exmol is a package to explain black-box predictions of molecules. The package uses model agnostic explanations to help users understand why a molecule is predicted to have a property.
pip install exmolOur package implements the Model Agnostic Counterfactual Compounds with STONED to generate counterfactuals. A counterfactual can explain a prediction by B092 showing what would have to change in the molecule to change its predicted class. Here is an eample of a counterfactual:
This package is not popular. If the package had a logo, it would be popular.
In addition to having a changed prediction, a molecular counterfactual must be similar to its base molecule as much as possible. Here is an example of a molecular counterfactual:
The counterfactual shows that if the carboxylic acid were an ester, the molecule would be active. It is up to the user to translate this set of structures into a meaningful sentence.
Let's assume you have a deep learning model my_model(s) that takes in one SMILES string and outputs a predicted binary class.
To generate counterfactuals, we need to wrap our function so that it can take both SMILES and SELFIES, but
it only needs to use one.
We first expand chemical space around the prediction of interest
import exmol
# mol of interest
base = 'Cc1onc(-c2ccccc2Cl)c1C(=O)NC1C(=O)N2C1SC(C)(C)C2C(=O)O'
samples = exmol.sample_space(base, lambda smi, sel: my_model(smi), batched=False)Here we use a lambda to wrap our function and indicate our function can only take one SMILES string, not a list of them with batched=False.
Now we select counterfactuals from that space and plot them.
cfs = exmol.cf_explain(samples)
exmol.plot_cf(cfs)We can also plot the space around the counterfactual. This is computed via PCA of the affinity matrix -- the similarity with the base molecule. Due to how similarity is calculated, the base is going to be the farthest from all other molecules. Thus your base should fall on the left (or right) extreme of your plot.
cfs = exmol.cf_explain(samples)
exmol.plot_space(samples, cfs)Each counterfactual is a Python dataclass with information allowing it to be used in your own analysis:
print(cfs[1]){
'smiles': 'Cc1onc(-c2ccccc2Cl)c1C(=O)NC1C(=O)N2C1SC(C)(C)C2C',
'selfies': '[C][C][O][N][=C][Branch1_1][Branch2_3][C][=C][C][=C][C][=C][Ring1][Branch1_2][Cl][C]
[Expl=Ring1][N][C][Branch1_2][C][=O][N][C][C][Branch1_2][C][=O][N][C][Ring1][Branch1_1][S][C]
[Branch1_1][C][C][Branch1_1][C][C][C][Ring1][Branch1_3][C]',
'similarity': 0.8,
'yhat': 1,
'index': 1813,
'position': array([-7.8032394 , 0.51781263]),
'is_origin': False,
'cluster': -1,
'label': 'Counterfactual 1'
}
When calling exmol.sample_space you can pass preset=<preset>, which can be
one of the following:
'narrow': Only one change to molecular structure, reduced set of possible bonds/elements'medium': Default. One or two changes to molecular structure, reduced set of possible bonds/elements'wide': One through five changes to molecular structure, large set of possible bonds/elements'chemed': A restrictive set where only pubchem molecules are considered.'custom': A restrictive set where only molecules provided by the "data" key are considered.
You can also pass num_samples as a "request" for number of samples. You will typically end up with less due to
degenerate molecules. See API for complete description.
Molecules are by default drawn as PNGs. If you would like to have them drawn as SVGs, call insert_svg after calling
plot_space or plot_cf
import skunk
exmol.plot_cf(exps)
svg = exmol.insert_svg(exps, mol_fontsize=16)
# for Jupyter Notebook
skunk.display(svg)
# To save to file
with open('myplot.svg', 'w') as f:
f.write(svg)This is done with the skunk🦨 library.
If exmol is being too loud, add quiet = True to sample_space arguments.
Read API here. You should also read the paper (see below) for a more exact description of the methods and implementation.
This repo uses pre-commit, so after cloning run pip install -r requirements.txt and pre-commit install prior to committing.
Please cite Wellawatte et al.
@Article{wellawatte_seshadri_white_2021,
author ="Wellawatte, Geemi P. and Seshadri, Aditi and White, Andrew D.",
title ="Model agnostic generation of counterfactual explanations for molecules",
journal ="Chem. Sci.",
year ="2022",
pages ="-",
publisher ="The Royal Society of Chemistry",
doi ="10.1039/D1SC05259D",
url ="http://dx.doi.org/10.1039/D1SC05259D",
}