CDPL.Descr Package Reference

Contains classes and functions related to the generation and processing of pharmacophore and molecule descriptors. More...

Classes
class	AtomAutoCorrelation3DVectorCalculator
	AtomAutoCorrelation3DVectorCalculator. More...
class	AtomRDFCodeCalculator
	AtomRDFCodeCalculator. More...
class	AutoCorrelation2DVectorCalculator
	AutoCorrelation2DVectorCalculator. More...
class	BCUTDescriptorCalculator
	BCUTDescriptorCalculator. More...
class	BitSetBulkSimilarityCalculator
class	BurdenMatrixGenerator
	BurdenMatrixGenerator. More...
class	CircularFingerprintGenerator
	CircularFingerprintGenerator. More...
class	CosineSimilarity
	Functor class for calculating Cosine Similarities [WCOS] of bitsets and vectors. More...
class	DVectorBulkSimilarityCalculator
class	DiceSimilarity
	Functor class for calculating the Dice Similarity [GSIM] of bitsets. More...
class	DoubleBitSet2Functor
class	DoubleDVector2Functor
class	DoubleFVector2Functor
class	DoubleLVector2Functor
class	DoubleULVector2Functor
class	EuclideanDistance
	Functor class for calculating the Euclidean Distance [CITB] between bitsets and vectors. More...
class	EuclideanSimilarity
	Functor class for calculating the Euclidean Similarity [GSIM] of bitsets. More...
class	FVectorBulkSimilarityCalculator
class	FeatureAutoCorrelation3DVectorCalculator
	FeatureAutoCorrelation3DVectorCalculator. More...
class	FeatureRDFCodeCalculator
	FeatureRDFCodeCalculator. More...
class	HammingDistance
	Functor class for calculating the Hamming Distance [WHAM, CITB] between bitsets. More...
class	LVectorBulkSimilarityCalculator
class	MACCSFingerprintGenerator
	Generation of 166 bit MACCS key fingerprints. More...
class	ManhattanDistance
	Functor class for calculating the Manhattan Distance [MADI] between bitsets and vectors. More...
class	ManhattanSimilarity
	Functor class for calculating the Manhattan Similarity [GSIM] of bitsets. More...
class	MolecularComplexityCalculator
	MolecularComplexityCalculator. More...
class	MoleculeAutoCorr2DDescriptorCalculator
	MoleculeAutoCorr2DDescriptorCalculator. More...
class	MoleculeAutoCorr3DDescriptorCalculator
	MoleculeAutoCorr3DDescriptorCalculator. More...
class	MoleculeRDFDescriptorCalculator
	MoleculeRDFDescriptorCalculator. More...
class	NPoint2DPharmacophoreFingerprintGenerator
	NPoint2DPharmacophoreFingerprintGenerator. More...
class	NPoint3DPharmacophoreFingerprintGenerator
	NPoint3DPharmacophoreFingerprintGenerator. More...
class	NPointPharmacophoreFingerprintGenerator
	NPointPharmacophoreFingerprintGenerator. More...
class	PathFingerprintGenerator
	PathFingerprintGenerator. More...
class	PharmacophoreAutoCorr3DDescriptorCalculator
	PharmacophoreAutoCorr3DDescriptorCalculator. More...
class	PharmacophoreRDFDescriptorCalculator
	PharmacophoreRDFDescriptorCalculator. More...
class	PubChemFingerprintGenerator
	Generation of 881 bit PubChem fingerprints. More...
class	TanimotoSimilarity
	Functor class for calculating Tanimoto Similarities [CITB] of bitsets and vectors. More...
class	TverskySimilarity
	Functor class for calculating the Tversky Similarity [GSIM] of bitsets. More...
class	ULVectorBulkSimilarityCalculator

Functions
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 [GSIM] of the bitsets bs1 and bs2. More...
float	calcCosineSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Cosine Similarity [WCOS] of the bitsets bs1 and bs2. More...
float	calcEuclideanSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Euclidean Similarity [GSIM] of the bitsets bs1 and bs2. More...
float	calcManhattanSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Manhattan Similarity [GSIM] of the bitsets bs1 and bs2. More...
float	calcTanimotoSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Tanimoto Similarity [CITB] of the bitsets bs1 and bs2. More...
float	calcTverskySimilarity (Util.BitSet bs1, Util.BitSet bs2, float a, float b)
	Calculates the Tversky Similarity [GSIM] of the bitsets bs1 and bs2. More...
float	calcEuclideanDistance (Math.DVector v1, Math.DVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...
float	calcManhattanDistance (Math.DVector v1, Math.DVector v2)
	Calculates the Manhattan Distance [MADI] between the vectors v1 and v2. More...
float	calcCosineSimilarity (Math.DVector v1, Math.DVector v2)
	Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2. More...
float	calcTanimotoSimilarity (Math.DVector v1, Math.DVector v2)
	Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2. More...
float	calcGeometricalDiameter (Chem.Entity3DContainer cntnr)
float	calcGeometricalRadius (Chem.Entity3DContainer cntnr)
float	calcEuclideanDistance (Math.FVector v1, Math.FVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...
float	calcManhattanDistance (Math.FVector v1, Math.FVector v2)
	Calculates the Manhattan Distance [MADI] between the vectors v1 and v2. More...
float	calcCosineSimilarity (Math.FVector v1, Math.FVector v2)
	Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2. More...
float	calcTanimotoSimilarity (Math.FVector v1, Math.FVector v2)
	Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2. More...
float	calcEuclideanDistance (Math.LVector v1, Math.LVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...
float	calcManhattanDistance (Math.LVector v1, Math.LVector v2)
	Calculates the Manhattan Distance [MADI] between the vectors v1 and v2. More...
float	calcCosineSimilarity (Math.LVector v1, Math.LVector v2)
	Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2. More...
float	calcTanimotoSimilarity (Math.LVector v1, Math.LVector v2)
	Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2. More...
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)
float	calcEuclideanDistance (Math.ULVector v1, Math.ULVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...
float	calcManhattanDistance (Math.ULVector v1, Math.ULVector v2)
	Calculates the Manhattan Distance [MADI] between the vectors v1 and v2. More...
float	calcCosineSimilarity (Math.ULVector v1, Math.ULVector v2)
	Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2. More...
float	calcTanimotoSimilarity (Math.ULVector v1, Math.ULVector v2)
	Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2. More...

Detailed Description

Contains classes and functions related to the generation and processing of pharmacophore and molecule descriptors.

Function Documentation

calcGeometricalDiameter() [1/2]

float CDPL.Descr.calcGeometricalDiameter	(	Chem.AtomContainer	cntnr,
		Chem.Atom3DCoordinatesFunction	coords_func
	)

Parameters

cntnr
coords_func

Returns

calcGeometricalRadius() [1/2]

float CDPL.Descr.calcGeometricalRadius	(	Chem.AtomContainer	cntnr,
		Chem.Atom3DCoordinatesFunction	coords_func
	)

Parameters

cntnr
coords_func

Returns

calcHammingDistance()

int CDPL.Descr.calcHammingDistance	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

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.

calcEuclideanDistance() [1/5]

float CDPL.Descr.calcEuclideanDistance	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

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.

calcDiceSimilarity()

float CDPL.Descr.calcDiceSimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

Calculates the Dice Similarity [GSIM] of the bitsets bs1 and bs2.

The Dice Similarity $S_{ab}$ 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.

calcCosineSimilarity() [1/5]

float CDPL.Descr.calcCosineSimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

Calculates the Cosine Similarity [WCOS] of the bitsets bs1 and bs2.

The Cosine Similarity $S_{ab}$ 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.

calcEuclideanSimilarity()

float CDPL.Descr.calcEuclideanSimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

Calculates the Euclidean Similarity [GSIM] of the bitsets bs1 and bs2.

The Euclidean Similarity $S_{ab}$ 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.

calcManhattanSimilarity()

float CDPL.Descr.calcManhattanSimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

Calculates the Manhattan Similarity [GSIM] of the bitsets bs1 and bs2.

The Manhattan Similarity $S_{ab}$ 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.

calcTanimotoSimilarity() [1/5]

float CDPL.Descr.calcTanimotoSimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2
	)

Calculates the Tanimoto Similarity [CITB] of the bitsets bs1 and bs2.

The Tanimoto Similarity $S_{ab}$ 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.

calcTverskySimilarity()

float CDPL.Descr.calcTverskySimilarity	(	Util.BitSet	bs1,
		Util.BitSet	bs2,
		float	a,
		float	b
	)

Calculates the Tversky Similarity [GSIM] of the bitsets bs1 and bs2.

The Tversky Similarity $S_{ab}$ 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 $a = b = 1.0$ makes it identical to 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.

calcEuclideanDistance() [2/5]

float CDPL.Descr.calcEuclideanDistance	(	Math.DVector	v1,
		Math.DVector	v2
	)

Calculates the Euclidean Distance [CITB] between the vectors v1 and v2.

The Euclidean Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcManhattanDistance() [1/4]

float CDPL.Descr.calcManhattanDistance	(	Math.DVector	v1,
		Math.DVector	v2
	)

Calculates the Manhattan Distance [MADI] between the vectors v1 and v2.

The Manhattan Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |}_1 ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcCosineSimilarity() [2/5]

float CDPL.Descr.calcCosineSimilarity	(	Math.DVector	v1,
		Math.DVector	v2
	)

Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2.

The Cosine Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}{\left | \vec{v}_2 \right |}} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcTanimotoSimilarity() [2/5]

float CDPL.Descr.calcTanimotoSimilarity	(	Math.DVector	v1,
		Math.DVector	v2
	)

Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2.

The Tanimoto Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}^2 + {\left | \vec{v}_2 \right |}^2 - \vec{v}_1 \cdot \vec{v}_2} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcGeometricalDiameter() [2/2]

float CDPL.Descr.calcGeometricalDiameter

(

Chem.Entity3DContainer

cntnr

)

Parameters

cntnr

Returns

calcGeometricalRadius() [2/2]

float CDPL.Descr.calcGeometricalRadius

(

Chem.Entity3DContainer

cntnr

)

Parameters

cntnr

Returns

calcEuclideanDistance() [3/5]

float CDPL.Descr.calcEuclideanDistance	(	Math.FVector	v1,
		Math.FVector	v2
	)

Calculates the Euclidean Distance [CITB] between the vectors v1 and v2.

The Euclidean Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcManhattanDistance() [2/4]

float CDPL.Descr.calcManhattanDistance	(	Math.FVector	v1,
		Math.FVector	v2
	)

Calculates the Manhattan Distance [MADI] between the vectors v1 and v2.

The Manhattan Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |}_1 ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcCosineSimilarity() [3/5]

float CDPL.Descr.calcCosineSimilarity	(	Math.FVector	v1,
		Math.FVector	v2
	)

Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2.

The Cosine Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}{\left | \vec{v}_2 \right |}} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcTanimotoSimilarity() [3/5]

float CDPL.Descr.calcTanimotoSimilarity	(	Math.FVector	v1,
		Math.FVector	v2
	)

Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2.

The Tanimoto Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}^2 + {\left | \vec{v}_2 \right |}^2 - \vec{v}_1 \cdot \vec{v}_2} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcEuclideanDistance() [4/5]

float CDPL.Descr.calcEuclideanDistance	(	Math.LVector	v1,
		Math.LVector	v2
	)

Calculates the Euclidean Distance [CITB] between the vectors v1 and v2.

The Euclidean Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcManhattanDistance() [3/4]

float CDPL.Descr.calcManhattanDistance	(	Math.LVector	v1,
		Math.LVector	v2
	)

Calculates the Manhattan Distance [MADI] between the vectors v1 and v2.

The Manhattan Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |}_1 ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcCosineSimilarity() [4/5]

float CDPL.Descr.calcCosineSimilarity	(	Math.LVector	v1,
		Math.LVector	v2
	)

Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2.

The Cosine Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}{\left | \vec{v}_2 \right |}} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcTanimotoSimilarity() [4/5]

float CDPL.Descr.calcTanimotoSimilarity	(	Math.LVector	v1,
		Math.LVector	v2
	)

Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2.

The Tanimoto Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}^2 + {\left | \vec{v}_2 \right |}^2 - \vec{v}_1 \cdot \vec{v}_2} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcKierShape1()

float CDPL.Descr.calcKierShape1

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcZagrebIndex1()

int CDPL.Descr.calcZagrebIndex1

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcKierShape2()

float CDPL.Descr.calcKierShape2

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcZagrebIndex2()

int CDPL.Descr.calcZagrebIndex2

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcKierShape3()

float CDPL.Descr.calcKierShape3

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcTopologicalDiameter()

int CDPL.Descr.calcTopologicalDiameter

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcTopologicalRadius()

int CDPL.Descr.calcTopologicalRadius

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcTotalWalkCount()

int CDPL.Descr.calcTotalWalkCount

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcRandicIndex()

float CDPL.Descr.calcRandicIndex

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcWienerIndex()

int CDPL.Descr.calcWienerIndex

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcRingComplexity()

float CDPL.Descr.calcRingComplexity

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcMolecularComplexity()

float CDPL.Descr.calcMolecularComplexity

(

Chem.MolecularGraph

molgraph

)

Parameters

molgraph

Returns

calcEuclideanDistance() [5/5]

float CDPL.Descr.calcEuclideanDistance	(	Math.ULVector	v1,
		Math.ULVector	v2
	)

Calculates the Euclidean Distance [CITB] between the vectors v1 and v2.

The Euclidean Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcManhattanDistance() [4/4]

float CDPL.Descr.calcManhattanDistance	(	Math.ULVector	v1,
		Math.ULVector	v2
	)

Calculates the Manhattan Distance [MADI] between the vectors v1 and v2.

The Manhattan Distance $D_{12}$ is calculated by:

[ D_{12} = {\left | \vec{v}_1 - \vec{v}_2 \right |}_1 ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated distance measure.

calcCosineSimilarity() [5/5]

float CDPL.Descr.calcCosineSimilarity	(	Math.ULVector	v1,
		Math.ULVector	v2
	)

Calculates the Cosine Similarity [WCOS] of the vectors v1 and v2.

The Cosine Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}{\left | \vec{v}_2 \right |}} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.

calcTanimotoSimilarity() [5/5]

float CDPL.Descr.calcTanimotoSimilarity	(	Math.ULVector	v1,
		Math.ULVector	v2
	)

Calculates the Tanimoto Similarity [CITB] of the vectors v1 and v2.

The Tanimoto Similarity $S_{12}$ is calculated by:

[ S_{12} = \frac{\vec{v}_1 \cdot \vec{v}_2}{{\left | \vec{v}_1 \right |}^2 + {\left | \vec{v}_2 \right |}^2 - \vec{v}_1 \cdot \vec{v}_2} ]

Parameters

v1	The first vector.
v2	The second vector.

Returns

The calculated similarity measure.