Wang¶
- autoFragment(inchi, radius, dGPredictorPath, canonicalise, cacheName, printLevel)[source]¶
Automatically fragment one or more InChI into a set of SMILES substructures, each centred on an atom, with a radius specifying the number of bonds to neighbouring atoms
- USAGE:
[fragmentedMol, decomposableBool, inchiExistBool] = autoFragment (inchi, radius, dGPredictorPath, canonicalise, cacheName, printLevel)
- INPUT:
inchi – n x 1 cell array of molecules, each an InChI string, or a single InChI string as a char
- OPTIONAL INPUTS:
radius – number of bonds around each central SMILES atom (default 1)
dGPredictorPath – full absolute path (without ~/) to a git clone of dGPredictor
canonicalise – boolean, consolidate duplicate canonical SMILES fragments (default 0)
cacheName – filename of a cache to load (if it exists) or save to (if not)
printLevel – verbosity level (default 0)
- OUTPUTS:
fragmentedMol – n x 1 structure with, for each InChI, the fields:
.inchi - the InChI string
.smilesCounts - containers.Map from each SMILES substructure to its incidence in the molecule
decomposableBool – n x 1 logical vector, true where the InChI is decomposable
inchiExistBool – n x 1 logical vector, true where an InChI is present
Note
External dependencies: Python (see initPythonEnvironment), rdkit (https://www.rdkit.org) and dGPredictor (https://github.com/maranasgroup/dGPredictor). Reference: Wang L, Upadhyay V, Maranas CD (2021) dGPredictor. PLoS Comput Biol 17(9): e1009448. https://doi.org/10.1371/journal.pcbi.1009448
- createFragmentIncidenceMatrix(inchi, radius, dGPredictorPath, canonicalise)[source]¶
Build a fragment incidence matrix by automatically fragmenting each InChI and mapping metabolites to the consolidated set of SMILES fragments
- USAGE:
model = createFragmentIncidenceMatrix (inchi, radius, dGPredictorPath, canonicalise)
- INPUT:
inchi – k x 1 cell array of InChI strings (one per metabolite)
- OPTIONAL INPUTS:
radius – number of bonds around each central SMILES atom (default 1)
dGPredictorPath – full absolute path (without ~/) to a git clone of dGPredictor
canonicalise – boolean, consolidate duplicate canonical SMILES fragments
- OUTPUT:
model – structure with fields:
.inchi - the input k x 1 cell array of InChI strings
.frag - g x 1 cell array of unique SMILES fragments
.G - k x g fragment incidence matrix