Contains classes and functions related to the generation and processing of pharmacophore and molecule descriptors.
More...
|
| float | calcGeometricalDiameter (Chem.AtomContainer cntnr, Chem.Atom3DCoordinatesFunction coords_func) |
| |
| float | calcGeometricalRadius (Chem.AtomContainer cntnr, Chem.Atom3DCoordinatesFunction coords_func) |
| |
| int | calcHammingDistance (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Hamming Distance [WHAM, CITB] between the bitsets bs1 and bs2. More...
|
| |
| float | calcEuclideanDistance (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Euclidean Distance [CITB] between the bitsets bs1 and bs2. More...
|
| |
| float | calcDiceSimilarity (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Dice Similarity Measure [GSIM] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcCosineSimilarity (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Cosine Similarity Measure [WCOS] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcEuclideanSimilarity (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Euclidean Similarity Measure [GSIM] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcManhattanSimilarity (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Manhattan Similarity Measure [GSIM] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcTanimotoSimilarity (Util.BitSet bs1, Util.BitSet bs2) |
| | Calculates the Tanimoto Similarity Measure [CITB] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcTverskySimilarity (Util.BitSet bs1, Util.BitSet bs2, float a, float b) |
| | Calculates the Tversky Similarity Measure [GSIM] for the given bitsets bs1 and bs2. More...
|
| |
| float | calcGeometricalDiameter (Chem.Entity3DContainer cntnr) |
| |
| float | calcGeometricalRadius (Chem.Entity3DContainer cntnr) |
| |
| float | calcKierShape1 (Chem.MolecularGraph molgraph) |
| |
| int | calcZagrebIndex1 (Chem.MolecularGraph molgraph) |
| |
| float | calcKierShape2 (Chem.MolecularGraph molgraph) |
| |
| int | calcZagrebIndex2 (Chem.MolecularGraph molgraph) |
| |
| float | calcKierShape3 (Chem.MolecularGraph molgraph) |
| |
| int | calcTopologicalDiameter (Chem.MolecularGraph molgraph) |
| |
| int | calcTopologicalRadius (Chem.MolecularGraph molgraph) |
| |
| int | calcTotalWalkCount (Chem.MolecularGraph molgraph) |
| |
| float | calcRandicIndex (Chem.MolecularGraph molgraph) |
| |
| int | calcWienerIndex (Chem.MolecularGraph molgraph) |
| |
| float | calcRingComplexity (Chem.MolecularGraph molgraph) |
| |
| float | calcMolecularComplexity (Chem.MolecularGraph molgraph) |
| |
Contains classes and functions related to the generation and processing of pharmacophore and molecule descriptors.
Calculates the Hamming Distance [WHAM, CITB] between the bitsets bs1 and bs2.
The Hamming Distance \( D_{ab} \) is calculated by:
[ D_{ab} = N_a + N_b ]
where \( N_a \) is the number of bits that are set in the first bitset but not in the second bitset and \( N_b \) is the number of bits that are set in the second bitset but not in the first one.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated distance.
Calculates the Euclidean Distance [CITB] between the bitsets bs1 and bs2.
The Euclidean Distance \( D_{ab} \) is calculated by:
[ D_{ab} = \sqrt{N_a + N_b} ]
where \( N_a \) is the number of bits that are set in the first bitset but not in the second bitset and \( N_b \) is the number of bits that are set in the second bitset but not in the first one.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated distance.
Calculates the Dice Similarity Measure [GSIM] for the given bitsets bs1 and bs2.
The Dice Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \frac{2 * N_{ab}}{N_a + N_b + 2 * N_{ab}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are only set in the first bitset and \( N_b \) is the number of bits that are only set in the second bitset.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated similarity measure.
Calculates the Cosine Similarity Measure [WCOS] for the given bitsets bs1 and bs2.
The Cosine Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \frac{N_{ab}}{\sqrt{N_a * N_b}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are set in the first bitset and \( N_b \) is the number of bits that are set in the second bitset.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated similarity measure.
Calculates the Euclidean Similarity Measure [GSIM] for the given bitsets bs1 and bs2.
The Euclidean Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \sqrt{\frac{N_{ab} + N_{!ab}}{N_a + N_b + N_{ab} + N_{!ab}}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are set only in the first bitset, \( N_b \) is the number of bits that are set only in the second bitset and \( N_{!ab} \) is the number of bits that are not set in both bitsets.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated similarity measure.
Calculates the Manhattan Similarity Measure [GSIM] for the given bitsets bs1 and bs2.
The Manhattan Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \frac{N_a + N_b}{N_a + N_b + N_{ab} + N_{!ab}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are set only in the first bitset, \( N_b \) is the number of bits that are set only in the second bitset and \( N_{!ab} \) is the number of bits that are not set in both bitsets.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated similarity measure.
Calculates the Tanimoto Similarity Measure [CITB] for the given bitsets bs1 and bs2.
The Tanimoto Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \frac{N_{ab}}{N_a + N_b - N_{ab}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are set in the first bitset and \( N_b \) is the number of bits that are set in the second bitset.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
- Returns
- The calculated similarity measure.
Calculates the Tversky Similarity Measure [GSIM] for the given bitsets bs1 and bs2.
The Tversky Similarity Measure \( S_{ab} \) is calculated by:
[ S_{ab} = \frac{N_{ab}}{a * N_a + b * N_b + N_{ab}} ]
where \( N_{ab} \) is the number of bits that are set in both bitsets, \( N_a \) is the number of bits that are only set in the first bitset and \( N_b \) is the number of bits that are only set in the second bitset. \( a \) and \( b \) are bitset contribution weighting factors.
The Tversky Measure is asymmetric. Setting the parameters \( a = b = 1.0 \) is identical to using the Tanimoto measure.
If the specified bitsets bs1 and bs2 are of different size, missing bits at the end of the smaller bitset are assumed to be zero.
- Parameters
-
| bs1 | The first bitset. |
| bs2 | The second bitset. |
| a | Weights the contribution of the first bitset. |
| b | Weights the contribution of the second bitset. |
- Returns
- The calculated similarity measure.
1.9.1