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GetAtomCode = rdMolDescriptors.GetAtomPairAtomCode
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Imports: Chem, rdMolDescriptors, math
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**Arguments**: - the code to be considered - branchSubtract: (optional) the constant that was subtracted off the number of neighbors before integrating it into the code. This is used by the topological torsions code. >>> m = Chem.MolFromSmiles('C=CC(=O)O') >>> code = GetAtomCode(m.GetAtomWithIdx(0)) >>> ExplainAtomCode(code) ('C', 1, 1) >>> code = GetAtomCode(m.GetAtomWithIdx(1)) >>> ExplainAtomCode(code) ('C', 2, 1) >>> code = GetAtomCode(m.GetAtomWithIdx(2)) >>> ExplainAtomCode(code) ('C', 3, 1) >>> code = GetAtomCode(m.GetAtomWithIdx(3)) >>> ExplainAtomCode(code) ('O', 1, 1) >>> code = GetAtomCode(m.GetAtomWithIdx(4)) >>> ExplainAtomCode(code) ('O', 1, 0) |
Returns the number of electrons an atom is using for pi bonding >>> m = Chem.MolFromSmiles('C=C') >>> NumPiElectrons(m.GetAtomWithIdx(0)) 1 >>> m = Chem.MolFromSmiles('C#CC') >>> NumPiElectrons(m.GetAtomWithIdx(0)) 2 >>> NumPiElectrons(m.GetAtomWithIdx(1)) 2 >>> m = Chem.MolFromSmiles('O=C=CC') >>> NumPiElectrons(m.GetAtomWithIdx(0)) 1 >>> NumPiElectrons(m.GetAtomWithIdx(1)) 2 >>> NumPiElectrons(m.GetAtomWithIdx(2)) 1 >>> NumPiElectrons(m.GetAtomWithIdx(3)) 0 FIX: this behaves oddly in these cases: >>> m = Chem.MolFromSmiles('S(=O)(=O)') >>> NumPiElectrons(m.GetAtomWithIdx(0)) 2 >>> m = Chem.MolFromSmiles('S(=O)(=O)(O)O') >>> NumPiElectrons(m.GetAtomWithIdx(0)) 0 In the second case, the S atom is tagged as sp3 hybridized. |
Returns the number of bits in common between two vectors **Arguments**: - two vectors (sequences of bit ids) **Returns**: an integer **Notes** - the vectors must be sorted - duplicate bit IDs are counted more than once >>> BitsInCommon( (1,2,3,4,10), (2,4,6) ) 2 Here's how duplicates are handled: >>> BitsInCommon( (1,2,2,3,4), (2,2,4,5,6) ) 3 |
Implements the DICE similarity metric. This is the recommended metric in both the Topological torsions and Atom pairs papers. **Arguments**: - two vectors (sequences of bit ids) **Returns**: a float. **Notes** - the vectors must be sorted >>> DiceSimilarity( (1,2,3), (1,2,3) ) 1.0 >>> DiceSimilarity( (1,2,3), (5,6) ) 0.0 >>> DiceSimilarity( (1,2,3,4), (1,3,5,7) ) 0.5 >>> DiceSimilarity( (1,2,3,4,5,6), (1,3) ) 0.5 Note that duplicate bit IDs count multiple times: >>> DiceSimilarity( (1,1,3,4,5,6), (1,1) ) 0.5 but only if they are duplicated in both vectors: >>> DiceSimilarity( (1,1,3,4,5,6), (1,) )==2./7 True |
Returns the Dot product between two vectors: **Arguments**: - two vectors (sequences of bit ids) **Returns**: an integer **Notes** - the vectors must be sorted - duplicate bit IDs are counted more than once >>> Dot( (1,2,3,4,10), (2,4,6) ) 2 Here's how duplicates are handled: >>> Dot( (1,2,2,3,4), (2,2,4,5,6) ) 5 >>> Dot( (1,2,2,3,4), (2,4,5,6) ) 2 >>> Dot( (1,2,2,3,4), (5,6) ) 0 >>> Dot( (), (5,6) ) 0 |
Implements the Cosine similarity metric. This is the recommended metric in the LaSSI paper **Arguments**: - two vectors (sequences of bit ids) **Returns**: a float. **Notes** - the vectors must be sorted >>> print('%.3f'%CosineSimilarity( (1,2,3,4,10), (2,4,6) )) 0.516 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (2,2,4,5,6) )) 0.714 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (1,2,2,3,4) )) 1.000 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), (5,6,7) )) 0.000 >>> print('%.3f'%CosineSimilarity( (1,2,2,3,4), () )) 0.000 |
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