Tutorial

We’ll show here how to explain molecular property prediction tasks without access to the gradients or any properties of a molecule. To set-up this activity, we need a black box model. We’ll use something simple here – the model is classifier that says if a molecule as an alcohol (1) or not (0). Let’s implement this model first

from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole

# set-up rdkit drawing preferences
IPythonConsole.ipython_useSVG = True
IPythonConsole.drawOptions.drawMolsSameScale = False

def model(smiles):
    mol = Chem.MolFromSmiles(smiles)
    match = mol.GetSubstructMatches(Chem.MolFromSmarts('[O;!H0]'))
    return 1 if match else 0

Let’s now try it out on some molecules

smi = 'CCCCCCO'
print('f(s)', model(smi))
Chem.MolFromSmiles(smi)

f(s) 1

smi = 'OCCCCCCO'
print('f(s)', model(smi))
Chem.MolFromSmiles(smi)

f(s) 1

smi = 'c1ccccc1'
print('f(s)', model(smi))
Chem.MolFromSmiles(smi)

f(s) 0

Counterfacutal explanations

Let’s now explain the model - pretending we don’t know how it works - using counterfactuals

import exmol

instance = 'CCCCCCO'
space = exmol.sample_space(instance, model, batched=False)

cfs = exmol.cf_explain(space, 1)
exmol.plot_cf(cfs)

We can see that removing the alcohol is the smallest change to affect the prediction of this molecule. Let’s see the space and look at where these counterfactuals are.

exmol.plot_space(space, cfs)

Explain using substructures

Now we’ll try to explain our model using substructures.

exmol.lime_explain(space)
exmol.plot_descriptors(space)

This seems like a pretty clear explanation. Let’s take a look at using substructures that are present in the molecule

import skunk
exmol.lime_explain(space, descriptor_type='ECFP')
svg = exmol.plot_descriptors(space, return_svg=True)
skunk.display(svg)
exmol.plot_utils.similarity_map_using_tstats(space[0])

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [9], in <cell line: 4>()
      2 exmol.lime_explain(space, descriptor_type='ECFP')
      3 svg = exmol.plot_descriptors(space, return_svg=True)
----> 4 skunk.display(svg)
      5 exmol.plot_utils.similarity_map_using_tstats(space[0])

File /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/skunk/__init__.py:64, in display(svg)
     62 import IPython.display as display
     63 import base64
---> 64 data = base64.b64encode(svg.encode('utf8'))
     65 display.display(display.HTML(
     66     '<img src=data:image/svg+xml;base64,' + data.decode() + '>'))

AttributeError: 'NoneType' object has no attribute 'encode'

We can see that most of the model is explained from the presence of the alcohol group - as expected