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Expand Up @@ -25,6 +25,11 @@ pattern matching (SMARTS).
<img width="800" height="400" src="images/globalchemlogo.png">
</p>

Docs
====

Link to Documentation: [![Documentation](https://img.shields.io/badge/GitBook-Docu-lightblue)](https://sulstice.gitbook.io/globalchem-your-chemical-graph-network/)

Installation
============

Expand All @@ -46,8 +51,6 @@ pip install global-chem-extensions
```



Rules
=====

Expand All @@ -68,244 +71,6 @@ Quick Start
Just with no dependencies, intialize the class and there you go! All the common and rare groups of the world
at your disposal

#### Print the GlobalChem Structure

```python

gc = GlobalChem()
gc.print_globalchem_network()

>>>

┌solvents─common_organic_solvents
┌organic_synthesis─└protecting_groups─amino_acid_protecting_groups
│ ┌polymers─common_monomer_repeating_units
├materials─└clay─montmorillonite_adsorption
│ ┌privileged_kinase_inhibtors
│ ├privileged_scaffolds
├proteins─kinases─┌scaffolds─├iupac_blue_book_substituents
│ │ └common_r_group_replacements
│ └braf─inhibitors
│ ┌vitamins
│ ├open_smiles
├miscellaneous─├amino_acids
│ └regex_patterns
global_chem──├environment─emerging_perfluoroalkyls
│ ┌schedule_one
│ ├schedule_four
│ ├schedule_five
├narcotics─├pihkal
│ ├schedule_two
│ └schedule_three
├interstellar_space
│ ┌cannabinoids
│ │ ┌electrophillic_warheads_for_kinases
│ ├warheads─└common_warheads_covalent_inhibitors
└medicinal_chemistry─│ ┌phase_2_hetereocyclic_rings
└rings─├iupac_blue_book_rings
└rings_in_drugs

```
#### To Access Nodes and Visualize the Internal Network:

```python

from global_chem import GlobalChem

gc = GlobalChem()

nodes_list = gc.check_available_nodes()
print (nodes_list)

>>>
'emerging_perfluoro_alkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'electrophilic_warheads_for_kinases',

gc.build_global_chem_network(print_output=True)

>>>
'global_chem': {
'children': [
'environment',
'miscellaneous',
'organic_synthesis',
'medicinal_chemistry',
'narcotics',
'interstellar_space',
'proteins',
'materials'
],
'name': 'global_chem',
'node_value': <global_chem.global_chem.Node object at 0x10f60eed0>,
'parents': []
},
```

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 childrens connected in this fashion.

#### Fetch the Node:

```python

gc = GlobalChem()
gc.build_global_chem_network(print_output=False, debugger=False)
node = gc.get_node('emerging_perfluoroalkyls').get_smiles()
print (node)

```

#### Fetch the IUPAC:SMILES/SMARTS Data from the Node:

```python

gc = GlobalChem()
gc.build_global_chem_network(print_output=True, debugger=False)
smiles = gc.get_node_smiles('emerging_perfluoroalkyls')
smarts = gc.get_node_smarts('emerging_perfluoroalkyls')

print (smiles)
```

#### Fetch All Data from Network:

```python

gc = GlobalChem()
print(gc.get_all_smiles())
print(gc.get_all_smarts())
print(gc.get_all_names())

>>>
['C(=O)(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)O', 'C(=O)(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)O' etc...]

```

#### Remove a Node from the Network:

Removes the Node and it's connections to any parents.

```python

gc = GlobalChem()
gc.build_global_chem_network(print_output=False, debugger=False)
gc.remove_node('emerging_perfluoroalkyls')

```

#### Fetch a SMILES By IUPAC

```python

gc = GlobalChem()
definition = gc.get_smiles_by_iupac(
'benzene',
return_network_path=False,
return_all_network_paths=False
)

```
#### Set & Get the Node Value:

If the user wants to put some metadata inside the node they can:

```python

gc = GlobalChem()
gc.build_global_chem_network(print_output=True, debugger=False)
gc.set_node_value('emerging_perfluoroalkyls', {'some_data': ['bunny']})
print (gc.get_node_value('emerging_perfluoroalkyls'))

>>>
{'some_data': ['bunny']}
```

#### To Create Your Own Chemical Graph Network (GN) And Check the Values

```python

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)

>>>
'3′-bromo-2-chloro[1,1′:4′,1′′-terphenyl]-4,4′′': 'ClC1=CC=CC=C1C2=CC=C(C3=CC=CC=C3)C(Br)=C2'

values = gc.get_node_smarts('electrophilic_warheads_for_kinases')

>>>
'propane-1,3-diyl': '[#6]-[#6]-[#6]', 'methylmethylene': '[#6H]-[#6]',

```

#### Creating Deep Layer Chemical Graph Networks (DGN) & Print it out:

This is for more advanced users of graph theory and understanding.

```python

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

gc.print_deep_network()


>>>
┌common_warhead_covalent_inhibitors
┌emerging_perfluoroalkyls─├privileged_scaffolds
│ └iupac_blue_book
│ ┌common_warhead_covalent_inhibitors
global_chem─├montmorillonite_adsorption─├privileged_scaffolds
│ └iupac_blue_book
│ ┌common_warhead_covalent_inhibitors
└common_monomer_repeating_units─├privileged_scaffolds
└iupac_blue_book

```

#### Compute Common Score for an IUPAC Name:

Based on how many times a word is mentioned per object increases the common weight. The more weight the more common.
A score of 0 indicates it is "uncommon".

```
Common Score Algorithm:
1.) Data mine the current state of GlobalChem
2.) Get the Object Weights of Each mention
3.) Determine the Mention Weight
4.) Sum the Weights and That's How common it is.
```

```python

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

```

Adding Your Own Chemical List
=============================

Expand All @@ -324,58 +89,6 @@ smiles = {
```

GlobalChem Extensions
=====================

Applications of `GlobalChem` can be applied to a variety of cheminformatic usage. One of which is functional group analysos of
any SMILES dataset using the SMARTS patterns strings described in the data. GlobalChemExtensions have


#### Sunbursting

Please navigate here for more documentation: https://github.com/Sulstice/global-chem-extensions
```python

from global_chem_extensions.global_chem_extensions import GlobalChemExtensions

test_set = [
'c1[n+](cc2n(c1OCCc1cc(c(cc1)F)F)c(nn2)c1ccc(cc1)OC(F)F)[O-]',
'c1nc(c2n(c1OCCc1cc(c(cc1)F)F)c(nn2)c1ccc(cc1)OC(F)F)Cl',
'c1ncc2n(c1CCO)c(nn2)c1ccc(cc1)OC(F)F',
'C1NCc2n(C1CCO)c(nn2)c1ccc(cc1)OC(F)F',
'C1(CN(C1)c1cc(c(cc1)F)F)Oc1cncc2n1c(nn2)c1ccc(cc1)OC(F)F',
'c1ncc2n(c1N1CCC(C1)c1ccccc1)c(nn2)c1ccc(cc1)OC(F)F',
]

GlobalChemExtensions().sunburst_chemical_list(test_set, save_file=False)

```

<p align="center">
<img width="550" height="500" src="images/extensions/figure_1.gif">
</p>

#### PCA Analysis

Conduct PCA Analysis with a SMILES list input.

```python

from global_chem.global_chem import GlobalChem
from global_chem_extensions.global_chem_extensions import GlobalChemExtensions

gc = GlobalChem()
gc.build_global_chem_network(print_output=False, debugger=False)
smiles_list = list(gc.get_node_smiles('schedule_one').values())

GlobalChemExtensions().node_pca_analysis(smiles_list, save_file=False)

```

<p align="center">
<img width="500" height="450" src="images/extensions/pca_analysis.gif">
</p>

Nodes List
==============

Expand Down Expand Up @@ -404,6 +117,7 @@ Nodes List
| Schedule 3 United States Narcotics | 22 | ECFR :: 21 CFR Part 1308 - Schedules. |
| Schedule 4 United States Narcotics | 77 | ECFR :: 21 CFR Part 1308 - Schedules. |
| Schedule 5 United States Narcotics | 8 | ECFR :: 21 CFR Part 1308 - Schedules. |
| Pihkal | 179 | Shulgin, Alexander T., and Ann Shulgin. Pihkal: A Chemical Love Story. 1. ed., 8. print, Transform, 2010. |
| Common Regex Patterns | 1 | |


Expand Down Expand Up @@ -433,6 +147,5 @@ Citation
It's on it's way



## License
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