GlobalChem

First initialize the class must be initialized:

gc = GlobalChem()

Print the Network

Code

gc.print_globalchem_network()

Output

Check the available nodes in GlobalChem:

Code

nodes_list = gc.check_available_nodes()
print (nodes_list)

Output

Retrieve all the Nodes

Code

nodes_list = gc.get_all_nodes()

Get the Tree Depth of GlobalChem

Code

depth = gc.get_depth_of_globalchem()

Get GlobalChem All Nodes SMILES

Code

depth = gc.get_all_smiles()

Get GlobalChem All Nodes SMARTS

Code

depth = gc.get_all_smarts()

Get GlobalChem All Nodes Names

Code

depth = gc.get_all_names()

Get SMILES Definition by IUPAC Name

This function fetches the distance between two words using the Levenshtein distance with a distance tolerance number. It removes both grammar and upper case letters automatically and tries to match the best fitting word against the query and return their dedicated paths. Users have the option to return the exact definition or partial definitions.

Code

definition = gc.get_smiles_by_iupac(
    'benzene',   
    distance_tolerance=7,
    return_partial_definitions=True                       
)
print (definition)

Output

You have the option to do a fuzzy reconstruction of the SMILES from the IUPAC used stripped grammar and functional groups:

Code

definition = gc.get_smiles_by_iupac(
        '(4R,4aR,7S,7aR,12bS)-3-methyl-2,4,4a,7,7a,13-hexahydro-1H-4,12-methanobenzofuro[3,2-e]isoquinoline-7,9-diol',
        distance_tolerance=2,
        return_partial_definitions=False,
        reconstruct_smiles=True,
)

print (definition)

Output

Build the GlobalChem Network and Print it Out

Code

gc.build_global_chem_network(
    print_output=True,          # Print the network out
    debugger=False,             # For Developers mostly to see all node values
)

Output

The algorithm uses a series of parents/children to connect nodes instead of "edges" as in traditional graph networks. This just makes it easier to code if the graph database lives as a 1-dimensional with lists of parents and children's connected in this fashion.

Fetch a Node

Code

gc = GlobalChem()
gc.build_global_chem_network()
node = gc.get_node('emerging_perfluoroalkyls')
print (node)

Output

Fetch the IUPAC:SMILES/SMARTS Data from the Node

Code

gc = GlobalChem()
gc.build_global_chem_network()
smiles = gc.get_node_smiles('emerging_perfluoroalkyls')
smarts = gc.get_node_smarts('emerging_perfluoroalkyls')

print ("Length of Perfluoroalkyls: %s " % len(smiles))

Code

from global_chem import GlobalChem

gc = GlobalChem(verbose=False)
gc.initiate_network()
gc.add_node('global_chem', 'common_monomer_repeating_units')
gc.add_node('common_monomer_repeating_units','electrophilic_warheads_for_kinases')
values = gc.get_node_smiles('common_monomer_repeating_units')

print (values)

Output

Creating a Deep Layer Chemical Graph Networks (DGN) & Print it Out:

This is for the more advanced users of building networks and how to manage sets of layers.

Code

# Create a Deep Layer Network

gc = GlobalChem()
gc.initiate_deep_layer_network()
gc.add_deep_layer(
    [
        'emerging_perfluoroalkyls',
        'montmorillonite_adsorption',
        'common_monomer_repeating_units'
    ]
)
gc.add_deep_layer(
    [
        'common_warheads_covalent_inhibitors',
        'privileged_scaffolds',
        'iupac_blue_book'
    ]
)

gc.print_deep_network()

Output

Compute a Common Score

Common Score Algorithm:

1. Datamine the current graph network of GlobalChem

2. Get the object weights of each mention

3. Determine the mention weight

4. Sum the Weight's and that is how common the molecule is.

The higher the value the higher the common score tied with it's IUPAC name.

Code

gc = GlobalChem()
gc.build_global_chem_network(print_output=False, debugger=False)
gc.compute_common_score('benzene', verbose=True)

Output

To TSV

The network returned in all CSV format for interoperability for web application development mostly but can also be used to search.

Code

gc = GlobalChem()
gc.to_tsv('global_chem.tsv')