Interactive ligands visualizer

I (Olivier) put together this interactive visualizer to make sure that I don't miss out some chemotypes when coming up with actives at the start of a retrospective campaign. Starting from a downloaded ChEMBL CSV file for a list of ligands, images of each molecule are generated with RDKit and a text file with filtered Smiles is generated. You then need to compute the ECFP fingerprints on Gimel from that file (see below), and then a generated script will show an interactive visualization of the chemical space spanned by the ligands (tSNE), with each molecule shown on mouse hovering.

Step 1: install chemspace_vis package

Make sure you are using Python 3, and then simply:

pip install chemspace_vis

N.B. This only works on Mac and Linux, sorry Windows users (if you exist).

Step 2: obtain ChEMBL CSV file (or use provided example)

Any ChEMBL CSV from a given activity of a given target will do.

You can also clone the example repository, which contains the CSV for mu-opioid ligands with measured Emax and an example script:

git clone https://github.com/gregorpatof/chemspace_vis_example

Just to make things too clear, here is how I obtained that CSV:

• 

  Mu-opioid receptor on ChEMBL

• 

  1100 ligands with measured Emax

• 

  Generating the CSV

Step 3: extract Smiles and activity for given HAC and MW filters

This is accomplished by the preprocess_part1() method in the example script, which runs a single command:

from chemspace_vis.preprocess import preprocess_chembl

chembl_csv = "mor_chembl_emax.csv"

activity_name = "Emax" # The text name of the activity (in this case, Emax)
preprocess_chembl(chembl_csv, activity_name, max_hac=35, max_mw=600, img_folder="mol_images")

As you can see, you can specify the maximum number of heavy atoms (max_hac) and maximum molecular weight (max_mw) for the ligands to keep.

This will generate two files: a .smi file with the Smiles for all the kept ligands, and .df file which keeps the activity value (Emax here) in dataframe format.

Step 4: compute the fingerprints on Gimel

Copy the .smi file to gimel, source the DOCK3.7 base, and then run this command (on gimel, not gimel2 or others):

python ~jklyu/zzz.github/ChemInfTools/utils/teb_chemaxon_cheminf_tools/generate_chemaxon_fingerprints.py mor_chembl_emax.smi mor_chembl_emax

This will generate a .fp file, in the present case mor_chembl_emax.fp

Step 5: tSNE and interactive visualization

Almost done! Copy the .fp file back to your machine, then run part 2 of the example script:

from chemspace_vis.preprocess import make_tsne_from_fingerprints
from chemspace_vis.visualizer import make_visualizer_script

fingerprints_file = "mor_chembl_emax.fp"
make_tsne_from_fingerprints(fingerprints_file)
make_visualizer_script("tsne_data.df", "mol_images",
                       activity_filename="mor_chembl_emax_activity.df", use_log10=False)