Python 摩根指纹数据库

通过一个例子，我意识到至少有两种方法可以使用

rdkit

计算分子的摩根指纹。但是在两种方法中使用完全相同的属性，我得到了不同的向量。我错过什么了吗

第一种方法：

info = {}
mol = Chem.MolFromSmiles('C/C1=C\\C[C@H]([C+](C)C)CC/C(C)=C/CC1')
fp = AllChem.GetMorganFingerprintAsBitVect(mol, useChirality=True, radius=2, nBits = 124, bitInfo=info)
vector = np.array(fp)
vector
array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1])

morgan_fp_gen = rdFingerprintGenerator.GetMorganGenerator(includeChirality=True, radius=2, fpSize=124)
mol = Chem.MolFromSmiles('C/C1=C\\C[C@H]([C+](C)C)CC/C(C)=C/CC1')
fp = morgan_fp_gen.GetFingerprint(mol)
vector = np.array(fp)
vector
array([0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0])

第二种方法：

info = {}
mol = Chem.MolFromSmiles('C/C1=C\\C[C@H]([C+](C)C)CC/C(C)=C/CC1')
fp = AllChem.GetMorganFingerprintAsBitVect(mol, useChirality=True, radius=2, nBits = 124, bitInfo=info)
vector = np.array(fp)
vector
array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1])

morgan_fp_gen = rdFingerprintGenerator.GetMorganGenerator(includeChirality=True, radius=2, fpSize=124)
mol = Chem.MolFromSmiles('C/C1=C\\C[C@H]([C+](C)C)CC/C(C)=C/CC1')
fp = morgan_fp_gen.GetFingerprint(mol)
vector = np.array(fp)
vector
array([0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0])

这显然是不同的，即使在这两种情况下使用手性

---

此外，还有一种方法可以使用第二种方法从位向量获取

bitInfo

。

默认情况下，Morgan生成器使用“计数模拟”：向位向量指纹添加额外的位，以获得位向量相似性。如果通过传递useCountSimulation=False关闭此选项，则指纹应等效：

mol = Chem.MolFromSmiles('C/C1=C\\C[C@H]([C+](C)C)CC/C(C)=C/CC1')
fp1 = AllChem.GetMorganFingerprintAsBitVect(mol, useChirality=True, radius=2, nBits=124)
vec1 = np.array(fp1)

morgan_fp_gen = rdFingerprintGenerator.GetMorganGenerator(includeChirality=True, radius=2, fpSize=124, useCountSimulation=False)
fp2 = morgan_fp_gen.GetFingerprint(mol)
vec2 = np.array(fp2)

assert np.all(vec1 == vec2) == True

至于

bitInfo

我不确定第二种方法是否可以做到这一点，尽管有人可能会纠正我