calcDrugFPSim: Calculate Drug Molecule Similarity Derived by Molecular...

In wind22zhu/BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs, RNAs and Their Interactions

Description

Calculate Drug Molecule Similarity Derived by Molecular Fingerprints

Usage

calcDrugFPSim(fp1, fp2, fptype = c("compact", "complete"),
  metric = c("tanimoto", "euclidean", "cosine", "dice", "hamming"))

Arguments

fp1	The first molecule's fingerprints, could be extracted by extractDrugMACCS(), extractDrugMACCSComplete() etc.
fp2	The second molecule's fingerprints.
fptype	The fingerprint type, must be one of "compact" or "complete".
metric	The similarity metric, one of "tanimoto", "euclidean", "cosine", "dice" and "hamming".

Details

This function calculate drug molecule fingerprints similarity. Define a as the features of object A, b is the features of object B, c is the number of common features to A and B:

• Tanimoto: aka Jaccard - c/a+b+c

• Euclidean: √(a + b)

• Dice: aka Sorensen, Czekanowski, Hodgkin-Richards - c/0.5[(a+c) + (b+c)]

• Cosine: aka Ochiai, Carbo - c/√((a + c)(b + c))

• Hamming: aka Manhattan, taxi-cab, city-block distance - (a + b)

Value

The numeric similarity value.

Author(s)

Min-feng Zhu <[e-mail]>, Nan Xiao <http://r2s.name>

References

Gasteiger, Johann, and Thomas Engel, eds. Chemoinformatics. Wiley.com, 2006.

Examples

mols = readMolFromSDF(system.file('compseq/tyrphostin.sdf', package = 'BioMedR'))

fp1 = extrDrugEstate(mols[[1]])
fp2 = extrDrugEstate(mols[[2]])
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'tanimoto')

wind22zhu/BioMedR documentation built on Oct. 21, 2019, 12:51 p.m.