Interactive ligands visualizer - Revision history

Omailhot at 23:54, 27 January 2023

2023-01-27T23:54:14Z

← Older revision		Revision as of 23:54, 27 January 2023
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	This will generate a .fp file, in the present case mor_chembl_emax.fp		This will generate a .fp file, in the present case mor_chembl_emax.fp

			I personally like to use bash, so the way I source the DOCK3.7 base is:

			export DOCKBASE=/nfs/soft/dock/versions/dock37/DOCK-3.7-trunk
			source /nfs/soft/dock/versions/dock37/DOCK-3.7-trunk/env.sh

2023-01-27T23:17:53Z

@@ Line 19: / Line 19: @@
 '''Step 2: obtain ChEMBL CSV file (or use provided example)'''
-Any ChEMBL CSV from a given activity of a given target will do.
+Any ChEMBL CSV from a given activity of a given target will do. If you want to run the visualizer directly from smiles and fingerprints, see bottom of page.
 You can also clone the example repository, which contains the CSV for mu-opioid ligands with measured Emax and an example script:
@@ Line 73: / Line 73: @@
 The first command will compute tSNE from the fingerprints. You will see a print telling you what percentage of the variance is covered by the PCA first applied (anything over 90-95% is good).
 Then, the visualizer script will be generated. If you supply an activity filename, you will get coloring based on that property (here, Emax). The use_log10 flag can be useful if you have extreme values driving the coloring.
@@ Line 86: / Line 94: @@
 [[File:mor_zoom_example.gif]]

2023-01-27T22:37:57Z

← Older revision		Revision as of 22:37, 27 January 2023
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	'''Step 1: install chemspace_vis package'''		'''Step 1: install chemspace_vis package'''

			The visualizer will need to be installed on your local machine, not the cluster.

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

2023-01-20T23:12:50Z

← Older revision		Revision as of 23:12, 20 January 2023
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	N.B. This only works on Mac and Linux, sorry Windows users (if you exist).		N.B. This only works on Mac and Linux, sorry Windows users (if you exist).

			For those interested, the source code can be found on my GitHub: https://github.com/gregorpatof/chemspace_vis_package

2023-01-20T23:07:48Z

← Older revision		Revision as of 23:07, 20 January 2023
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	fingerprints_file = "mor_chembl_emax.fp"		fingerprints_file = "mor_chembl_emax.fp"
	make_tsne_from_fingerprints(fingerprints_file)		make_tsne_from_fingerprints(fingerprints_file)
	make_visualizer_script("tsne_data.df", "mol_images",		make_visualizer_script("tsne_data.df", "mol_images", activity_filename="mor_chembl_emax_activity.df", use_log10=False)
	activity_filename="mor_chembl_emax_activity.df", use_log10=False)

	The first command will compute tSNE from the fingerprints. You will see a print telling you what percentage of the variance is covered by the PCA first applied (anything over 90-95% is good).		The first command will compute tSNE from the fingerprints. You will see a print telling you what percentage of the variance is covered by the PCA first applied (anything over 90-95% is good).

	Then, the visualizer script will be generated.		Then, the visualizer script will be generated. If you supply an activity filename, you will get coloring based on that property (here, Emax). The use_log10 flag can be useful if you have extreme values driving the coloring.

2023-01-20T23:04:19Z

← Older revision		Revision as of 23:04, 20 January 2023
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	You can then zoom on parts where ligands are close together, and go back to the general view with the back arrow:		You can then zoom on parts where ligands are close together, and go back to the general view with the back arrow:

	[[mor_zoom_example.gif]]		[[File:mor_zoom_example.gif]]

2023-01-20T23:04:05Z

← Older revision		Revision as of 23:04, 20 January 2023
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	python visualizer_script.py		python visualizer_script.py

	You can then zoom on parts where ligands are close together, and go back to the general view with the back arrow.		You can then zoom on parts where ligands are close together, and go back to the general view with the back arrow:

			[[mor_zoom_example.gif]]

2023-01-20T22:52:41Z

@@ Line 44: / Line 44: @@
 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.
@@ Line 66: / Line 68: @@
   make_visualizer_script("tsne_data.df", "mol_images",
                          activity_filename="mor_chembl_emax_activity.df", use_log10=False)

2023-01-20T22:45:39Z

@@ Line 38: / Line 38: @@
   chembl_csv = "mor_chembl_emax.csv"
-  activity_name = "IC50" # The text name of the activity (in this case, IC50)
+  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")

2023-01-20T22:21:37Z

← Older revision		Revision as of 22:21, 20 January 2023
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	Any ChEMBL CSV from a given activity of a given target will do.		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 ~~....~~ and an example script:		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		git clone https://github.com/gregorpatof/chemspace_vis_example

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

			<gallery>
			chembl_mor1.png\|Mu-opioid receptor on ChEMBL
			chembl_mor2.png\|1100 ligands with measured Emax
			chembl_mor3.png\|Generating the CSV
			</gallery>


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	from chemspace_vis.preprocess import preprocess_chembl		from chemspace_vis.preprocess import preprocess_chembl

	chembl_csv = "~~c5a_ic50_chembl~~.csv"		chembl_csv = "mor_chembl_emax.csv"

	activity_name = "IC50" # The text name of the activity (in this case, IC50)		activity_name = "IC50" # The text name of the activity (in this case, IC50)
	preprocess_chembl(chembl_csv, activity_name, max_hac=35, max_mw=600, img_folder="mol_images")		preprocess_chembl(chembl_csv, activity_name, max_hac=35, max_mw=600, img_folder="mol_images")