r"""Extended Topochemical Atom(ETA) descriptor.
References
* :doi:`10.1021/ci0342066`
* :doi:`10.1016/j.jhazmat.2013.03.023`
"""
from __future__ import division
import numpy as np
from rdkit import Chem
from . import _atomic_property as ap
from ._base import Descriptor
from ._util import atoms_to_numpy
from .RingCount import RingCount
__all__ = (
"EtaCoreCount",
"EtaShapeIndex",
"EtaVEMCount",
"EtaCompositeIndex",
"EtaFunctionalityIndex",
"EtaBranchingIndex",
"EtaDeltaAlpha",
"EtaEpsilon",
"EtaDeltaEpsilon",
"EtaDeltaBeta",
"EtaPsi",
"EtaDeltaPsi",
)
class AlterMolecule(Descriptor):
__slots__ = ("explicit_hydrogens", "_saturated")
kekulize = True
require_connected = True
def parameters(self):
return self.explicit_hydrogens, self._saturated
def __init__(self, explicit_hydrogens, saturated=False):
self._saturated = saturated
self.explicit_hydrogens = explicit_hydrogens
def __str__(self):
b = "Saturated" if self._saturated else "Reference"
H = "H" if self.explicit_hydrogens else ""
return "{}Mol{}".format(b, H)
def calculate(self):
new = Chem.RWMol(Chem.Mol())
ids = {}
for a in self.mol.GetAtoms():
if a.GetAtomicNum() == 1:
continue
if self._saturated:
new_a = Chem.Atom(a.GetAtomicNum())
new_a.SetFormalCharge(a.GetFormalCharge())
else:
new_a = Chem.Atom(6)
ids[a.GetIdx()] = new.AddAtom(new_a)
for bond in self.mol.GetBonds():
ai = bond.GetBeginAtom()
aj = bond.GetEndAtom()
if not self._saturated and (ai.GetDegree() > 4 or aj.GetDegree() > 4):
self.fail(ValueError("bond degree greater then 4"))
i = ids.get(ai.GetIdx())
j = ids.get(aj.GetIdx())
if i is not None and j is not None:
if self._saturated and (
ai.GetAtomicNum() != 6 or aj.GetAtomicNum() != 6
):
order = bond.GetBondType()
else:
order = Chem.BondType.SINGLE
new.AddBond(i, j, order)
new = Chem.Mol(new)
if Chem.SanitizeMol(new, catchErrors=True) != 0:
typ = "saturated" if self._saturated else "referense"
self.fail(ValueError("cannot sanitize {} mol".format(typ)))
if self.explicit_hydrogens:
new = Chem.AddHs(new)
Chem.Kekulize(new)
return new
class EtaBase(Descriptor):
__slots__ = ()
explicit_hydrogens = False
kekulize = True
require_connected = True
rtype = float
[docs]class EtaCoreCount(EtaBase):
r"""ETA core count descriptor.
.. math::
\alpha = \sum_{i = 1}^A \frac{Z_i - Z_i^v}{Z_i^v} \cdot \frac{1}{PN_i - 1}
where :math:`Z_i` and :math:`Z_i^v` are number of total and valence electons,
:math:`PN` is periodic number.
:type averaged: bool
:param averaged: averaged by number of heavy count
:type reference: bool
:param reference: use reference alkane
(same graph structure, but all atoms are carbon and all bonds are single bond)
:returns: reference and valence of any atoms > 4
"""
since = "1.0.0"
__slots__ = ("_averaged", "_reference")
[docs] def description(self):
return "{}ETA core count{}".format(
"averaged " if self._averaged else "",
" for reference graph" if self._reference else "",
)
@classmethod
def preset(cls, version):
return map(cls, [False, True])
def __str__(self):
suffix = "_R" if self._reference else ""
ave = "A" if self._averaged else ""
return "{}ETA_alpha{}".format(ave, suffix)
def parameters(self):
return self._averaged, self._reference
def __init__(self, averaged=False, reference=False):
self._averaged = averaged
self._reference = reference
def dependencies(self):
if self._reference:
return {"rmol": AlterMolecule(self.explicit_hydrogens)}
def calculate(self, rmol=None):
mol = rmol if self._reference else self.mol
v = sum(ap.get_core_count(a) for a in mol.GetAtoms())
if self._averaged:
v /= mol.GetNumAtoms()
return v
[docs]class EtaShapeIndex(EtaBase):
r"""ETA shape index descriptor.
.. math::
{\rm shape}_t = \frac{\alpha_t}{\alpha}
where :math:`\alpha_t` is p(alpha value only atoms which bond to 1 heavy atom),
y(3), or x(4).
:type type: str
:param type: one of shape_types
"""
since = "1.0.0"
__slots__ = ("_type",)
[docs] def description(self):
return "ETA shape index (type: {})".format(self._type)
shape_types = ("p", "y", "x")
_type_to_degree = {"p": 1, "y": 3, "x": 4}
@classmethod
def preset(cls, version):
return (cls(t) for t in cls.shape_types)
def __str__(self):
return "ETA_shape_{}".format(self._type)
def parameters(self):
return (self._type,)
def __init__(self, type="p"):
assert type in self.shape_types
self._type = type
def dependencies(self):
return {"a": EtaCoreCount(False)}
def calculate(self, a):
d = self._type_to_degree[self._type]
return (
sum(ap.get_core_count(a) for a in self.mol.GetAtoms() if a.GetDegree() == d)
/ a
)
[docs]class EtaVEMCount(EtaBase):
r"""ETA VEM(valence electron mobile) count descriptor.
.. math::
\beta^{\rm s} = \frac{1}{2} \sum^A_{i=1} \beta^{\rm s}_i
\beta^{\rm s}_i = \sum^A_{j = 1} x_{ij}\sigma_{ij}
x_{ij} = \begin{cases}
0.5 & \left( \left| \epsilon_i - \epsilon_j \right| \leq 0.3 \right) \\
0.75 & \left( \left| \epsilon_i - \epsilon_j \right| > 0.3 \right)
\end{cases}
\epsilon_i = - \alpha_i + 0.3 Z^{\rm v}
where :math:`\sigma_{ij}` is sigma bond count between i and j.
.. math::
\beta^{\rm ns\delta} = \sum^A_{i = 1} \beta^{\rm ns\delta}_i
where :math:`\beta^{\rm ns\delta}_i` is
0.5 if i-th atom is making resonance with an aromatic ring.
.. math::
\beta^{\rm ns} = \frac{1}{2} \sum^A_{i=1} \beta^{\rm ns}_i
\beta^{\rm ns}_i = \sum^A_{j = 1} y_{ij}\pi_{ij} + \beta^{\rm ns\delta}_i
y_{ij} = \begin{cases}
2.0 & \left( {\rm ij\ is\ aromatic\ bond} \right) \\
1.5 & \left( \left| \epsilon_i - \epsilon_j \right| > 0.3 \right) \\
1.0 & \left( \left| \epsilon_i - \epsilon_j \right| \leq 0.3 \right)
\end{cases}
where :math:`\pi_{ij}` is pi bond count between i and j.
.. math::
\beta = \beta^{\rm s} + \beta^{\rm ns}
:type type: str
:param type: one of beta_types
:type averaged: bool
:param averaged: averaged by heavy atom count
"""
since = "1.0.0"
__slots__ = ("_type", "_averaged")
[docs] def description(self):
if self._type == "":
d = ""
elif self._type == "s":
d = "sigma contribution to "
elif self._type == "ns":
d = "nonsigma contribution to "
else:
d = "delta contribution to "
return "{}{}valence electron mobile count".format(
"averaged " if self._averaged else "", d
)
@classmethod
def preset(cls, version):
return (cls(b, a) for b in cls.beta_types for a in [False, True])
def __str__(self):
typ = "_{}".format(self._type) if self._type else ""
ave = "A" if self._averaged else ""
return "{}ETA_beta{}".format(ave, typ)
beta_types = ("", "s", "ns", "ns_d")
def parameters(self):
return self._type, self._averaged
def __init__(self, type="", averaged=False):
assert type in self.beta_types
self._type = type
self._averaged = averaged
def _get_beta_s(self, atom):
return ap.get_eta_beta_sigma(atom) / 2.0
def _get_beta_ns_d(self, atom):
v = ap.get_eta_beta_delta(atom)
return v
def _get_beta_ns(self, atom):
return ap.get_eta_beta_non_sigma(atom) / 2.0 + self._get_beta_ns_d(atom)
def _get_beta_(self, atom):
return self._get_beta_s(atom) + self._get_beta_ns(atom)
def calculate(self):
getter = getattr(self, "_get_beta_" + self._type)
if getter:
v = sum(getter(a) for a in self.mol.GetAtoms())
if self._averaged:
v /= self.mol.GetNumAtoms()
return v
[docs]class EtaCompositeIndex(EtaBase):
r"""ETA composite index descriptor.
.. math::
\eta = \sum_{i < j} \left( \frac{\gamma_i \gamma_j}{r_{ij}^2} \right)^{0.5}
\gamma_i = \frac{\alpha_i}{\beta_i}
where :math:`r_{ij}` is graph distance.
.. math::
\eta^{\rm local} = \sum_{i < j, r_{ij} = 1} \left( \gamma_i \gamma_j \right)^{0.5}
:type reference: bool
:param reference: use reference alkane.
:type local: bool
:param local: use :math:`\eta^{\rm local}`
:type averaged: bool
:param averaged: averaged
:returns: reference and valence of any atoms > 4
"""
since = "1.0.0"
__slots__ = ("_reference", "_local", "_averaged")
[docs] def description(self):
return "{}{}ETA composite index{}".format(
"averaged " if self._averaged else "",
"local " if self._local else "",
" for reference graph",
)
@classmethod
def preset(cls, version):
ft = [False, True]
return (cls(r, l, a) for r in ft for l in ft for a in ft)
def __str__(self):
suffixes = []
if self._reference:
suffixes.append("R")
if self._local:
suffixes.append("L")
if len(suffixes) > 0:
suffix = "_" + "".join(suffixes)
else:
suffix = ""
ave = "A" if self._averaged else ""
return "{}ETA_eta{}".format(ave, suffix)
def parameters(self):
return self._reference, self._local, self._averaged
def __init__(self, reference=False, local=False, averaged=False):
self._reference = reference
self._local = local
self._averaged = averaged
def dependencies(self):
if self._reference:
return {"rmol": AlterMolecule(self.explicit_hydrogens)}
def calculate(self, rmol=None):
mol = rmol if self._reference else self.mol
D = Chem.GetDistanceMatrix(mol, force=True)
if self._local:
def checker(r):
return r == 1
else:
def checker(r):
return r != 0
gamma = atoms_to_numpy(ap.get_eta_gamma, mol)
v = sum(
sum(
np.sqrt(gamma[i] * gamma[j] / r ** 2)
for j, r in enumerate(row)
if i < j and checker(r)
)
for i, row in enumerate(D)
)
if self._averaged:
v /= mol.GetNumAtoms()
return v
[docs]class EtaFunctionalityIndex(EtaBase):
r"""ETA functionality index descriptor.
.. math::
\eta^{\rm F} = \eta^{\rm R} - \eta
where :math:`\eta^{\rm R}` is eta value for reference alkane.
:type local: bool
:param local: use local eta
:type averaged: bool
:param averaged: averaged
"""
since = "1.0.0"
__slots__ = ("_local", "_averaged")
[docs] def description(self):
return "{}{}ETA functionality index".format(
"averaged " if self._averaged else "", "local " if self._local else ""
)
@classmethod
def preset(cls, version):
return (cls(l, a) for l in [False, True] for a in [False, True])
def __str__(self):
loc = "L" if self._local else ""
ave = "A" if self._averaged else ""
return "{}ETA_eta_F{}".format(ave, loc)
def parameters(self):
return self._local, self._averaged
def __init__(self, local=False, averaged=False):
self._local = local
self._averaged = averaged
def dependencies(self):
return {
"eta": EtaCompositeIndex(local=self._local),
"eta_R": EtaCompositeIndex(local=self._local, reference=True),
}
def calculate(self, eta, eta_R):
v = eta_R - eta
if self._averaged:
v /= self.mol.GetNumAtoms()
return v
[docs]class EtaBranchingIndex(EtaBase):
r"""ETA branching index descriptor.
.. math::
\eta^{\rm B} = \eta^{\rm local,N} - \eta^{local,R} + 0.086 N^{\rm R}
where :math:`\eta^{\rm local,N}` is :math:`\eta^{\rm local}` for normal alkane.
:math:`N^{\rm R}` is ring count.
:type ring: bool
:param ring: use ring count or not
:type averaged: bool
:param averaged: averaged
:returns: NaN when A < 2
"""
since = "1.0.0"
__slots__ = ("_ring", "_averaged")
[docs] def description(self):
return "{}ETA branching index{}".format(
"averaged " if self._averaged else "",
" (use ring count)" if self._ring else "",
)
@classmethod
def preset(cls, version):
return (cls(r, a) for r in [False, True] for a in [False, True])
def __str__(self):
ring = "R" if self._ring else ""
ave = "A" if self._averaged else ""
return "{}ETA_eta_B{}".format(ave, ring)
def parameters(self):
return self._ring, self._averaged
def __init__(self, ring=True, averaged=False):
self._ring = ring
self._averaged = averaged
def dependencies(self):
return {
"NR": RingCount() if self._ring else None,
"eta_RL": EtaCompositeIndex(reference=True, local=True),
}
def calculate(self, eta_RL, NR):
N = self.mol.GetNumAtoms()
if N <= 1:
self.fail(ValueError("single atom"))
elif N == 2:
eta_NL = 1.0
else:
eta_NL = np.sqrt(2) + 0.5 * (N - 3)
v = eta_NL - eta_RL + 0.086 * (NR or 0)
if self._averaged:
v /= N
return v
[docs]class EtaDeltaAlpha(EtaBase):
r"""ETA delta alpha descriptor.
.. math::
\Delta\alpha_{\rm A} = \max\left(\frac{\alpha - \alpha^{\rm R}}{A}, 0\right)
\Delta\alpha_{\rm B} = \max\left(\frac{\alpha^{\rm R} - \alpha}{A}, 0\right)
:type type: str
:param type: one of delta_types
"""
since = "1.0.0"
__slots__ = ("_type",)
[docs] def description(self):
return "ETA delta alpha (type: {})".format(self._type)
delta_types = ("A", "B")
@classmethod
def preset(cls, version):
return (cls(t) for t in cls.delta_types)
def __str__(self):
return "ETA_dAlpha_{}".format(self._type)
def parameters(self):
return (self._type,)
def __init__(self, type="A"):
assert type in self.delta_types
self._type = type
def dependencies(self):
return {"alpha": EtaCoreCount(), "alpha_R": EtaCoreCount(reference=True)}
def calculate(self, alpha, alpha_R):
if self._type == "A":
d = alpha - alpha_R
else:
d = alpha_R - alpha
return max(d / self.mol.GetNumAtoms(), 0.0)
[docs]class EtaEpsilon(EtaBase):
r"""ETA epsilon descriptor.
.. math::
\epsilon^i = \frac{\epsilon^i}{N^i} (i \leq 4)
\epsilon^5 = \frac{\epsilon^2 + \epsilon^{\rm XH}}{N^2 + N^{\rm XH}}
types(i)
1
all atoms
2
heavy atoms
3
all atoms of reference alkane
4
all atoms of saturated carbon skeleton(reduce C-C bonds)
XH
hydrogens bond to hetero atoms
:type type: str
:param type: one of epsilon_types
:returns: type = 3 and valence of any atoms > 4
"""
since = "1.0.0"
__slots__ = ("_type",)
[docs] def description(self):
return "ETA epsilon (type: {})".format(self._type)
@classmethod
def preset(cls, version):
return map(cls, cls.epsilon_types)
def __str__(self):
return "ETA_epsilon_{}".format(self._type)
@property
def explicit_hydrogens(self):
return self._type != 2
epsilon_types = tuple(range(1, 6))
def parameters(self):
return (self._type,)
def __init__(self, type=1):
self._type = type
def dependencies(self):
if self._type == 3:
return {"rmol": AlterMolecule(self.explicit_hydrogens)}
elif self._type == 4:
return {"rmol": AlterMolecule(self.explicit_hydrogens, True)}
def calculate(self, rmol=None):
mol = rmol if self._type in [3, 4] else self.mol
if self._type == 5:
eps = [
ap.get_eta_epsilon(a)
for a in mol.GetAtoms()
if a.GetAtomicNum() != 1 or a.GetNeighbors()[0].GetAtomicNum() != 6
]
return sum(eps) / len(eps)
return sum(ap.get_eta_epsilon(a) for a in mol.GetAtoms()) / mol.GetNumAtoms()
[docs]class EtaDeltaEpsilon(EtaBase):
r"""ETA delta epsilon descriptor.
.. math::
\Delta \epsilon^{\rm A} = \epsilon^1 - \epsilon^3
\Delta \epsilon^{\rm B} = \epsilon^1 - \epsilon^4
\Delta \epsilon^{\rm C} = \epsilon^3 - \epsilon^4
\Delta \epsilon^{\rm D} = \epsilon^2 - \epsilon^5
:type type: str
:param type: one of delta_epsilon_types
"""
since = "1.0.0"
__slots__ = ("_type",)
[docs] def description(self):
return "ETA delta epsilon (type: {})".format(self._type)
@classmethod
def preset(cls, version):
return map(cls, cls.delta_epsilon_types)
def __str__(self):
return "ETA_dEpsilon_{}".format(self._type)
delta_epsilon_types = tuple("ABCD")
def parameters(self):
return (self._type,)
def __init__(self, type="A"):
self._type = type
_deps = {"A": (1, 3), "B": (1, 4), "C": (3, 4), "D": (2, 5)}
def dependencies(self):
L, R = self._deps[self._type]
return {"L": EtaEpsilon(L), "R": EtaEpsilon(R)}
def calculate(self, L, R):
return L - R
[docs]class EtaDeltaBeta(EtaBase):
r"""ETA delta beta descriptor.
.. math::
\Delta\beta = \beta^{\rm ns} - \beta^{\rm s}
:type averaged: bool
:param averaged: averaged
"""
since = "1.0.0"
__slots__ = ("_averaged",)
[docs] def description(self):
return "{}ETA delta beta".format("averaged " if self._averaged else "")
@classmethod
def preset(cls, version):
return (cls(a) for a in [False, True])
def __str__(self):
ave = "A" if self._averaged else ""
return "{}ETA_dBeta".format(ave)
def parameters(self):
return (self._averaged,)
def __init__(self, averaged=False):
self._averaged = averaged
def dependencies(self):
return {"ns": EtaVEMCount("ns"), "s": EtaVEMCount("s")}
def calculate(self, ns, s):
v = ns - s
if self._averaged:
v /= self.mol.GetNumAtoms()
return v
[docs]class EtaPsi(EtaBase):
r"""ETA psi descriptor.
.. math::
\psi_1 = \frac{\alpha}{A \cdot \epsilon^2}
"""
since = "1.0.0"
__slots__ = ()
[docs] def description(self):
return "ETA psi"
@classmethod
def preset(cls, version):
yield cls()
def __str__(self):
return "ETA_psi_1"
def parameters(self):
return ()
def dependencies(self):
return {"a": EtaCoreCount(), "e": EtaEpsilon(2)}
def calculate(self, a, e):
return a / (self.mol.GetNumAtoms() * e)
[docs]class EtaDeltaPsi(EtaBase):
r"""ETA delta psi descriptor.
.. math::
\Delta\psi_{\rm A} = \max\left(0.714 - \psi_1, 0\right)
\Delta\psi_{\rm B} = \max\left(\psi_1 - 0.714, 0\right)
:type type: str
:param type: one of delta_psi_types
"""
since = "1.0.0"
__slots__ = ("_type",)
[docs] def description(self):
return "ETA delta psi (type: {})".format(self._type)
@classmethod
def preset(cls, version):
return map(cls, cls.delta_psi_types)
def __str__(self):
return "ETA_dPsi_{}".format(self._type)
delta_psi_types = ("A", "B")
def parameters(self):
return (self._type,)
def __init__(self, type="A"):
assert type in self.delta_psi_types
self._type = type
def dependencies(self):
return {"psi": EtaPsi()}
def calculate(self, psi):
L = 0.714
R = psi
if self._type == "B":
L, R = R, L
return max(L - R, 0.0)