Python command line and GUI tool to analyze molecular similarity.
Project description
molSim README
molSim is a tool for visualizing diversity in your molecular data-set using graph theory.
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
- Python 3+
- Matplotlib
- Numpy
- RDKIT
- SEABORN
- PyYAML
- Pandas 1.0.1+
- 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/* .
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
- Morgan Fingerprint (Equivalent to the ECFP-6)
- RDKIT Topological Fingerprint
- All descriptors available through the Mordred library (only available through command-line. In
fingerprint_type
, specify 'mordred:desciptorname'.).
Currently Implemented Similarity Scores
- Tanomito Similarity (0 for completely dissimilar and 1 for identical molecules)
- Negative L0, L1 and L2 norms
- Cosine Similarity
Currently Implemented Functionalities
-
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)
-
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.
-
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 beterminal
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
Project details
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