如果你的環境還沒準備好, 可以官方網站看如何配置環境:http://openbabel.org/wiki/Install_Python_bindings
1. 通過使用OBMol, OBAtom和OBBond來創建原子和鍵
import openbabel

mol = openbabel.OBMol()
print 'Should print 0 (atoms)'
print mol.NumAtoms()

a = mol.NewAtom()
a.SetAtomicNum(6)   # carbon atom
a.SetVector(0.0, 1.0, 2.0) # coordinates

b = mol.NewAtom()
mol.AddBond(1, 2, 1)   # atoms indexed from 1
print 'Should print 2 (atoms)'
print mol.NumAtoms()
print 'Should print 1 (bond)'
print mol.NumBonds()

mol.Clear();

2. 通過OBConversion來讀取分子, 并輸出不同格式文件或字符串值
import openbabel

obConversion = openbabel.OBConversion()
obConversion.SetInAndOutFormats("smi", "mdl")    //讀取smiles值, 然后輸出mdl值

mol = openbabel.OBMol()
obConversion.ReadString(mol, "C1=CC=CS1")

print 'Should print 5 (atoms)'
print mol.NumAtoms()

mol.AddHydrogens()
print 'Should print 9 (atoms) after adding hydrogens'
print mol.NumAtoms()      //輸出原子個數

outMDL = obConversion.WriteString(mol)

3. 計算fp值
import pybel
smiles = ['CCCC', 'CCCN']
mols = [pybel.readstring("smi", x) for x in smiles]   # Create two molecules from the SMILES
fps = [x.calcfp() for x in mols]   # Calculate their fingerprints
print fps[0].bits, fps[1].bits
print fps[0].fp[0]

mol2 = pybel.readstring('smi', 'c2ccc1ccccc1c2')
fp2 = mol2.calcfp("FP4")
print fp2
print fp2.bits

mol3 = pybel.readstring('smi', 'C1CCCCC1')
fp3 = mol3.calcfp()

print fp3.__or__(fp2)  //計算相似度值

4. 讀取sdf文件
#encoding=utf-8
import pybel
for mymol in pybel.readfile("sdf", "structures_all.sdf"):
    fp = mymol.calcfp("FP2")
    print fp

5. 輸出txt文件和sdf文件

print mymol.write("smi")    //'CCCC'
mymol.write("smi", "outputfile.txt")
largeSDfile = Outputfile("sdf", "multipleSD.sdf")
largeSDfile.write(mymol)
largeSDfile.write(myothermol)
largeSDfile.close()