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Calculate the Eccentric Connectivity Index Descriptor


extractDrugECI(molecules, silent = TRUE)



Parsed molucule object.


Logical. Whether the calculating process should be shown or not, default is TRUE.


A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named ECCEN.


Eccentric Connectivity Index (ECI) is a topological descriptor combining distance and adjacency information. This descriptor is described by Sharma et al. and has been shown to correlate well with a number of physical properties. The descriptor is also reported to have good discriminatory ability. The eccentric connectivity index for a hydrogen supressed molecular graph is given by $$x_i^c = \sum_{i = 1}^{n} E(i) V(i)$$ where E(i) is the eccentricity of the i-th atom (path length from the i-th atom to the atom farthest from it) and V(i) is the vertex degree of the i-th atom.


Sharma, V. and Goswami, R. and Madan, A.K. (1997), Eccentric Connectivity Index: A Novel Highly Discriminating Topological Descriptor for Structure-Property and Structure-Activity Studies, Journal of Chemical Information and Computer Sciences, 37:273-282


smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')
# \donttest{
mol = readMolFromSmi(smi, type = 'mol')
#> Error in parseSmiles(smi): The package "rcdk" is required to parse SMILES
dat = extractDrugECI(mol)
#> Error in evaluateDescriptor(molecules, type = "EccentricConnectivityIndexDescriptor",     silent = silent): The package "rcdk" is required to compute molecular descriptors
head(dat)# }
#> Error in eval(expr, envir, enclos): object 'dat' not found