exmol 0.3.1

Counterfactual generation with STONED SELFIES

Explaining why that molecule

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. View on Github.

Install

pip install exmol

Counterfactual Generation

Our package implements the Model Agnostic Counterfactual Compounds with STONED (MACCS) to generate counterfactuals. A counterfactual can explain a prediction by showing what would have to change in the molecule to change its predicted class. Here is an eample of a counterfactual:

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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.

Usage

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 = 'CCCO'

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[0])

Examples(
  smiles='CCOC(=O)c1ccc(N=CN(Cl)c2ccccc2)cc1',
  selfies='[C][C][O][C][Branch1_2][C][=O][C][=C][C][=C][Branch1_1][#C][N][=C][N][Branch1_1][C][Cl][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][=C][Ring1][S]',
  similarity=0.8181818181818182,
  yhat=-5.459493637084961,
  index=1807,
  position=array([-6.11371691,  1.24629293]),
  is_origin=False,
  cluster=26,
  label='Counterfactual')

Chemical Space

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. Experimental

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.

API and Docs

Read docs here. You should also read the paper (see below) for a more exact description of the methods and implementation.

Citation

Please cite Wellawatte et al.

 @article{wellawatte_seshadri_white_2021,
 place={Cambridge},
 title={Model agnostic generation of counterfactual explanations for molecules},
 DOI={10.33774/chemrxiv-2021-4qkg8},
 journal={ChemRxiv},
 publisher={Cambridge Open Engage},
 author={Wellawatte, Geemi P and Seshadri, Aditi and White, Andrew D},
 year={2021}}

This content is a preprint and has not been peer-reviewed.