Olivier's way of computing novelty - Revision history

Omailhot at 20:10, 22 September 2023

2023-09-22T20:10:46Z

← Older revision		Revision as of 20:10, 22 September 2023
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	get_novelty('chembl.smi', 'your_mols.smi', 'novelty.txt', threshold=0.35)		get_novelty('chembl.smi', 'your_mols.smi', 'novelty.txt', threshold=0.35)

			'''Note:'''

			There are intermediary files created with the '.fp' file extension. If RDKit fails to read some of your input SMILES, delete all .fp files before rerunning.

	'''Step 3: ultimate laziness'''		'''Step 3: ultimate laziness'''

Omailhot at 01:12, 21 September 2023

2023-09-21T01:12:03Z

← Older revision		Revision as of 01:12, 21 September 2023
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	'''Step 1: get all knowns from ChEMBL'''		'''Step 1: get all knowns from ChEMBL'''

	Here we don't really care about properties, drug-likeness etc. So just ~~got~~ to your target on ChEMBL, grab the version with the highest number of compounds, and click on the number of associated compounds to view the list of them (no activity data). Then, download as csv. Unzip the file and rename it to what you want, we will call it chembl.csv here. Now to generate a .smi file from this, simply use:		Here we don't really care about properties, drug-likeness etc. So just go to your target on ChEMBL, grab the version with the highest number of compounds, and click on the number of associated compounds to view the list of them (no activity data). Then, download as csv. Unzip the file and rename it to what you want, we will call it chembl.csv here. Now to generate a .smi file from this, simply use:

	from bksltk.utils import write_smiles_from_chembl		from bksltk.utils import write_smiles_from_chembl

Omailhot: Created page with "This is my lazy way of doing novelty calculations. '''Step 0: source my environment''' All I'm developing is part of a single Python package called bksltk (BKS-lab Toolkit). So just source my Python environment on gimel2 (or other gimelX which has Python3) and you'll have access to everything: source /nfs/home/omailhot/pyenv_source.sh Eventually the package will be better documented (and probably integrated into pydock). '''Step 1: get all knowns from ChEMBL''' H..."

2023-09-20T21:42:02Z

Created page with "This is my lazy way of doing novelty calculations. '''Step 0: source my environment''' All I'm developing is part of a single Python package called bksltk (BKS-lab Toolkit). So just source my Python environment on gimel2 (or other gimelX which has Python3) and you'll have access to everything: source /nfs/home/omailhot/pyenv_source.sh Eventually the package will be better documented (and probably integrated into pydock). '''Step 1: get all knowns from ChEMBL''' H..."

New page

This is my lazy way of doing novelty calculations.

'''Step 0: source my environment'''

All I'm developing is part of a single Python package called bksltk (BKS-lab Toolkit). So just source my Python environment on gimel2 (or other gimelX which has Python3) and you'll have access to everything:

source /nfs/home/omailhot/pyenv_source.sh

Eventually the package will be better documented (and probably integrated into pydock).

'''Step 1: get all knowns from ChEMBL'''

Here we don't really care about properties, drug-likeness etc. So just got to your target on ChEMBL, grab the version with the highest number of compounds, and click on the number of associated compounds to view the list of them (no activity data). Then, download as csv. Unzip the file and rename it to what you want, we will call it chembl.csv here. Now to generate a .smi file from this, simply use:

from bksltk.utils import write_smiles_from_chembl

write_smiles_from_chembl('chembl.csv', 'chembl.smi')

'''Step 2: compute novelty'''

You will also need your list of molecules, as a ".smi" file which is basically just a text file, without any header, and with whitespace-separated data of which the first column is the SMILES strings of your compound. The second column should be an identifier. Look at the 'chembl.smi' file if you are confused. Then, to get a dataframe of the maximum similarities to any of your knowns, simply do:

from bksltk.utils import get_novelty

get_novelty('chembl.smi', 'your_mols.smi', 'novelty.txt')

This will write all the maximum Tcs to the 'novelty.txt' file. Alternatively, you can give a Tc threshold and only molecules less similar than the threshold will be written out:

get_novelty('chembl.smi', 'your_mols.smi', 'novelty.txt', threshold=0.35)

'''Step 3: ultimate laziness'''

Combine these in a single script:

from bksltk.utils import write_smiles_chembl, get_novelty

write_smiles_chembl('chembl.csv', 'chembl.smi')
get_novelty('chembl.smi', 'your_mols.smi', 'novelty.txt', threshold=your_threshold)