@@ -25,6 +25,11 @@ pattern matching (SMARTS).
2525 <img width =" 800 " height =" 400 " src =" images/globalchemlogo.png " >
2626</p >
2727
28+ Docs
29+ ====
30+
31+ Link to Documentation: [ ![ Documentation] ( https://img.shields.io/badge/GitBook-Docu-lightblue )] ( https://sulstice.gitbook.io/globalchem-your-chemical-graph-network/ )
32+
2833Installation
2934============
3035
@@ -46,8 +51,6 @@ pip install global-chem-extensions
4651
4752```
4853
49-
50-
5154Rules
5255=====
5356
@@ -68,244 +71,6 @@ Quick Start
6871Just with no dependencies, intialize the class and there you go! All the common and rare groups of the world
6972at your disposal
7073
71- #### Print the GlobalChem Structure
72-
73- ``` python
74-
75- gc = GlobalChem()
76- gc.print_globalchem_network()
77-
78- >> >
79-
80- ┌solvents─common_organic_solvents
81- ┌organic_synthesis─└protecting_groups─amino_acid_protecting_groups
82- │ ┌polymers─common_monomer_repeating_units
83- ├materials─└clay─montmorillonite_adsorption
84- │ ┌privileged_kinase_inhibtors
85- │ ├privileged_scaffolds
86- ├proteins─kinases─┌scaffolds─├iupac_blue_book_substituents
87- │ │ └common_r_group_replacements
88- │ └braf─inhibitors
89- │ ┌vitamins
90- │ ├open_smiles
91- ├miscellaneous─├amino_acids
92- │ └regex_patterns
93- global_chem──├environment─emerging_perfluoroalkyls
94- │ ┌schedule_one
95- │ ├schedule_four
96- │ ├schedule_five
97- ├narcotics─├pihkal
98- │ ├schedule_two
99- │ └schedule_three
100- ├interstellar_space
101- │ ┌cannabinoids
102- │ │ ┌electrophillic_warheads_for_kinases
103- │ ├warheads─└common_warheads_covalent_inhibitors
104- └medicinal_chemistry─│ ┌phase_2_hetereocyclic_rings
105- └rings─├iupac_blue_book_rings
106- └rings_in_drugs
107-
108- ```
109- #### To Access Nodes and Visualize the Internal Network:
110-
111- ``` python
112-
113- from global_chem import GlobalChem
114-
115- gc = GlobalChem()
116-
117- nodes_list = gc.check_available_nodes()
118- print (nodes_list)
119-
120- >> >
121- ' emerging_perfluoro_alkyls' ' montmorillonite_adsorption' ' common_monomer_repeating_units' ' electrophilic_warheads_for_kinases'
122-
123- gc.build_global_chem_network(print_output = True )
124-
125- >> >
126- ' global_chem'
127- ' children'
128- ' environment'
129- ' miscellaneous'
130- ' organic_synthesis'
131- ' medicinal_chemistry'
132- ' narcotics'
133- ' interstellar_space'
134- ' proteins'
135- ' materials'
136- ],
137- ' name' ' global_chem'
138- ' node_value' < global_chem.global_chem.Node object at 0x 10f60eed0> ,
139- ' parents'
140- },
141- ```
142-
143- 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
144- the graph database lives as a 1-dimensional with lists of parents and childrens connected in this fashion.
145-
146- #### Fetch the Node:
147-
148- ``` python
149-
150- gc = GlobalChem()
151- gc.build_global_chem_network(print_output = False , debugger = False )
152- node = gc.get_node(' emerging_perfluoroalkyls'
153- print (node)
154-
155- ```
156-
157- #### Fetch the IUPAC: SMILES /SMARTS Data from the Node:
158-
159- ``` python
160-
161- gc = GlobalChem()
162- gc.build_global_chem_network(print_output = True , debugger = False )
163- smiles = gc.get_node_smiles(' emerging_perfluoroalkyls'
164- smarts = gc.get_node_smarts(' emerging_perfluoroalkyls'
165-
166- print (smiles)
167- ```
168-
169- #### Fetch All Data from Network:
170-
171- ``` python
172-
173- gc = GlobalChem()
174- print (gc.get_all_smiles())
175- print (gc.get_all_smarts())
176- print (gc.get_all_names())
177-
178- >> >
179- [' 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' ... ]
180-
181- ```
182-
183- #### Remove a Node from the Network:
184-
185- Removes the Node and it's connections to any parents.
186-
187- ``` python
188-
189- gc = GlobalChem()
190- gc.build_global_chem_network(print_output = False , debugger = False )
191- gc.remove_node(' emerging_perfluoroalkyls'
192-
193- ```
194-
195- #### Fetch a SMILES By IUPAC
196-
197- ``` python
198-
199- gc = GlobalChem()
200- definition = gc.get_smiles_by_iupac(
201- ' benzene'
202- return_network_path = False ,
203- return_all_network_paths = False
204- )
205-
206- ```
207- #### Set & Get the Node Value:
208-
209- If the user wants to put some metadata inside the node they can:
210-
211- ``` python
212-
213- gc = GlobalChem()
214- gc.build_global_chem_network(print_output = True , debugger = False )
215- gc.set_node_value(' emerging_perfluoroalkyls' ' some_data' ' bunny'
216- print (gc.get_node_value(' emerging_perfluoroalkyls'
217-
218- >> >
219- {' some_data' ' bunny'
220- ```
221-
222- #### To Create Your Own Chemical Graph Network (GN) And Check the Values
223-
224- ``` python
225-
226- from global_chem import GlobalChem
227-
228- gc = GlobalChem(verbose = False )
229- gc.initiate_network()
230- gc.add_node(' global_chem' ' common_monomer_repeating_units'
231- gc.add_node(' common_monomer_repeating_units' ' electrophilic_warheads_for_kinases'
232- values = gc.get_node_smiles(' common_monomer_repeating_units'
233-
234- print (values)
235-
236- >> >
237- ' 3′-bromo-2-chloro[1,1′:4′,1′′-terphenyl]-4,4′′' ' ClC1=CC=CC=C1C2=CC=C(C3=CC=CC=C3)C(Br)=C2'
238-
239- values = gc.get_node_smarts(' electrophilic_warheads_for_kinases'
240-
241- >> >
242- ' propane-1,3-diyl' ' [#6]-[#6]-[#6]' ' methylmethylene' ' [#6H]-[#6]'
243-
244- ```
245-
246- #### Creating Deep Layer Chemical Graph Networks (DGN) & Print it out:
247-
248- This is for more advanced users of graph theory and understanding.
249-
250- ``` python
251-
252- gc = GlobalChem()
253- gc.initiate_deep_layer_network()
254- gc.add_deep_layer(
255- [
256- ' emerging_perfluoroalkyls'
257- ' montmorillonite_adsorption'
258- ' common_monomer_repeating_units'
259- ]
260- )
261- gc.add_deep_layer(
262- [
263- ' common_warhead_covalent_inhibitors'
264- ' privileged_scaffolds'
265- ' iupac_blue_book'
266- ]
267- )
268-
269- gc.print_deep_network()
270-
271-
272- >> >
273- ┌common_warhead_covalent_inhibitors
274- ┌emerging_perfluoroalkyls─├privileged_scaffolds
275- │ └iupac_blue_book
276- │ ┌common_warhead_covalent_inhibitors
277- global_chem─├montmorillonite_adsorption─├privileged_scaffolds
278- │ └iupac_blue_book
279- │ ┌common_warhead_covalent_inhibitors
280- └common_monomer_repeating_units─├privileged_scaffolds
281- └iupac_blue_book
282-
283- ```
284-
285- #### Compute Common Score for an IUPAC Name:
286-
287- Based on how many times a word is mentioned per object increases the common weight. The more weight the more common.
288- A score of 0 indicates it is "uncommon".
289-
290- ```
291-
292- Common Score Algorithm:
293-
294- 1.) Data mine the current state of GlobalChem
295- 2.) Get the Object Weights of Each mention
296- 3.) Determine the Mention Weight
297- 4.) Sum the Weights and That's How common it is.
298-
299- ```
300-
301- ``` python
302-
303- gc = GlobalChem()
304- gc.build_global_chem_network(print_output = False , debugger = False )
305- gc.compute_common_score(' benzene' verbose = True )
306-
307- ```
308-
30974Adding Your Own Chemical List
31075=============================
31176
@@ -324,58 +89,6 @@ smiles = {
32489
32590```
32691
327- GlobalChem Extensions
328- =====================
329-
330- Applications of ` GlobalChem ` can be applied to a variety of cheminformatic usage. One of which is functional group analysos of
331- any SMILES dataset using the SMARTS patterns strings described in the data. GlobalChemExtensions have
332-
333-
334- #### Sunbursting
335-
336- Please navigate here for more documentation: https://github.com/Sulstice/global-chem-extensions
337- ``` python
338-
339- from global_chem_extensions.global_chem_extensions import GlobalChemExtensions
340-
341- test_set = [
342- ' c1[n+](cc2n(c1OCCc1cc(c(cc1)F)F)c(nn2)c1ccc(cc1)OC(F)F)[O-]'
343- ' c1nc(c2n(c1OCCc1cc(c(cc1)F)F)c(nn2)c1ccc(cc1)OC(F)F)Cl'
344- ' c1ncc2n(c1CCO)c(nn2)c1ccc(cc1)OC(F)F'
345- ' C1NCc2n(C1CCO)c(nn2)c1ccc(cc1)OC(F)F'
346- ' C1(CN(C1)c1cc(c(cc1)F)F)Oc1cncc2n1c(nn2)c1ccc(cc1)OC(F)F'
347- ' c1ncc2n(c1N1CCC(C1)c1ccccc1)c(nn2)c1ccc(cc1)OC(F)F'
348- ]
349-
350- GlobalChemExtensions().sunburst_chemical_list(test_set, save_file = False )
351-
352- ```
353-
354- <p align =" center " >
355- <img width =" 550 " height =" 500 " src =" images/extensions/figure_1.gif " >
356- </p >
357-
358- #### PCA Analysis
359-
360- Conduct PCA Analysis with a SMILES list input.
361-
362- ``` python
363-
364- from global_chem.global_chem import GlobalChem
365- from global_chem_extensions.global_chem_extensions import GlobalChemExtensions
366-
367- gc = GlobalChem()
368- gc.build_global_chem_network(print_output = False , debugger = False )
369- smiles_list = list (gc.get_node_smiles(' schedule_one'
370-
371- GlobalChemExtensions().node_pca_analysis(smiles_list, save_file = False )
372-
373- ```
374-
375- <p align =" center " >
376- <img width =" 500 " height =" 450 " src =" images/extensions/pca_analysis.gif " >
377- </p >
378-
37992Nodes List
38093==============
38194
@@ -404,6 +117,7 @@ Nodes List
404117| Schedule 3 United States Narcotics | 22 | ECFR :: 21 CFR Part 1308 - Schedules. |
405118| Schedule 4 United States Narcotics | 77 | ECFR :: 21 CFR Part 1308 - Schedules. |
406119| Schedule 5 United States Narcotics | 8 | ECFR :: 21 CFR Part 1308 - Schedules. |
120+ | Pihkal | 179 | Shulgin, Alexander T., and Ann Shulgin. Pihkal: A Chemical Love Story. 1. ed., 8. print, Transform, 2010. |
407121| Common Regex Patterns | 1 | |
408122
409123
@@ -433,6 +147,5 @@ Citation
433147It's on it's way
434148
435149
436-
437150## License
438151[ ![ FOSSA Status] ( https://app.fossa.com/api/projects/git%2Bgithub.com%2FSulstice%2Fglobal-chem.svg?type=large )] ( https://app.fossa.com/projects/git%2Bgithub.com%2FSulstice%2Fglobal-chem?ref=badge_large )
0 commit comments