This is a big PR!

• [x] Document LIME function
• [x] Compute t-stats using examples that have non-zero weights
• [x] Add plotting code for descriptors - needs SMARTS annotations for MACCS keys (166 files)
• [x] Add plotting code for chemical space and fit
• [x] Description in readme
• [x] Clean up notebooks and add documentation
• [x] Remove extra files
• [x] Add LIME notebooks to CI?

plot_cf() function errors out with the following error. This behavior is also consistent across all notebooks in paper/.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-b6c8ed26216e> in <module>
      1 fkw = {"figsize": (8, 6)}
      2 mpl.rc("axes", titlesize=12)
----> 3 exmol.plot_cf(exps, figure_kwargs=fkw, mol_size=(450, 400), nrows=1)
      4 
      5 plt.savefig("rf-simple.png", dpi=180)

/gpfs/fs2/scratch/hgandhi/exmol/exmol/exmol.py in plot_cf(exps, fig, figure_kwargs, mol_size, mol_fontsize, nrows, ncols)
    682         title += f"\nf(x) = {e.yhat:.3f}"
    683         axs[i].set_title(title)
--> 684         axs[i].imshow(np.asarray(img), gid=f"rdkit-img-{i}")
    685         axs[i].axis("off")
    686     for j in range(i, C * R):

~/.local/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
   1359     def inner(ax, *args, data=None, **kwargs):
   1360         if data is None:
-> 1361             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1362 
   1363         bound = new_sig.bind(ax, *args, **kwargs)

~/.local/lib/python3.7/site-packages/matplotlib/axes/_axes.py in imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)
   5607                               resample=resample, **kwargs)
   5608 
-> 5609         im.set_data(X)
   5610         im.set_alpha(alpha)
   5611         if im.get_clip_path() is None:

~/.local/lib/python3.7/site-packages/matplotlib/image.py in set_data(self, A)
    699                 not np.can_cast(self._A.dtype, float, "same_kind")):
    700             raise TypeError("Image data of dtype {} cannot be converted to "
--> 701                             "float".format(self._A.dtype))
    702 
    703         if self._A.ndim == 3 and self._A.shape[-1] == 1:

TypeError: Image data of dtype <U14622 cannot be converted to float

Hi,

First at all, thank you for your work!. I am obtaining a problem installing your library, o better say when I do "import exmol", I obtaing one error:"No module named 'dataclasses'".

I have installed as: pip install exmol...

Thanks!

While messing around with CODEX, I noticed it wants to compute ECFP4 fingerprints using a different method and this gives slightly different similarities. @geemi725 could you double-check the ECFP4 implementation we have is correct, or is the CODEX one correct?

Hi there, I noticed that sample_space does not seem to work with class instances, because they do not have a __code__ attribute:

import exmol
class A:
    pass
exmol.sample_space('C', A(), batched=True)

AttributeError: 'A' object has no attribute '__code__'

Is there any way around this other than forcing the call to a separate function?

In the notebook RF-lime.ipynb, the command

exmol.lime_explain(space, descriptor_type=descriptor_type)

gives a error module 'exmol' has no attribute 'lime_explain'

Please, let me know how to fix this error. Thanks.

Working through some examples, I've noted the following things:

1. Descriptor type should have a default - maybe MACCS since the plots will show-up
2. Maybe we should only save SVGs, rather than return unless prompted
3. We should do string comparison for descriptor types using lowercase strings, so that classic and Classic and ecfp are valid.
4. We probably shouldn't save without a filename - it is unexpected

Hello @whitead, this is very nice package !

I found the new chemed option very useful and thought extending it to any list of molecule would make sense.

Here is the main change to the API:

explanation = exmol.sample_space(
      "CCCC",
      model,
      preset="custom", #use custom preset
      batched=False,
      data=data, # provide list of smiles or molecules
)

Let me know if this PR make sense.

In your example provided in the code, the target molecule is on the edge of the sampled distribution (in the PCA plot). I also find this happens very frequently with my experiments on my model. I think this suggests that the sampling produces molecules that are not evenly distributed around the target. I just want to verify that this is a property of the STONED sampling algorithm, and not an artifact of the visualization code (which it does not seem to be). I've attached an example of my own, for both "narrow" and "medium" presets.

preset="narrow", nmols=10

preset="medium", nmols=10

On this line of sample_space(), chirality information of origin_smiles is removed. The output is then unsuitable as input to a chirality-aware ML model, e.g. to distinguish L vs. D amino acids which are important in models of binding affinity. Could the option to skip this sanitization step be provided to the user?

PS: Great code base and beautiful visualizations! We're finding it very useful in explaining our Gaussian Process models. The future of SAR ←→ ML looks exciting.

Are you planning to release 0.5.0 on pypi? I am maintaining the conda package of exmol and I would like to bump it to 0.5.0. See https://github.com/conda-forge/exmol-feedstock

Thanks!

For certain SMILES, run_STONED() failed to generate after running for so long. So far, one SMILES known to cause such issue is

[Na+].[Na+].[Na+].[Na+].[Na+].[O-][S](=O)(=O)OCC[S](=O)(=O)c1cccc(Nc2nc(Cl)nc(Nc3cc(cc4C=C(\C(=N/Nc5ccc6c(cccc6[S]([O-])(=O)=O)c5[S]([O-])(=O)=O)C(=O)c34)[S]([O-])(=O)=O)[S]([O-])(=O)=O)n2)c1

Here is how I use the function: exmol.run_stoned(smiles, num_samples=10, max_mutations=1).