calcDrugMCSSim {Rcpi} | R Documentation

## Calculate Drug Molecule Similarity Derived by Maximum Common Substructure Search

### Description

Calculate Drug Molecule Similarity Derived by Maximum Common Substructure Search

### Usage

    calcDrugMCSSim(mol1, mol2, type = c("smile", "sdf"), plot = FALSE,
      al = 0, au = 0, bl = 0, bu = 0, matching.mode = "static", ...)

### Arguments

`mol1` | The first molecule. R character string object containing the molecule. See examples.
`mol2` | The second molecule. R character string object containing the molecule. See examples.
`type` | The input molecule format, 'smile' or 'sdf'.
`plot` | Logical. Should we plot the two molecules and their maximum common substructure?
`al` | Lower bound for the number of atom mismatches. Default is 0.
`au` | Upper bound for the number of atom mismatches. Default is 0.
`bl` | Lower bound for the number of bond mismatches. Default is 0.
`bu` | Upper bound for the number of bond mismatches. Default is 0.
`matching.mode` | Three modes for bond matching are supported: `'static'`, `'aromatic'`, and `'ring'`.
`...` | Other graphical parameters

### Details

This function calculate drug molecule similarity derived by maximum common substructure search. The maximum common substructure search algorithm is provided by the `fmcsR` package.

### Value

A list containing the detail MCS information and similarity values. The numeric similarity value includes Tanimoto coefficient and overlap coefficient.

### Author(s)

Nan Xiao <https://nanx.me>

### References

Wang, Y., Backman, T. W., Horan, K., & Girke, T. (2013). fmcsR: mismatch tolerant maximum common substructure searching in R. Bioinformatics, 29(21), 2792–2794.

### Examples

    mol1 = 'CC(C)CCCCCC(=O)NCC1=CC(=C(C=C1)O)OC'
    mol2 = 'O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C'
    mol3 = readChar(system.file('compseq/DB00859.sdf', package = 'Rcpi'), nchars = 1e+6)
    mol4 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'), nchars = 1e+6)

    sim1 = calcDrugMCSSim(mol1, mol2, type = 'smile')
    sim2 = calcDrugMCSSim(mol3, mol4, type = 'sdf', plot = TRUE)
    print(sim1[[2]])  # Tanimoto Coefficient
    print(sim2[[3]])  # Overlap Coefficient

[Package Rcpi version 1.30.0 Index]