features: bond, covalent, and vdW radius.

min_val property

Returns minimum values for features: bond, covalent, and vdW radius.

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes AtomicRadiusFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self) -> None:
    """Initializes AtomicRadiusFeaturizer class."""
    self.pt = Chem.GetPeriodicTable()
    self._min_val = torch.Tensor(
        [
            0.0,  # Bond radius
            0.28,  # Covalent radius
            1.2,  # van der Waals radius
        ]
    )

    self._max_val = torch.Tensor(
        [
            2.4,  # Bond radius
            2.6,  # Covalent radius
            3.0,  # van der Waals radius
        ]
    )

get_atom_features(mol, atom_indices=None)

Computes bond radius, covalent radius, and van der Waals radius without normalization.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
Tensor

A torch.Tensor of different atomic radii. Each atom is featurizer by bond radius, covalent radius, and van der Waals radius.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
    """Computes bond radius, covalent radius, and van der Waals radius without normalization.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.Tensor of different atomic radii. Each atom is featurizer by bond radius, covalent radius, and van der Waals radius.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

    feats = []
    for aidx in _atom_indices:
        atomic_num = mol.GetAtomWithIdx(aidx).GetAtomicNum()
        feats.append([self.pt.GetRb0(atomic_num), self.pt.GetRcovalent(atomic_num), self.pt.GetRvdw(atomic_num)])

    return torch.Tensor(feats)

ChiralTypeFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its chirality type.

Source code in bionemo/geometric/atom_featurizers.py 
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class ChiralTypeFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its chirality type."""

    def __init__(self) -> None:
        """Initializes ChiralTypeFeaturizer class."""
        self.dim_chiral_types = len(ChiralType.values)

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return self.dim_chiral_types

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor representing chirality type of atoms as integers.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([int(mol.GetAtomWithIdx(a).GetChiralTag()) for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes ChiralTypeFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self) -> None:
    """Initializes ChiralTypeFeaturizer class."""
    self.dim_chiral_types = len(ChiralType.values)

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor representing chirality type of atoms as integers.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor representing chirality type of atoms as integers.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([int(mol.GetAtomWithIdx(a).GetChiralTag()) for a in _atom_indices], dtype=torch.int)

CrippenFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by Crippen logP and molar refractivity.

Source code in bionemo/geometric/atom_featurizers.py 
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class CrippenFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by Crippen logP and molar refractivity."""

    def __init__(self):
        """Initializes CrippenFeaturizer class."""
        self._min_val = torch.Tensor(
            [
                -2.996,  # logP
                0.0,  # MR
            ]
        )

        self._max_val = torch.Tensor(
            [
                0.8857,  # logP
                6.0,  # MR
            ]
        )

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 2

    @property
    def min_val(self) -> torch.tensor:
        """Returns minimum values for features: logP and molar refractivity."""
        return self._min_val

    @property
    def max_val(self) -> torch.tensor:
        """Returns maximum values for features: logP and molar refractivity."""
        return self._max_val

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
        """Compute atomic contributions to Crippen logP and molar refractivity.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.Tensor featurizing atoms by its atomic contribution to logP and molar refractivity.
        """
        logp_mr_list = torch.Tensor(rdMolDescriptors._CalcCrippenContribs(mol))
        logp_mr_list = torch.clamp(logp_mr_list, min=self.min_val, max=self.max_val)
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return logp_mr_list[_atom_indices, :]

max_val property

Returns maximum values for features: logP and molar refractivity.

min_val property

Returns minimum values for features: logP and molar refractivity.

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes CrippenFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self):
    """Initializes CrippenFeaturizer class."""
    self._min_val = torch.Tensor(
        [
            -2.996,  # logP
            0.0,  # MR
        ]
    )

    self._max_val = torch.Tensor(
        [
            0.8857,  # logP
            6.0,  # MR
        ]
    )

get_atom_features(mol, atom_indices=None)

Compute atomic contributions to Crippen logP and molar refractivity.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
Tensor

A torch.Tensor featurizing atoms by its atomic contribution to logP and molar refractivity.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
    """Compute atomic contributions to Crippen logP and molar refractivity.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.Tensor featurizing atoms by its atomic contribution to logP and molar refractivity.
    """
    logp_mr_list = torch.Tensor(rdMolDescriptors._CalcCrippenContribs(mol))
    logp_mr_list = torch.clamp(logp_mr_list, min=self.min_val, max=self.max_val)
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return logp_mr_list[_atom_indices, :]

DegreeFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its degree (excluding hydrogens) of connectivity.

Source code in bionemo/geometric/atom_featurizers.py 
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class DegreeFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its degree (excluding hydrogens) of connectivity."""

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 6

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of integers representing degree of connectivity of atoms.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([mol.GetAtomWithIdx(a).GetDegree() for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of integers representing degree of connectivity of atoms.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of integers representing degree of connectivity of atoms.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([mol.GetAtomWithIdx(a).GetDegree() for a in _atom_indices], dtype=torch.int)

ElectronicPropertyFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its electronic properties.

This class computes electronic properties like electronegativity, ionization energy, and electron affinity.

Source code in bionemo/geometric/atom_featurizers.py 
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class ElectronicPropertyFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its electronic properties.

    This class computes electronic properties like electronegativity, ionization energy, and electron affinity.
    """

    def __init__(self, data_file=None) -> None:
        """Initializes PeriodicTableFeaturizer class.

        Args:
            data_file: Path to the data file.
        """
        if data_file is None:
            # Use default
            root_path = Path(__file__).resolve().parent
            data_file = root_path / "data" / "electronic_data.csv"
        self.data_df = pd.read_csv(data_file).set_index("AtomicNumber")

        self.pauling_en_dict = self.data_df["Electronegativity"].to_dict()
        self.ie_dict = self.data_df["IonizationEnergy"].to_dict()
        self.ea_dict = self.data_df["ElectronAffinity"].to_dict()

        self._min_val = torch.Tensor(
            [
                self.data_df["Electronegativity"].min(),
                self.data_df["IonizationEnergy"].min(),
                self.data_df["ElectronAffinity"].min(),
            ]
        )

        self._max_val = torch.Tensor(
            [
                self.data_df["Electronegativity"].max(),
                self.data_df["IonizationEnergy"].max(),
                self.data_df["ElectronAffinity"].max(),
            ]
        )

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 3

    @property
    def min_val(self) -> torch.Tensor:
        """Returns minimum values for features: electronegativity, ionization energy, electron affinity."""
        return self._min_val

    @property
    def max_val(self) -> torch.Tensor:
        """Returns maximum values for features: electronegativity, ionization energy, electron affinity."""
        return self._max_val

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
        """Returns electronic features of the atom.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.Tensor consisting of Pauling scale electronegativity, ionization energy, and electron affinity for each atom.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

        feats = []
        for aidx in _atom_indices:
            atomic_num = mol.GetAtomWithIdx(aidx).GetAtomicNum()
            feats.append([self.pauling_en_dict[atomic_num], self.ie_dict[atomic_num], self.ea_dict[atomic_num]])
        return torch.Tensor(feats)

max_val property

Returns maximum values for features: electronegativity, ionization energy, electron affinity.

min_val property

Returns minimum values for features: electronegativity, ionization energy, electron affinity.

n_dim property

Returns dimensionality of the computed features.

__init__(data_file=None)

Initializes PeriodicTableFeaturizer class.

Parameters:

Name Type Description Default
data_file

Path to the data file.

 None
Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self, data_file=None) -> None:
    """Initializes PeriodicTableFeaturizer class.

    Args:
        data_file: Path to the data file.
    """
    if data_file is None:
        # Use default
        root_path = Path(__file__).resolve().parent
        data_file = root_path / "data" / "electronic_data.csv"
    self.data_df = pd.read_csv(data_file).set_index("AtomicNumber")

    self.pauling_en_dict = self.data_df["Electronegativity"].to_dict()
    self.ie_dict = self.data_df["IonizationEnergy"].to_dict()
    self.ea_dict = self.data_df["ElectronAffinity"].to_dict()

    self._min_val = torch.Tensor(
        [
            self.data_df["Electronegativity"].min(),
            self.data_df["IonizationEnergy"].min(),
            self.data_df["ElectronAffinity"].min(),
        ]
    )

    self._max_val = torch.Tensor(
        [
            self.data_df["Electronegativity"].max(),
            self.data_df["IonizationEnergy"].max(),
            self.data_df["ElectronAffinity"].max(),
        ]
    )

get_atom_features(mol, atom_indices=None)

Returns electronic features of the atom.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
Tensor

A torch.Tensor consisting of Pauling scale electronegativity, ionization energy, and electron affinity for each atom.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.Tensor:
    """Returns electronic features of the atom.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.Tensor consisting of Pauling scale electronegativity, ionization energy, and electron affinity for each atom.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

    feats = []
    for aidx in _atom_indices:
        atomic_num = mol.GetAtomWithIdx(aidx).GetAtomicNum()
        feats.append([self.pauling_en_dict[atomic_num], self.ie_dict[atomic_num], self.ea_dict[atomic_num]])
    return torch.Tensor(feats)

HybridizationFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its hybridization type.

Source code in bionemo/geometric/atom_featurizers.py 
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class HybridizationFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its hybridization type."""

    def __init__(self) -> None:
        """Initializes HybridizationFeaturizer class."""
        self.dim_hybridization_types = len(HybridizationType.values)

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return self.dim_hybridization_types

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor representing hybridization type of atoms as integers.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([int(mol.GetAtomWithIdx(a).GetHybridization()) for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes HybridizationFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self) -> None:
    """Initializes HybridizationFeaturizer class."""
    self.dim_hybridization_types = len(HybridizationType.values)

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor representing hybridization type of atoms as integers.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor representing hybridization type of atoms as integers.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([int(mol.GetAtomWithIdx(a).GetHybridization()) for a in _atom_indices], dtype=torch.int)

PeriodicTableFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its position (period and group) in the periodic table.

Source code in bionemo/geometric/atom_featurizers.py 
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class PeriodicTableFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its position (period and group) in the periodic table."""

    def __init__(self) -> None:
        """Initializes PeriodicTableFeaturizer class."""
        self.pt = Chem.GetPeriodicTable()
        # The number of elements per period in the periodic table
        self.period_limits = [2, 10, 18, 36, 54, 86, 118]

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 25

    def get_period(self, atom: Chem.Atom) -> int:
        """Returns periodic table period of atom."""
        atomic_number = atom.GetAtomicNum()

        # Determine the period based on atomic number.
        for period, limit in enumerate(self.period_limits, start=1):
            if atomic_number <= limit:
                return period
        return None

    def get_group(self, atom: Chem.Atom) -> int:
        """Returns periodic table group of atom."""
        group = self.pt.GetNOuterElecs(atom.GetAtomicNum())
        return group

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes periodic table position of atoms of all or select atoms specific in `atom_indices`.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of representing positions of atoms in periodic table. First index represents period and second index represents group.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor(
            [(self.get_period(mol.GetAtomWithIdx(a)), self.get_group(mol.GetAtomWithIdx(a))) for a in _atom_indices],
            dtype=torch.int,
        )

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes PeriodicTableFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self) -> None:
    """Initializes PeriodicTableFeaturizer class."""
    self.pt = Chem.GetPeriodicTable()
    # The number of elements per period in the periodic table
    self.period_limits = [2, 10, 18, 36, 54, 86, 118]

get_atom_features(mol, atom_indices=None)

Computes periodic table position of atoms of all or select atoms specific in atom_indices.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of representing positions of atoms in periodic table. First index represents period and second index represents group.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes periodic table position of atoms of all or select atoms specific in `atom_indices`.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of representing positions of atoms in periodic table. First index represents period and second index represents group.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor(
        [(self.get_period(mol.GetAtomWithIdx(a)), self.get_group(mol.GetAtomWithIdx(a))) for a in _atom_indices],
        dtype=torch.int,
    )

get_group(atom)

Returns periodic table group of atom.

Source code in bionemo/geometric/atom_featurizers.py 
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def get_group(self, atom: Chem.Atom) -> int:
    """Returns periodic table group of atom."""
    group = self.pt.GetNOuterElecs(atom.GetAtomicNum())
    return group

get_period(atom)

Returns periodic table period of atom.

Source code in bionemo/geometric/atom_featurizers.py 
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def get_period(self, atom: Chem.Atom) -> int:
    """Returns periodic table period of atom."""
    atomic_number = atom.GetAtomicNum()

    # Determine the period based on atomic number.
    for period, limit in enumerate(self.period_limits, start=1):
        if atomic_number <= limit:
            return period
    return None

ScaffoldFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom based on whether it is present in Bemis-Murcko scaffold.

Source code in bionemo/geometric/atom_featurizers.py 
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class ScaffoldFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom based on whether it is present in Bemis-Murcko scaffold."""

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 1

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Returns position of the atoms with respect to Bemis-Murcko scaffold.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor indicating if atoms are present in the Bemis-Murcko scaffold of the molecule.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

        scaffold = MurckoScaffold.GetScaffoldForMol(mol)
        scaffold_atom_idx = set(mol.GetSubstructMatch(scaffold))

        feats = [int(aidx in scaffold_atom_idx) for aidx in _atom_indices]
        return torch.tensor(feats, dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

get_atom_features(mol, atom_indices=None)

Returns position of the atoms with respect to Bemis-Murcko scaffold.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor indicating if atoms are present in the Bemis-Murcko scaffold of the molecule.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Returns position of the atoms with respect to Bemis-Murcko scaffold.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor indicating if atoms are present in the Bemis-Murcko scaffold of the molecule.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())

    scaffold = MurckoScaffold.GetScaffoldForMol(mol)
    scaffold_atom_idx = set(mol.GetSubstructMatch(scaffold))

    feats = [int(aidx in scaffold_atom_idx) for aidx in _atom_indices]
    return torch.tensor(feats, dtype=torch.int)

SmartsFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by hydrogen donor/acceptor and acidity/basicity.

Source code in bionemo/geometric/atom_featurizers.py 
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class SmartsFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by hydrogen donor/acceptor and acidity/basicity."""

    def __init__(self):
        """Initializes SmartsFeaturizer class."""
        self.hydrogen_donor = Chem.MolFromSmarts("[$([N;!H0;v3,v4&+1]),$([O,S;H1;+0]),n&H1&+0]")
        self.hydrogen_acceptor = Chem.MolFromSmarts(
            "[$([O,S;H1;v2;!$(*-*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=[O,N,P,S])]),"
            "n&H0&+0,$([o,s;+0;!$([o,s]:n);!$([o,s]:c:n)])]"
        )
        self.acidic = Chem.MolFromSmarts("[$([C,S](=[O,S,P])-[O;H1,-1])]")
        self.basic = Chem.MolFromSmarts(
            "[#7;+,$([N;H2&+0][$([C,a]);!$([C,a](=O))]),$([N;H1&+0]([$([C,a]);!$([C,a](=O))])[$([C,a]);"
            "!$([C,a](=O))]),$([N;H0&+0]([C;!$(C(=O))])([C;!$(C(=O))])[C;!$(C(=O))])]"
        )

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 4

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes matches by prefixed SMARTS patterns.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            An torch.tensor indicating if atoms are hydrogen bond donors, hydrogen bond acceptors, acidic, or basic.
        """
        hydrogen_donor_match = sum(mol.GetSubstructMatches(self.hydrogen_donor), ())
        hydrogen_acceptor_match = sum(mol.GetSubstructMatches(self.hydrogen_acceptor), ())
        acidic_match = sum(mol.GetSubstructMatches(self.acidic), ())
        basic_match = sum(mol.GetSubstructMatches(self.basic), ())

        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        feats = [
            [
                aidx in hydrogen_donor_match,
                aidx in hydrogen_acceptor_match,
                aidx in acidic_match,
                aidx in basic_match,
            ]
            for aidx in _atom_indices
        ]

        return torch.tensor(feats, dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes SmartsFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self):
    """Initializes SmartsFeaturizer class."""
    self.hydrogen_donor = Chem.MolFromSmarts("[$([N;!H0;v3,v4&+1]),$([O,S;H1;+0]),n&H1&+0]")
    self.hydrogen_acceptor = Chem.MolFromSmarts(
        "[$([O,S;H1;v2;!$(*-*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=[O,N,P,S])]),"
        "n&H0&+0,$([o,s;+0;!$([o,s]:n);!$([o,s]:c:n)])]"
    )
    self.acidic = Chem.MolFromSmarts("[$([C,S](=[O,S,P])-[O;H1,-1])]")
    self.basic = Chem.MolFromSmarts(
        "[#7;+,$([N;H2&+0][$([C,a]);!$([C,a](=O))]),$([N;H1&+0]([$([C,a]);!$([C,a](=O))])[$([C,a]);"
        "!$([C,a](=O))]),$([N;H0&+0]([C;!$(C(=O))])([C;!$(C(=O))])[C;!$(C(=O))])]"
    )

get_atom_features(mol, atom_indices=None)

Computes matches by prefixed SMARTS patterns.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

An torch.tensor indicating if atoms are hydrogen bond donors, hydrogen bond acceptors, acidic, or basic.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes matches by prefixed SMARTS patterns.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        An torch.tensor indicating if atoms are hydrogen bond donors, hydrogen bond acceptors, acidic, or basic.
    """
    hydrogen_donor_match = sum(mol.GetSubstructMatches(self.hydrogen_donor), ())
    hydrogen_acceptor_match = sum(mol.GetSubstructMatches(self.hydrogen_acceptor), ())
    acidic_match = sum(mol.GetSubstructMatches(self.acidic), ())
    basic_match = sum(mol.GetSubstructMatches(self.basic), ())

    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    feats = [
        [
            aidx in hydrogen_donor_match,
            aidx in hydrogen_acceptor_match,
            aidx in acidic_match,
            aidx in basic_match,
        ]
        for aidx in _atom_indices
    ]

    return torch.tensor(feats, dtype=torch.int)

TotalDegreeFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by its total degree (including hydrogens) of connectivity.

Source code in bionemo/geometric/atom_featurizers.py 
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class TotalDegreeFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by its total degree (including hydrogens) of connectivity."""

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return 6

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of integers representing total connectivity (including hydrogens) of atoms.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([mol.GetAtomWithIdx(a).GetTotalDegree() for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of integers representing total connectivity (including hydrogens) of atoms.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of integers representing total connectivity (including hydrogens) of atoms.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([mol.GetAtomWithIdx(a).GetTotalDegree() for a in _atom_indices], dtype=torch.int)

TotalNumHFeaturizer

Bases: BaseAtomFeaturizer

Class for featurizing atom by total number of hydrogens.

Source code in bionemo/geometric/atom_featurizers.py 
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class TotalNumHFeaturizer(BaseAtomFeaturizer):
    """Class for featurizing atom by total number of hydrogens."""

    def __init__(self) -> None:
        """Initializes TotalNumHFeaturizer class."""
        self.dim_total_num_hydrogen = 5  # 4 + 1 (no hydrogens)

    @property
    def n_dim(self) -> int:
        """Returns dimensionality of the computed features."""
        return self.dim_total_num_hydrogen

    def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
        """Computes features of atoms of all of select atoms.

        Args:
            mol: An RDkit Chem.Mol object
            atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

        Returns:
            A torch.tensor of integers representing total number of hydrogens on atoms.
        """
        _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
        return torch.tensor([mol.GetAtomWithIdx(a).GetTotalNumHs() for a in _atom_indices], dtype=torch.int)

n_dim property

Returns dimensionality of the computed features.

__init__()

Initializes TotalNumHFeaturizer class.

Source code in bionemo/geometric/atom_featurizers.py 
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def __init__(self) -> None:
    """Initializes TotalNumHFeaturizer class."""
    self.dim_total_num_hydrogen = 5  # 4 + 1 (no hydrogens)

get_atom_features(mol, atom_indices=None)

Computes features of atoms of all of select atoms.

Parameters:

Name Type Description Default
mol Mol

An RDkit Chem.Mol object

 required
atom_indices Optional[Iterable]

Indices of atoms for feature computation. By default, features for all atoms is computed.

 None

Returns:

Type Description
tensor

A torch.tensor of integers representing total number of hydrogens on atoms.
Source code in bionemo/geometric/atom_featurizers.py 
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def get_atom_features(self, mol: Mol, atom_indices: Optional[Iterable] = None) -> torch.tensor:
    """Computes features of atoms of all of select atoms.

    Args:
        mol: An RDkit Chem.Mol object
        atom_indices: Indices of atoms for feature computation. By default, features for all atoms is computed.

    Returns:
        A torch.tensor of integers representing total number of hydrogens on atoms.
    """
    _atom_indices = atom_indices if atom_indices else range(mol.GetNumAtoms())
    return torch.tensor([mol.GetAtomWithIdx(a).GetTotalNumHs() for a in _atom_indices], dtype=torch.int)