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

Classes
class	AtomAutoCorrelation3DVectorCalculator
	AutoCorrelation3DVectorCalculator implementation for the calculation of atom-centered 3D auto-correlation vectors of chemical structures. More...

class	AtomRDFCodeCalculator
	RDFCodeCalculator implementation for the calculation of atom-centered radial distribution function (RDF) codes of chemical structures. More...

class	AutoCorrelation2DVectorCalculator
	Calculation of the topological (2D) auto-correlation vector of a molecular graph. More...

class	BCUTDescriptorCalculator
	Calculation of the BCUT (Burden-CAS-University-of-Texas) descriptor of a molecular graph. More...

class	BitSetBulkSimilarityCalculator

class	BurdenMatrixGenerator
	Calculation of the Burden matrix of a molecular graph. More...

class	CircularFingerprintGenerator
	Generation of atom-centered circular substructure fingerprints in the spirit of SciTegic's Extended Connectivity Fingerprints (ECFP). 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
	AutoCorrelation3DVectorCalculator specialization for feature-centered 3D auto-correlation vectors of pharmacophores. More...

class	FeatureRDFCodeCalculator
	RDFCodeCalculator implementation for the calculation of feature-centered radial distribution function (RDF) codes of pharmacophores. More...

class	HammingDistance
	Functor class for calculating the Hamming Distance [WHAM, CITB] between bitsets. More...

class	KuvekPocketDescriptorCalculator
	Implements the algorithm devised by Kuvek et al. 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
	Calculation of the molecular complexity of a molecular graph after Hendrickson, Huang and Toczko. More...

class	MoleculeAutoCorr2DDescriptorCalculator
	Calculation of a topological (2D) auto-correlation descriptor of a molecular graph partitioned by specific atom types. More...

class	MoleculeAutoCorr3DDescriptorCalculator
	Calculation of a 3D auto-correlation descriptor of a molecular graph partitioned by specific atom types. More...

class	MoleculeRDFDescriptorCalculator
	Calculation of an RDF descriptor of a molecular graph partitioned by specific atom types. More...

class	NPoint2DPharmacophoreFingerprintGenerator
	N-point pharmacophore fingerprint generator that uses topological feature distances. More...

class	NPoint3DPharmacophoreFingerprintGenerator
	N-point pharmacophore fingerprint generator that uses spatial 3D feature distances. More...

class	NPointPharmacophoreFingerprintGenerator
	Abstract base for N-point pharmacophore fingerprint generators. More...

class	PathFingerprintGenerator
	Generation of Daylight-style path fingerprints of molecular graphs. More...

class	PharmacophoreAutoCorr3DDescriptorCalculator
	Calculation of a 3D auto-correlation descriptor of a pharmacophore partitioned by specific feature types. More...

class	PharmacophoreRDFDescriptorCalculator
	Calculation of an RDF descriptor of a pharmacophore partitioned by specific feature types. 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	calcCosineSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Cosine Similarity [WCOS] of the bitsets bs1 and bs2. More...

float	calcCosineSimilarity (Math.DVector v1, Math.DVector v2)
	Calculates the Cosine Similarity [WCOS] of 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	calcCosineSimilarity (Math.LVector v1, Math.LVector v2)
	Calculates the Cosine Similarity [WCOS] of 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	calcDiceSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Dice Similarity [GSIM] of 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	calcEuclideanDistance (Math.DVector v1, Math.DVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...

float	calcEuclideanDistance (Math.FVector v1, Math.FVector v2)
	Calculates the Euclidean Distance [CITB] between 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	calcEuclideanDistance (Math.ULVector v1, Math.ULVector v2)
	Calculates the Euclidean Distance [CITB] between the vectors v1 and v2. More...

float	calcEuclideanSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Euclidean Similarity [GSIM] of the bitsets bs1 and bs2. More...

float	calcGeometricalDiameter (Chem.AtomContainer cntnr, Chem.Atom3DCoordinatesFunction coords_func)
	Calculates the geometrical diameter of the atoms in cntnr. More...

float	calcGeometricalDiameter (Chem.Entity3DContainer cntnr)
	Calculates the geometrical diameter of the entities in cntnr. More...

float	calcGeometricalRadius (Chem.AtomContainer cntnr, Chem.Atom3DCoordinatesFunction coords_func)
	Calculates the geometrical radius of the atoms in cntnr. More...

float	calcGeometricalRadius (Chem.Entity3DContainer cntnr)
	Calculates the geometrical radius of the entities in cntnr. More...

int	calcHammingDistance (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Hamming Distance [WHAM, CITB] between the bitsets bs1 and bs2. More...

float	calcKierShape1 (Chem.MolecularGraph molgraph)
	Calculates the first Kier shape index ( \( \kappa_1 \)) of molgraph. More...

float	calcKierShape2 (Chem.MolecularGraph molgraph)
	Calculates the second Kier shape index ( \( \kappa_2 \)) of molgraph. More...

float	calcKierShape3 (Chem.MolecularGraph molgraph)
	Calculates the third Kier shape index ( \( \kappa_3 \)) of molgraph. More...

float	calcManhattanDistance (Math.DVector v1, Math.DVector v2)
	Calculates the Manhattan Distance [MADI] 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	calcManhattanDistance (Math.LVector v1, Math.LVector v2)
	Calculates the Manhattan Distance [MADI] 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	calcManhattanSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Manhattan Similarity [GSIM] of the bitsets bs1 and bs2. More...

float	calcMolecularComplexity (Chem.MolecularGraph molgraph)
	Calculates the molecular complexity of molgraph (Hendrickson, Huang and Toczko). More...

float	calcRandicIndex (Chem.MolecularGraph molgraph)
	Calculates the Randic connectivity index of molgraph. More...

float	calcRingComplexity (Chem.MolecularGraph molgraph)
	Calculates the ring complexity of molgraph. More...

float	calcTanimotoSimilarity (Util.BitSet bs1, Util.BitSet bs2)
	Calculates the Tanimoto Similarity [CITB] of the bitsets bs1 and bs2. More...

float	calcTanimotoSimilarity (Math.DVector v1, Math.DVector v2)
	Calculates the Tanimoto Similarity [CITB] 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	calcTanimotoSimilarity (Math.LVector v1, Math.LVector v2)
	Calculates the Tanimoto Similarity [CITB] 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...

int	calcTopologicalDiameter (Chem.MolecularGraph molgraph)
	Calculates the topological diameter (maximum atom-pair topological distance) of molgraph. More...

int	calcTopologicalRadius (Chem.MolecularGraph molgraph)
	Calculates the topological radius (minimum atom eccentricity) of molgraph. More...

int	calcTotalWalkCount (Chem.MolecularGraph molgraph)
	Calculates the total walk count of molgraph. 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...

int	calcWienerIndex (Chem.MolecularGraph molgraph)
	Calculates the Wiener index of molgraph. More...

int	calcZagrebIndex1 (Chem.MolecularGraph molgraph)
	Calculates the first Zagreb index ( \( M_1 \)) of molgraph. More...

int	calcZagrebIndex2 (Chem.MolecularGraph molgraph)
	Calculates the second Zagreb index ( \( M_2 \)) of molgraph. More...

Detailed Description

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

Function Documentation

◆ 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.

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

◆ 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.

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

◆ calcGeometricalDiameter() [1/2]

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

Calculates the geometrical diameter of the atoms in cntnr.

The geometrical diameter is the maximum distance between any pair of atoms in the container. If cntnr contains at most one atom, 0 is returned.

Parameters

cntnr	The container with the atoms for which to calculate the geometrical diameter.
coords_func	A function that provides the 3D coordinates of an atom.

Returns: The calculated geometrical diameter.

◆ calcGeometricalDiameter() [2/2]

float CDPL.Descr.calcGeometricalDiameter ( Chem.Entity3DContainer cntnr )

Calculates the geometrical diameter of the entities in cntnr.

The geometrical diameter is the maximum distance between any pair of entities in the container. If cntnr contains at most one entity, 0 is returned.

Parameters

cntnr The container with the 3D entities for which to calculate the geometrical diameter.

Returns: The calculated geometrical diameter.

◆ calcGeometricalRadius() [1/2]

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

Calculates the geometrical radius of the atoms in cntnr.

The geometrical radius is the minimum, taken over all atoms, of the maximum distance from a given atom to any other atom in the container. If cntnr contains at most one atom, 0 is returned.

Parameters

cntnr	The container with the atoms for which to calculate the geometrical radius.
coords_func	A function that provides the 3D coordinates of an atom.

Returns: The calculated geometrical radius.

◆ calcGeometricalRadius() [2/2]

float CDPL.Descr.calcGeometricalRadius ( Chem.Entity3DContainer cntnr )

Calculates the geometrical radius of the entities in cntnr.

The geometrical radius is the minimum, taken over all entities, of the maximum distance from a given entity to any other entity in the container. If cntnr contains at most one entity, 0 is returned.

Parameters

cntnr The container with the 3D entities for which to calculate the geometrical radius.

Returns: The calculated geometrical radius.

◆ 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.

◆ calcKierShape1()

float CDPL.Descr.calcKierShape1 ( Chem.MolecularGraph molgraph )

Calculates the first Kier shape index ( \( \kappa_1 \)) of molgraph.

Parameters

molgraph The molecular graph.

Returns: The calculated Kier shape index \( \kappa_1 \).

◆ calcKierShape2()

float CDPL.Descr.calcKierShape2 ( Chem.MolecularGraph molgraph )

Calculates the second Kier shape index ( \( \kappa_2 \)) of molgraph.

Parameters

molgraph The molecular graph.

Returns: The calculated Kier shape index \( \kappa_2 \).

◆ calcKierShape3()

float CDPL.Descr.calcKierShape3 ( Chem.MolecularGraph molgraph )

Calculates the third Kier shape index ( \( \kappa_3 \)) of molgraph.

Parameters

molgraph The molecular graph.

Returns: The calculated Kier shape index \( \kappa_3 \).

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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:

\[ S_{ab} = 1 - \frac{N_a + N_b}{N_a + N_b + N_{ab} + N_{!ab}} \]

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.

◆ calcMolecularComplexity()

float CDPL.Descr.calcMolecularComplexity ( Chem.MolecularGraph molgraph )

Calculates the molecular complexity of molgraph (Hendrickson, Huang and Toczko).

Parameters

molgraph The molecular graph.

Returns: The calculated molecular complexity.

See also: [MCPLX]

◆ calcRandicIndex()

float CDPL.Descr.calcRandicIndex ( Chem.MolecularGraph molgraph )

Calculates the Randic connectivity index of molgraph.

Parameters

molgraph The molecular graph.

Returns: The Randic index.

◆ calcRingComplexity()

float CDPL.Descr.calcRingComplexity ( Chem.MolecularGraph molgraph )

Calculates the ring complexity of molgraph.

Parameters

molgraph The molecular graph.

Returns: The calculated ring complexity.

◆ 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.

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ 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.

Since: 1.2.3

◆ calcTopologicalDiameter()

int CDPL.Descr.calcTopologicalDiameter ( Chem.MolecularGraph molgraph )

Calculates the topological diameter (maximum atom-pair topological distance) of molgraph.

Parameters

molgraph The molecular graph.

Returns: The topological diameter.

◆ calcTopologicalRadius()

int CDPL.Descr.calcTopologicalRadius ( Chem.MolecularGraph molgraph )

Calculates the topological radius (minimum atom eccentricity) of molgraph.

Parameters

molgraph The molecular graph.

Returns: The topological radius.

◆ calcTotalWalkCount()

int CDPL.Descr.calcTotalWalkCount ( Chem.MolecularGraph molgraph )

Calculates the total walk count of molgraph.

Parameters

molgraph The molecular graph.

Returns: The total walk count.

◆ 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:

\[ S_{ab} = \frac{N_{ab}}{\alpha \: N_a + \beta \: N_b + N_{ab}} \]

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. \( \alpha \) and \( \beta \) are bitset contribution weighting factors.

The Tversky measure is asymmetric. Setting the parameters \( \alpha = \beta = 1.0 \) 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.

◆ calcWienerIndex()

int CDPL.Descr.calcWienerIndex ( Chem.MolecularGraph molgraph )

Calculates the Wiener index of molgraph.

The Wiener index is the sum of the topological distances over all pairs of heavy atoms.

Parameters

molgraph The molecular graph.

Returns: The Wiener index.

◆ calcZagrebIndex1()

int CDPL.Descr.calcZagrebIndex1 ( Chem.MolecularGraph molgraph )

Calculates the first Zagreb index ( \( M_1 \)) of molgraph.

\( M_1 \) is the sum of squared heavy atom degrees over all heavy atoms.

Parameters

molgraph The molecular graph.

Returns: The first Zagreb index.

◆ calcZagrebIndex2()

int CDPL.Descr.calcZagrebIndex2 ( Chem.MolecularGraph molgraph )

Calculates the second Zagreb index ( \( M_2 \)) of molgraph.

\( M_2 \) is the sum of the products of the heavy atom degrees over all heavy bonds.

Parameters

molgraph The molecular graph.

Returns: The second Zagreb index.

Classes

Functions

Detailed Description

Function Documentation

◆ calcCosineSimilarity() [1/5]

◆ calcCosineSimilarity() [2/5]

◆ calcCosineSimilarity() [3/5]

◆ calcCosineSimilarity() [4/5]

◆ calcCosineSimilarity() [5/5]

◆ calcDiceSimilarity()

◆ calcEuclideanDistance() [1/5]

◆ calcEuclideanDistance() [2/5]

◆ calcEuclideanDistance() [3/5]

◆ calcEuclideanDistance() [4/5]

◆ calcEuclideanDistance() [5/5]

◆ calcEuclideanSimilarity()

◆ calcGeometricalDiameter() [1/2]

◆ calcGeometricalDiameter() [2/2]

◆ calcGeometricalRadius() [1/2]

◆ calcGeometricalRadius() [2/2]

◆ calcHammingDistance()

◆ calcKierShape1()

◆ calcKierShape2()

◆ calcKierShape3()

◆ calcManhattanDistance() [1/4]

◆ calcManhattanDistance() [2/4]

◆ calcManhattanDistance() [3/4]

◆ calcManhattanDistance() [4/4]

◆ calcManhattanSimilarity()

◆ calcMolecularComplexity()

◆ calcRandicIndex()

◆ calcRingComplexity()

◆ calcTanimotoSimilarity() [1/5]

◆ calcTanimotoSimilarity() [2/5]

◆ calcTanimotoSimilarity() [3/5]

◆ calcTanimotoSimilarity() [4/5]

◆ calcTanimotoSimilarity() [5/5]

◆ calcTopologicalDiameter()

◆ calcTopologicalRadius()

◆ calcTotalWalkCount()

◆ calcTverskySimilarity()

◆ calcWienerIndex()

◆ calcZagrebIndex1()

◆ calcZagrebIndex2()