R/702-calcDrugMCSSim.R
calcDrugMCSSim.Rd
Calculate Drug Molecule Similarity Derived by Maximum Common Substructure Search
calcDrugMCSSim(mol1, mol2, type = c("smile", "sdf"), plot = FALSE, al = 0, au = 0, bl = 0, bu = 0, matching.mode = "static", ...)
mol1 | The first molecule. R character string object containing the molecule. See examples. |
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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:
|
... | Other graphical parameters |
A list containing the detail MCS information and similarity values. The numeric similarity value includes Tanimoto coefficient and overlap coefficient.
This function calculate drug molecule similarity derived by
maximum common substructure search. The maximum common substructure
search algorithm is provided by the fmcsR
package.
Wang, Y., Backman, T. W., Horan, K., & Girke, T. (2013). fmcsR: mismatch tolerant maximum common substructure searching in R. Bioinformatics, 29(21), 2792--2794.
# NOT RUN { 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) # }# NOT RUN { sim1 = calcDrugMCSSim(mol1, mol2, type = 'smile') sim2 = calcDrugMCSSim(mol3, mol4, type = 'sdf', plot = TRUE) print(sim1[[2]]) # Tanimoto Coefficient print(sim2[[3]]) # Overlap Coefficient # }