access CHEMBL DB with python

CHEMBL is a one of big public database. It has lots of useful data.
If you are good at python, pychembldb will be good tool.

I like the python package.
And I found another package chembl “chembl_web_client”.
The package can install using pip.

iwatobipen$ sudo pip install chembl_web_client

Yah, it easy. OK Let’s get data from python !
For example following code get data about Ty-kinase inh c-Met.
For example, chembl_assay_id is 1003887.

from chembl_webresource_client import *
assay = AssayResource()
comp = CompoundResource()
target = TargetResource()

bio_act = assay.bioactivities("CHEMBL1003887")
print bio_act[0]
{u'activity_comment': u'Unspecified',
 u'assay_chemblid': u'CHEMBL1003887',
 u'assay_description': u'Inhibition of MET',
 u'assay_type': u'B',
 u'bioactivity_type': u'IC50',
 u'ingredient_cmpd_chemblid': u'CHEMBL509101',
 u'name_in_reference': u'2',
 u'operator': u'=',
 u'organism': u'Homo sapiens',
 u'parent_cmpd_chemblid': u'CHEMBL509101',
 u'reference': u'J. Med. Chem., (2008) 51:17:5330',
 u'target_chemblid': u'CHEMBL3717',
 u'target_confidence': 8,
 u'target_name': u'Hepatocyte growth factor receptor',
 u'units': u'nM',
 u'value': u'1.8'}

Result can get as python dict-type. If you can use pandas, dic can convert data frame.

import pandas as pd
df = pd.DataFrame(bio_act)
In [15]: df.head()
Out[15]: 
  activity_comment assay_chemblid  assay_description assay_type  \
0      Unspecified  CHEMBL1003887  Inhibition of MET          B   
1      Unspecified  CHEMBL1003887  Inhibition of MET          B   
2      Unspecified  CHEMBL1003887  Inhibition of MET          B   
3      Unspecified  CHEMBL1003887  Inhibition of MET          B   
4      Unspecified  CHEMBL1003887  Inhibition of MET          B   

  bioactivity_type ingredient_cmpd_chemblid name_in_reference operator  \
0             IC50             CHEMBL509101                 2        =   
1             IC50             CHEMBL459876                49        =   
2             IC50             CHEMBL459875                 4        =   
3             IC50             CHEMBL508403                47        =   
4             IC50             CHEMBL451789                40        =   

       organism parent_cmpd_chemblid                         reference  \
0  Homo sapiens         CHEMBL509101  J. Med. Chem., (2008) 51:17:5330   
1  Homo sapiens         CHEMBL459876  J. Med. Chem., (2008) 51:17:5330   
2  Homo sapiens         CHEMBL459875  J. Med. Chem., (2008) 51:17:5330   
3  Homo sapiens         CHEMBL508403  J. Med. Chem., (2008) 51:17:5330   
4  Homo sapiens         CHEMBL451789  J. Med. Chem., (2008) 51:17:5330   

  target_chemblid  target_confidence                        target_name units  \
0      CHEMBL3717                  8  Hepatocyte growth factor receptor    nM   
1      CHEMBL3717                  8  Hepatocyte growth factor receptor    nM   
2      CHEMBL3717                  8  Hepatocyte growth factor receptor    nM   
3      CHEMBL3717                  8  Hepatocyte growth factor receptor    nM   
4      CHEMBL3717                  8  Hepatocyte growth factor receptor    nM   

  value  
0   1.8  
1   1.8  
2   1.3  
3   470  
4  1400  

Also easy to get compound data.

cmp = comp.get( df.ingredient_cmpd_chemblid[0] )
c=comp.get(df.ingredient_cmpd_chemblid[0])
print c

{u'smiles': u'Fc1ccc(cc1)N2C=CC=C(C(=O)Nc3ccc(Oc4ccnc5[nH]ccc45)c(F)c3)C2=O', u'chemblId': u'CHEMBL509101', u'passesRuleOfThree': u'No', u'molecularWeight': 458.42, u'molecularFormula': u'C25H16F2N4O3', u'acdLogp': 1.92, u'stdInChiKey': u'OBSFXHDOLBYWRJ-UHFFFAOYSA-N', u'acdLogd': 1.91, u'knownDrug': u'No', u'medChemFriendly': u'No', u'rotatableBonds': 5, u'acdAcidicPka': 10.72, u'alogp': 3.52, u'numRo5Violations': 0, u'species': u'NEUTRAL', u'acdBasicPka': 5.46}

Easy to use. 😉

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