ChEMBL_22_1 data update:
We would like to inform users that an update to ChEMBL_22 has been released.
The new version, ChEMBL_22_1, corrects an issue with the targets assigned to some BindingDB assays in ChEMBL (src_id = 37). If you are using the BindingDB data from ChEMBL, we recommend you download this update. This update also incorporates the mol file/canonical smiles correction announced previously.
Updates have been made to BindingDB data in the ASSAYS, ACTIVITIES, CHEMBL_ID_LOOKUP, LIGAND_EFF and PREDICTED_BINDING_DOMAINS tables. Corrections have also been made to molfiles and canonical_smiles in the COMPOUND_STRUCTURES table. No changes have been made to other data sets or to other drug/compound/target tables in ChEMBL_22.
The new release files can be downloaded from:
ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_22_1
A new version of the ChEMBL RDF is also available from:
ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBL-RDF/22.1
Improvements to Web Services:
1. Support for SDF format.
The "molecule" endpoint now supports the SDF format. For example, if you access this URL: https://www.ebi.ac.uk/chembl/api/data/molecule.json you will get information about 20 first compounds in JSON format. This URL https://www.ebi.ac.uk/chembl/api/data/molecule.sdf will return an SDF file of the same molecule page. Please note, that there will be only 18 compounds in SDF output because two compounds from https://www.ebi.ac.uk/chembl/api/data/molecule.json (CHEMBL6961 and CHEMBL6963) have no structure defined. You can easily join the information about the compound provided via JSON, XML or YML format with the structure by inspecting the
The "molecule" endpoint now supports the SDF format. For example, if you access this URL: https://www.ebi.ac.uk/chembl/api/data/molecule.json you will get information about 20 first compounds in JSON format. This URL https://www.ebi.ac.uk/chembl/api/data/molecule.sdf will return an SDF file of the same molecule page. Please note, that there will be only 18 compounds in SDF output because two compounds from https://www.ebi.ac.uk/chembl/api/data/molecule.json (CHEMBL6961 and CHEMBL6963) have no structure defined. You can easily join the information about the compound provided via JSON, XML or YML format with the structure by inspecting the
> <chembl_id>
sdf property.
Obviously the same format works for a single compound so this URL: https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json will provide an information about Aspirin while this URL https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.mol (or https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.sdf) will return its structure.
The same can be applied to filters, for example this URL https://www.ebi.ac.uk/chembl/api/data/molecule?molecule_properties__mw_freebase__lte=300&pref_name__iendswith=nib returns information about compounds with molecular weight <= 300 AND pref_name ending with nib. The https://www.ebi.ac.uk/chembl/api/data/molecule.sdf?molecule_properties__mw_freebase__lte=300&pref_name__iendswith=nib in turn will return corresponding structures.
We also released a new version of Python client (version 0.8.50 available from PyPI and GitHub) that is aware about molfile support. Example code:
from chembl_webresource_client.new_client import new_client
molecules = new_client.molecule
molecules.set_format('sdf')
molstring = molecules.all()[0]
Iterating through all molecules you can get an sdf files with all the structures from chembl, pagination is handled by the client.
2. Structural alerts.
This new API endpoint provides information about compound's structural alerts. For example, on order to get structural alerts for CHEMBL266429, you can use this URL: https://www.ebi.ac.uk/chembl/api/data/compound_structural_alert.json?molecule_chembl_id=CHEMBL266429
The same can be applied to filters, for example this URL https://www.ebi.ac.uk/chembl/api/data/molecule?molecule_properties__mw_freebase__lte=300&pref_name__iendswith=nib returns information about compounds with molecular weight <= 300 AND pref_name ending with nib. The https://www.ebi.ac.uk/chembl/api/data/molecule.sdf?molecule_properties__mw_freebase__lte=300&pref_name__iendswith=nib in turn will return corresponding structures.
We also released a new version of Python client (version 0.8.50 available from PyPI and GitHub) that is aware about molfile support. Example code:
from chembl_webresource_client.new_client import new_client
molecules = new_client.molecule
molecules.set_format('sdf')
molstring = molecules.all()[0]
Iterating through all molecules you can get an sdf files with all the structures from chembl, pagination is handled by the client.
2. Structural alerts.
This new API endpoint provides information about compound's structural alerts. For example, on order to get structural alerts for CHEMBL266429, you can use this URL: https://www.ebi.ac.uk/chembl/api/data/compound_structural_alert.json?molecule_chembl_id=CHEMBL266429
Then you can render each of the alerts to image, for example https://www.ebi.ac.uk/chembl/api/data/compound_structural_alert/898.png:
As you can see, the corresponding fragment is highlighted.You can add all parameters that are present in the standard "image" endpoint so format (png or svg), engine (rdkit or indigo), ignoreCoords to recompute coordinates from scratch and dimensions to change image size.
3. Document terms (keywords)
We used pytextrank package to extract most relevant terms from all document abstracts stored in ChEMBL, along with their significance score against each document (the code we used to perform the extraction is available).
For example, in order to get all the relevant terms for CHEMBL1124199 document, ordered by the significance score descending, you can use this URL:
https://www.ebi.ac.uk/chembl/api/data/document_term.json?document_chembl_id=CHEMBL1124199&order_by=-score
By parsing the results you can extract (term, score) pairs and multiply the score to get this list:
590 Inverse agonist activity
548 Thien-2-yl analogues
493 Pentylenetetrazole-induced convulsions
490 5'-alkyl group
477 Agonist activity
472 Inverse agonist
449 5-methylthien-3-yl derivative
427 Potent compounds
417 Vivo activity
403 Magnitude higher affinity
you can now use the HTML5 based word cloud and feed the list into this tool providing the following configuration:
{
gridSize: Math.round(16 * $('#canvas').width() / 1024),
drawOutOfBound: true,
weightFactor: function (size) {
return Math.pow(size/100.0, 2.3) * $('#canvas').width() / 1024;
},
fontFamily: 'Times, serif',
hover: function(){},
color: function (word, weight) {
return (weight > 500) ? '#f02222' : '#c09292';
},
rotateRatio: 0.0,
backgroundColor: '#ffe0e0'
}
and you will get this wordcloud:
We are planning to add this component to the new document report card.
It may be also interesting to ask about all the documents for a given keyword, for example in order to get all the documents for the "inverse agonist activity" term ordered by score descending, the following URL can be used: https://www.ebi.ac.uk/chembl/api/data/document_term.json?term_text=inverse%20agonist%20activity&order_by=-score
4. Document similarity
As the last endpoint we added "document_similarity". For example to get all documents similar to CHEMBL1122254 document this URL can be used: https://www.ebi.ac.uk/chembl/api/data/document_similarity.json?document_1_chembl_id=CHEMBL1122254
The endpoint uses the same protocol we use to generate the "Related Documents" section in the Document Report Card (https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1122254).
The current protocol is fairly simple (measuring overlap in compounds and targets between the two documents) and not very granular (it can be difficult to choose N most relevant documents from the 50 documents that the protocol returns). However, we are currently investigating alternative methods such as topic modelling.
3. Document terms (keywords)
We used pytextrank package to extract most relevant terms from all document abstracts stored in ChEMBL, along with their significance score against each document (the code we used to perform the extraction is available).
For example, in order to get all the relevant terms for CHEMBL1124199 document, ordered by the significance score descending, you can use this URL:
https://www.ebi.ac.uk/chembl/api/data/document_term.json?document_chembl_id=CHEMBL1124199&order_by=-score
By parsing the results you can extract (term, score) pairs and multiply the score to get this list:
590 Inverse agonist activity
548 Thien-2-yl analogues
493 Pentylenetetrazole-induced convulsions
490 5'-alkyl group
477 Agonist activity
472 Inverse agonist
449 5-methylthien-3-yl derivative
427 Potent compounds
417 Vivo activity
403 Magnitude higher affinity
you can now use the HTML5 based word cloud and feed the list into this tool providing the following configuration:
{
gridSize: Math.round(16 * $('#canvas').width() / 1024),
drawOutOfBound: true,
weightFactor: function (size) {
return Math.pow(size/100.0, 2.3) * $('#canvas').width() / 1024;
},
fontFamily: 'Times, serif',
hover: function(){},
color: function (word, weight) {
return (weight > 500) ? '#f02222' : '#c09292';
},
rotateRatio: 0.0,
backgroundColor: '#ffe0e0'
}
and you will get this wordcloud:
We are planning to add this component to the new document report card.
It may be also interesting to ask about all the documents for a given keyword, for example in order to get all the documents for the "inverse agonist activity" term ordered by score descending, the following URL can be used: https://www.ebi.ac.uk/chembl/api/data/document_term.json?term_text=inverse%20agonist%20activity&order_by=-score
4. Document similarity
As the last endpoint we added "document_similarity". For example to get all documents similar to CHEMBL1122254 document this URL can be used: https://www.ebi.ac.uk/chembl/api/data/document_similarity.json?document_1_chembl_id=CHEMBL1122254
The endpoint uses the same protocol we use to generate the "Related Documents" section in the Document Report Card (https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1122254).
The current protocol is fairly simple (measuring overlap in compounds and targets between the two documents) and not very granular (it can be difficult to choose N most relevant documents from the 50 documents that the protocol returns). However, we are currently investigating alternative methods such as topic modelling.
5. Other improvements
There are some minor improvements as well:
- Molecule endpoint includes three more properties as described in GitHub issue #106.
- Target endpoint can be filtered by synonym name, in other words you can get a list of targets for a given gene name, for example: https://www.ebi.ac.uk/chembl/api/data/target.json?target_components__target_component_synonyms__component_synonym__icontains=GABRB2
or using a shortcut: https://www.ebi.ac.uk/chembl/api/data/target.json?target_synonym__icontains=GABRB2
- Target relation endpoint can be accessed by primary ID as described in GitHub issue #114.
- parent_chembl_id filter working correctly for the molecule_form endpoint (for example https://www.ebi.ac.uk/chembl/api/data/molecule_form?parent_chembl_id=CHEMBL660 ) as described in GitHub issue #113.
The ChEMBL Team
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