yet another ChemClassifier (Python based on wikibase-cli and rdkit)
This is a proof of the concept of a simple compound classifier that relies completely on knowledge from Wikidata. At this stage it uses InChI keys, SMILES and SMARTS strings, downloaded from Wikidata, to get hits on compounds or classes. About 650 biosynthetic processes from Gene Ontology are directly associated with classes and compounds, so they are potential hits. Matching is done by going through the list of classes---fast enough to find all hits in the dataset with >110k classes within a few seconds.
While yaccl is, in principle, a general classifier, development of patterns focuses on biomolecules.
The current version is 2122.
- terpenoids
- steroids
- flavonoids
- alkaloids (25% unspecified but recognized)
- polyketides
- fatty acyls (in progress)
- Python 3
- rdkit python package
- wikibase-cli (optional, for dataset updates)
Cloning the repo will get you all datasets, so no need to download from Wikidata if you just want to play with yaccl.
Clone the repo.
Change into the repo src directory. There are four scripts. One is for classification, and three are for updating / statistics of datasets.
Takes a SMILES/InChI and outputs a classification. Examples:
$ python3 classify.py -d ./ -m 'CCO'
reading biosyn data
reading class pattern data
reading superclass data
collecting hits...
purging redundant hits
('Q153', 'ethanol', 'LFQSCWFLJHTTHZ-UHFFFAOYSA-N') {('GO:0044576', 'pentose catabolic process to ethanol'), ('GO:0044577', 'xylose catabolic process to ethanol'), 'GO:0006115', 'ethanol biosynthetic process', ('GO:0019431', 'acetyl-CoA biosynthetic process from ethanol')}
('Q2832210', 'primary alcohol', '[OX2H1][CX4H2]') {'primary alcohol biosynthetic process', 'GO:0034309'}
$ python3 classify.py -d ./ -m 'InChI=1S/C15H22O10/c16-3-6-9(19)10(20)11(21)14(23-6)24-13-7-5(1-2-22-13)8(18)12-15(7,4-17)25-12/h1-2,5-14,16-21H,3-4H2/t5-,6-,7-,8+,9-,10+,11-,12+,13+,14+,15-/m1/s1'
reading biosyn data
reading class pattern data
reading superclass data
collecting hits...
purging redundant hits
('Q105151716', '2-[[5-hydroxy-2-(hydroxymethyl)-3,9-dioxatricyclo[4.4.0.02,4]dec-7-en-10-yl]oxy]-6-(hydroxymethyl)oxane-3,4,5-triol', 'OCC1OC(OC2OC=CC3C(O)C4OC4(CO)C23)C(O)C(O)C1O') {'GO:0016138', 'glycoside biosynthetic process'}
('Q408028', 'epoxide', 'C1OC1') {'GO:1901503', 'ether biosynthetic process'}
('Q2832210', 'primary alcohol', '[OX2H1][CX4H2]') {'primary alcohol biosynthetic process', 'GO:0034309'}
('Q2832211', 'secondary alcohol', '[CX4H1][OX2H1]') {'GO:1902653', 'secondary alcohol biosynthetic process'}
('Q74173050', 'D-glucoside', '[C@@H]1(OC([C@H](O)[C@H]([C@@H]1O)O)O*)CO') {'GO:0016138', 'glycoside biosynthetic process'}
('Q74568405', 'alpha-D-mannoside', 'OC[C@@H]1[C@H]([C@@H]([C@H](O)[C@H](O1)O*)O)O') {'GO:0016138', 'glycoside biosynthetic process'}
('Q75078202', 'pyranoside', 'O1C(C(C(C(C1O*)O)O)O)CO') {'GO:0016138', 'glycoside biosynthetic process'}
('Q75082510', 'beta-D-galactoside', 'OC[C@H]1O[C@@H](O[*])[C@H](O)[C@@H](O)[C@H]1O') {'GO:0016138', 'glycoside biosynthetic process'}
('Q105151716', '2-[[5-hydroxy-2-(hydroxymethyl)-3,9-dioxatricyclo[4.4.0.02,4]dec-7-en-10-yl]oxy]-6-(hydroxymethyl)oxane-3,4,5-triol', 'OCC1OC(OC2OC=CC3C(O)C4OC4(CO)C23)C(O)C(O)C1O') {'GO:0016138', 'glycoside biosynthetic process'}
The -j option provides JSON format. This does only work if a natural product is recognized. Then, it gives the full path to root in the ontology:
$ python3 classify.py -d ./ -m 'InChI=1S/C28H32O15/c1-39-14-7-15-18(12(32)6-13(40-15)10-2-4-11(31)5-3-10)22(35)19(14)26-27(24(37)21(34)16(8-29)41-26)43-28-25(38)23(36)20(33)17(9-30)42-28/h2-7,16-17,20-21,23-31,33-38H,8-9H2,1H3/t16-,17-,20-,21-,23+,24+,25-,26+,27-,28+/m1/s1' -j
[{"classification_names": ["flavonoid", "2-phenylchromane flavonoid", "flavone", "6C-substituted flavone", "6C-glycosylated flavone", "spinosin"], "biological_process": ["flavone biosynthetic process", "GO:0051553"]}]
Given a file of InChI strings the -t option runs a test checking that at least one of the hits is a subclass of the test class item.
$ python3 classify.py -t test/sesterterpenoids.txt -d ./
reading biosyn data
reading class pattern data
reading superclass data
classifying testfile test/sesterterpenoids.txt
test class: Q107363222
1 OK
2 OK
3 OK
4 OK
5 OK
6 OK
7 OK
8 OK
9 FAIL: InChI=1S/C25H44/c1-9-21(6)13-11-17-25(8,16-10-12-19(2)3)24-18-22(7)14-15-23(24)20(4)5/h9,18-19,21,23-24H,1,4,10-17H2,2-3,5-8H3
...
The difference between the biosyn and the class-pattern datasets is that the former contains classes AND compounds that have associated GO processes in Wikidata (possibly lacking any pattern), while the latter contains all classes (no compounds) that have associated patterns (SMILES, SMARTS or InChI).
The design of datasets will be subject to change in future versions.
Using the -q option with the dataset scripts depends on a working wikibase-cli installation
to attempt an update of the respective database, directly from Wikidata. This is not necessary
if you frequently git pull this repo, as any Wikidata changes will be reflected by repo updates.
Output stats of biosyn-classes dataset.
$ python3 biosyn-class-stats.py
#items: 1167
#InChI keys: 635
#InChI1 keys: 242
#smarts: 42
#smiles: 694
# w/o pattern: 436
Output stats of class-pattern dataset.
$ python3 class-pattern.py
#items: 113788
#InChI keys: 112432
#InChI1 keys: 81120
#smarts: 374
#smiles: 1024
#wildcard smiles: 979
Yes, Wikidata has >110k compound classes, most of which (>81k) are compounds with unspecified stereochemistry, i.e. they are groups of stereoisomers.
Most errors or inconsistencies are due to data, so please contribute to Wikidata. More involved and pressing is to add patterns to the items of the biosyn dataset, or even add missing classes to expand Wikidata's class ontology.
If you want to tackle a specific superclass we recommend usage of wdtaxonomy to get an overview of the Wikidata situation.
Of course, any comment or PR is highly appreciated.
- do we use compound SMILES from biosyn items (e.g. Q27104306)?
- check if there are biosyn classes not in the NP sub-ontology
- optimize ontology in WD, remove or deprecate redundant P31/P279 stmts
- write toolserver client
- use RHEA to list enzymatic reactions
- handle iridoid + glycoside --> iridoid glycoside (probably just write a bot for giv cases)