Data Deposition made easy

Bioactivity data in ChEMBL originates from two main sources: biomedical literature and data entering ChEMBL via data deposition. Over the years, we have seen an increase in deposited data sets (as shown in the Figure above).

To make data deposition easier, we recently did some major updates to our deposition guide and supplemented it with a deposition video, and a brief checklist for depositors.

Compound structural data must be supplied to the ChEMBL team in CTAB format / as MOL file. If a whole set of structures is supplied, then an SDF file is required. There are several ways to convert a list of SMILES to SDF. One way is to use our public ChEMBL Beaker API. Its functionalities are explained in more detail here and they can be tested interactively here. Just a few lines of code are needed to convert a list of SMILES strings into SD format.

from chembl_webresource_client.utils import utils

smiles = ["CCO", "CC(=O)C1=CC=CC=C1C(=O)O", "CCN(CC)C(=O)C(C)(C)C"]
mols = [utils.smiles2ctab(smile) for smile in smiles]
sdf = ''.join(mols)
sdf

However, for data deposition to ChEMBL you need to provide an SDF file that also includes the CIDX. A CIDX is a compound identifier linked to every compound, respectively. The respective output file should look like this:

     RDKit          2D

 12 12  0  0  0  0  0  0  0  0999 V2000
    3.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    3.0000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    3.7500    1.2990    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
    1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    1.5000    2.5981    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    3.0000    2.5981    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
    0.7500    3.8971    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
  1  2  1  0
  2  3  2  0
  2  4  1  0
  4  5  2  0
  5  6  1  0
  6  7  2  0
  7  8  1  0
  8  9  2  0
  9 10  1  0
 10 11  2  0
 10 12  1  0
  9  4  1  0
M  END
>  <CIDX>  (1) 
MMV161996

$$$$

Below you find a few quick ways to generate the required SDF with CIDX included. You can use the rdkit.Chem package in several ways:

from rdkit import Chem

smiles_list = ["CCO", "CC(=O)C1=CC=CC=C1C(=O)O", "CCN(CC)C(=O)C(C)(C)C"]
cidx_list = [1, 2, 3]

with Chem.SDWriter("out.sdf") as writer:
    for smiles, cidx in zip(smiles_list, cidx_list):
        mol = Chem.MolFromSmiles(smiles)
        mol.SetProp("CIDX", str(cidx))
        writer.write(mol)

Using the rdkit.Chem.PandasTools module allows for using RDKit molecules as columns of a Pandas dataframe. In this example the input data is a excel spreadsheet, which will come in handy for data depositors:

import pandas as pd
from rdkit.Chem import PandasTools

df = pd.read_excel("CIDX_smiles.xlsx", names=["CIDX", "smiles"])
PandasTools.AddMoleculeColumnToFrame(df, "smile","Molecule") 
PandasTools.WriteSDF(df, "out.sdf", molColName='Molecule",

                     properties=list(df.columns))

Should you want to avoid code, here is an alternative solution:

The KNIME workflow reads in a Excel file containing SMILES and CIDX in two separate columns.

It then transforms SMILES strings into the SMILES format (Molecule Type Cast Node), removes rows with missing structure, converts SMILES into RDKit canonical SMILES (RDKit Canon Smiles Node), and finally transforms it into SD format (RDKit to Molecule) and writes out the SDF to the indicated path (SDF Writer).

Happy structure conversion!

Comments

ChEMBL 34 is out!

We are delighted to announce the release of ChEMBL 34, which includes a full update to drug and clinical candidate drug data. This version of the database, prepared on 28/03/2024 contains: 2,431,025 compounds (of which 2,409,270 have mol files) 3,106,257 compound records (non-unique compounds) 20,772,701 activities 1,644,390 assays 15,598 targets 89,892 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/ Please see ChEMBL_34 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/chembl_34_release_notes.txt New Data Sources European Medicines Agency (src_id = 66): European Medicines Agency's data correspond to EMA drugs prior to 20 January 2023 (excluding vaccines). 71 out of the 882 newly added EMA drugs are only authorised by EMA, rather than from other regulatory bodies e.g.

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! Almost 10 years ago , EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new f

Accessing SureChEMBL data in bulk

It is the peak of the summer (at least in this hemisphere) and many of our readers/users will be on holiday, perhaps on an island enjoying the sea. Luckily, for the rest of us there is still the 'sea' of SureChEMBL data that awaits to be enjoyed and explored for hidden 'treasures' (let me know if I pushed this analogy too far). See here and here for a reminder of SureChEMBL is and what it does. This wealth of (big) data can be accessed via the SureChEMBL interface , where users can submit quite sophisticated and granular queries by combining: i) Lucene fields against full-text and bibliographic metadata and ii) advanced structure query features against the annotated compound corpus. Examples of such queries will be the topic of a future post. Once the search results are back, users can browse through and export the chemistry from the patent(s) of interest. In addition to this functionality, we've been receiving user requests for local (behind the

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

Mapping lists of IDs in ChEMBL

In order to facilitate the mapping of identifiers in ChEMBL, we have developed a new type of search in the ChEMBL Interface. Now, it is possible to enter a list of ChEMBL IDs and see a list of the corresponding entities. Here is an example: 1. Open the ChEMBL Interface , on the main search bar, click on 'Advanced Search': 2. Click on the 'Search by IDs' tab: 3. Select the source entity of the IDs and the destination entity that you want to map to: 4. Enter the identifiers, you can either paste them, or select a file to upload. When you paste IDs, by default it tries to detect the separator. You can also select from a list of separators to force a specific one: Alternatively, you can upload a file, the file can be compressed in GZIP and ZIP formats, this makes the transfer of the file to the ChEMBL servers faster. Examples of the files that can be uploaded to the search by IDs can be found here . 5. Click on the search button: 6. You will be redirected to a search resul

The ChEMBL-og

Search This Blog