molSim 0.0.1

Python command line and GUI tool to analyze molecular similarity.

molSim README

molSim is a tool for visualizing diversity in your molecular data-set using graph theory.

Documentation

View our Online Documentation

Purpose

Why Do We Need To Visualize Molecular Similarity / Diversity?

There are two broad contexts where it is helpful to visualize the diversity of a molecular dataset:

Experimental Synthesis

For a chemist, synthesizing new molecules with targeted properties is often a laborious and time consuming task. In such a case, it becomes useful to check the similarity of a newly proposed (un-synthesized) molecule to the ones already synthesized. If the proposed molecule is too similar to the existing repertoire of molecules, it will probably not yield not enough new information / property and thus need not be synthesized. On the other hand, if the aim is to replicate the properties of a high performing molecule, it is useful to ensure that each new proposed molecule is similar to the high performing one. In both cases, a chemist can avoid spending time and effort synthesizing molecules not useful for the project.

Machine Learning Molecular Properties

In the context of machine learning, visualizing the diversity of the training set gives a good idea about its information quality. A more diverse training data-set yields a more robust model, which generalizes well to unseen data. Additionally, such a visualization can identify "clusters of similarity" indicating the need for separately trained models for each cluster.

Substrate Scope Robustness Verification

When proposing a novel reaction it is essential for the practicing chemist to evaluate the transformation's tolerance of diverse functional groups and substrates (Glorius, 2013). Using molSim, one can evaluate the structural and chemical similarity across an entire susbtrate scope to ensure that it avoids redundant species. Below is an example similarity heatmap generated to visualize the diversity of a three-component sulfonamide coupling reaction with a substantial number of substrates (Chen, 2018).

Many of the substrates appear similar to one another and thereby redundant, but in reality the core sulfone moiety and the use of the same coupling partner when evaluating functional group tolerance accounts for this apparent shortcoming. Also of note is the region of high similarity along the diagonal where the substrates often differ by a single halide heteratom or substitution pattern.

Installing molSim

Conda

Use the following command with conda to create an environment: conda create --name your-env-name --file spec-file.txt

1. Python 3+
2. Matplotlib
3. Numpy
4. RDKIT
5. SEABORN
6. PyYAML
7. Pandas 1.0.1+
8. openpyxl

Pip

Required dependency RDKit is only available through conda. To install using pip, first run conda install -c rdkit rdkit to install it. To then install molSim using pip, run the following command: pip install molSim

Running molSim

Start molSim with a graphical user interface:

molSim

Example Run:

molSim config.yaml

Using multiprocessing:

molSim includes support for multiprocessing to split up the work of molecular comparisons across multiple CPU cores, speeding up execution. Because there is a cost associated with creating and destroying these processes, setting n_workers to any number larger than 1 is not reccomended for datasets smaller than ~5000 molecules.

Tests:

python -m unittest discover

Note: Multiprocessing speedup and efficiency tests take more than 30 minutes to execute. To run all other tests and ignore these, create a file called .no-speedup-test in the molSim directory and execute the above command as shown.

To build the docs, execute the following with sphinx and m2r installed and from the /docs directory:

m2r ../README.md | mv ../README.rst . | sphinx-apidoc -f -o . .. | make html | cp _build/html/* .

For packaging on Pypi:

python -m build; twine upload dist/*

For packaging on conda:

conda skeleton pypi package; conda build package

Notes

General Workflow

Molecular Structure Information (SMILES strings, *.pdb files etc.) --> Generate a Molecular Graph / Environment Fingerprint --> Calculate a "similarity score" between moelcules based on some distance between their fingerprints.

Currently Implemented Fingerprints

1. Morgan Fingerprint (Equivalent to the ECFP-6)
2. RDKIT Topological Fingerprint
3. All descriptors available through the Mordred library (only available through command-line. In fingerprint_type, specify 'mordred:desciptorname'.).

Currently Implemented Similarity Scores

1. Tanomito Similarity (0 for completely dissimilar and 1 for identical molecules)
2. Negative L0, L1 and L2 norms
3. Cosine Similarity

Currently Implemented Functionalities

1. compare_target_molecule: Compare a proposed molecules to existing molecular database. The outputs are a similarity density plot and/ or the least similar and most similar molecules in the database (to the proposed molecule)

2. visualize_dataset: Visualize the diversity of molecules in existing database. The outputs are a heatmap of similarity scores and/or a density plot of similarity scores and /or a parity plot showing some molecular property (e.g. boiling point) between pairs of most similar molecules. The last output requires the input of the molecular property for each molecule. This can be inputted as a .txt file containing rows of name property pairs. An example of such a file with fictitious properties is provided in the file smiles_responses.txt. This option is typically used to check the suitability of the fingerprint / similarity measure for a property of interest. If they do a good job for the particular property then the parity plot should be scattered around the diagonal.

3. identify_outliers: Using an isolation forest, check for which molecules are potentially novel or are outliers according to the selected descriptor. Output can be directly to the command line by specifiying otuput to be terminal or to a text file by instead providing a filename.

Credits and Licensing

Developer: Himaghna Bhattacharjee, Vlachos Research Lab. (www.linkedin.com/in/himaghna-bhattacharjee)

Developer: Jackson Burns, Don Watson Lab. (Personal Site)

License

MIT Open

Works Cited

Collins, K., Glorius, F. A robustness screen for the rapid assessment of chemical reactions. Nature Chem 5, 597–601 (2013). https://doi.org/10.1038/nchem.1669

Yiding Chen, Philip R. D. Murray, Alyn T. Davies, and Michael C. Willis Journal of the American Chemical Society 2018 140 (28), 8781-8787 DOI: 10.1021/jacs.8b04532