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connorcoley Messier usable_model script with extra stuff for figures
- Contains similarity map towards end
- Not clean, but shows examples
Latest commit 0a272f0 Nov 10, 2017
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rdchiral Initial commit Jun 29, 2017
retrosim Messier usable_model script with extra stuff for figures Nov 10, 2017
.gitignore Initial commit Jun 29, 2017
LICENSE Initial commit Jun 29, 2017 Update Nov 10, 2017



This repository contains the data and code needed to test a similarity-based approach to one-step retrosynthesis.

Please note that rdchiral is a work-in-progress. The current version as of June 19, 2017 has been copied into this repository for result reproducibility. An up-to-date version can be found at the public repo


The set of 50k reactions comes from Each reaction is pre-labeled with a class number (1-10). The dataset is further cleaned following Liu et al. (2017) ( so that each reaction has a single product and trivial products are excluded. Atom maps are removed for reactant atoms that do not contribute atoms to the product of interest. data_processed.csv is a Pandas dataframe and is meant to work with the functions in


All of the "heavy lifting" occurs inside the scripts folder. extract_templates is just used for examining the templates corresponding to the training data. Likewise, analyze_templates looks at the some trends and the most common templates, but is not needed in the workflow.

After an initial data processing using proc_data, the test_similarity script actually applies the similarity method using the training data as a corpus. The Jupyter notebook is meant to look at a single condition (i.e., class, fingerprint type, similarity metric) at a time. The standalone script can test the whole suite of conditions. Results are written into results.txt and are saved in separate files.

The notebook process_results reads from results.txt and examines the validation performance visually. This is how the metric was selected for use on the test data, which required a simple modification of the test_similarity script. Test results are also read using process_results and output in a tabular form at the end of the notebook.


For any questions, feel free to email

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