Source code for moran
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2017, Zhijiang Yao, Jie Dong and Dongsheng Cao
# All rights reserved.
# This file is part of the PyBioMed.
# The contents are covered by the terms of the BSD license
# which is included in the file license.txt, found at the root
# of the PyBioMed source tree.
"""
##############################################################################
The calculation of Moran autocorrelation descriptors. You can get 32 molecular
decriptors. You can freely use and distribute it. If you hava any problem,
you could contact with us timely!
Authors: Zhijiang Yao and Dongsheng Cao.
Date: 2016.06.04
Email: gadsby@163.com and oriental-cds@163.com
##############################################################################
"""
from rdkit import Chem
from AtomProperty import GetRelativeAtomicProperty
import numpy
Version=1.0
################################################################
def _CalculateMoranAutocorrelation(mol,lag=1,propertylabel='m'):
"""
#################################################################
**Internal used only**
Calculation of Moran autocorrelation descriptors based on
different property weights.
Usage:
res=_CalculateMoranAutocorrelation(mol,lag=1,propertylabel='m')
Input: mol is a molecule object.
lag is the topological distance between atom i and atom j.
propertylabel is the weighted property.
Output: res is a numeric value.
#################################################################
"""
Natom=mol.GetNumAtoms()
prolist=[]
for i in mol.GetAtoms():
temp=GetRelativeAtomicProperty(i.GetSymbol(),propertyname=propertylabel)
prolist.append(temp)
aveweight=sum(prolist)/Natom
tempp=[numpy.square(x-aveweight) for x in prolist]
GetDistanceMatrix=Chem.GetDistanceMatrix(mol)
res=0.0
index=0
for i in range(Natom):
for j in range(Natom):
if GetDistanceMatrix[i,j]==lag:
atom1=mol.GetAtomWithIdx(i)
atom2=mol.GetAtomWithIdx(j)
temp1=GetRelativeAtomicProperty(element=atom1.GetSymbol(),propertyname=propertylabel)
temp2=GetRelativeAtomicProperty(element=atom2.GetSymbol(),propertyname=propertylabel)
res=res+(temp1-aveweight)*(temp2-aveweight)
index=index+1
else:
res=res+0.0
if sum(tempp)==0 or index==0:
result=0
else:
result=(res/index)/(sum(tempp)/Natom)
return round(result,3)
[docs]def CalculateMoranAutoMass(mol):
"""
#################################################################
Calculation of Moran autocorrelation descriptors based on
carbon-scaled atomic mass.
Usage:
res=CalculateMoranAutoMass(mol)
Input: mol is a molecule object.
Output: res is a dict form containing eight moran autocorrealtion
descriptors.
#################################################################
"""
res={}
for i in range(8):
res['MATSm'+str(i+1)]=_CalculateMoranAutocorrelation(mol,lag=i+1,propertylabel='m')
return res
[docs]def CalculateMoranAutoVolume(mol):
"""
#################################################################
Calculation of Moran autocorrelation descriptors based on
carbon-scaled atomic van der Waals volume.
Usage:
res=CalculateMoranAutoVolume(mol)
Input: mol is a molecule object.
Output: res is a dict form containing eight moran autocorrealtion
descriptors.
#################################################################
"""
res={}
for i in range(8):
res['MATSv'+str(i+1)]=_CalculateMoranAutocorrelation(mol,lag=i+1,propertylabel='V')
return res
[docs]def CalculateMoranAutoElectronegativity(mol):
"""
#################################################################
Calculation of Moran autocorrelation descriptors based on
carbon-scaled atomic Sanderson electronegativity.
Usage:
res=CalculateMoranAutoElectronegativity(mol)
Input: mol is a molecule object.
Output: res is a dict form containing eight moran autocorrealtion
descriptors.
#################################################################
"""
res={}
for i in range(8):
res['MATSe'+str(i+1)]=_CalculateMoranAutocorrelation(mol,lag=i+1,propertylabel='En')
return res
[docs]def CalculateMoranAutoPolarizability(mol):
"""
#################################################################
Calculation of Moran autocorrelation descriptors based on
carbon-scaled atomic polarizability.
Usage:
res=CalculateMoranAutoPolarizability(mol)
Input: mol is a molecule object.
Output: res is a dict form containing eight moran autocorrealtion
descriptors.
#################################################################
"""
res={}
for i in range(8):
res['MATSp'+str(i+1)]=_CalculateMoranAutocorrelation(mol,lag=i+1,propertylabel='alapha')
return res
[docs]def GetMoranAuto(mol):
"""
#################################################################
Calcualate all Moran autocorrelation descriptors.
(carbon-scaled atomic mass, carbon-scaled atomic van der Waals volume,
carbon-scaled atomic Sanderson electronegativity,
carbon-scaled atomic polarizability)
Usage:
res=GetMoranAuto(mol)
Input: mol is a molecule object.
Output: res is a dict form containing all moran autocorrealtion
descriptors.
#################################################################
"""
res={}
res.update(CalculateMoranAutoMass(mol))
res.update(CalculateMoranAutoVolume(mol))
res.update(CalculateMoranAutoElectronegativity(mol))
res.update(CalculateMoranAutoPolarizability(mol))
return res
###########################################################################
if __name__=='__main__':
smi5=['COCCCC','CCC(C)CC','CC(C)CCC','CC(C)C(C)C','CCOCCN','c1ccccc1N']
smis = ['CCCC','CCCCC','CCCCCC','CC(N)C(=O)O','CC(N)C(=O)[O-].[Na+]']
for index, smi in enumerate(smis):
m = Chem.MolFromSmiles(smi)
print index+1
print smi
## print '\t',CalculateEstateFingerprint(m)
## print '\t',CalculateEstateValue(m)
## print '\t',CalculateMaxAtomTypeEState(m)
## print '\t', CalculateMinAtomTypeEState(m)
print GetMoranAuto(m)
