GitHub - xinyue123-q/Python-based-polymer-unit-recognition-script-PURS-: PURS provides the following functions: 1. Polymer-units in a series of SMILES are identified. 2. Establish a database based on polymer-units of input data. 3. Classify the polymer-Unit in the database and provide the structure file of polymer-Unit. 4. Generate polymer-unit molecular fingerprints that can be used for machine learning eigenvalues.

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Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
LICENSE		LICENSE
README.txt		README.txt
get-polymer-unit.py		get-polymer-unit.py
polymer-unit-classify.py		polymer-unit-classify.py
structure_identity_tool.py		structure_identity_tool.py
test.csv		test.csv

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1.Get polymer-unit.py, polymer-unit-classify. py, structure-identity-tool.py and the input file (test.csv) are placed in the same folder.
2.Run get-polymer-unit. py to get the output file as follows (python get-polymer-unit.py):
 1）ring_total_list.csv ：Polymer-units identified from test.csv.
 2）one_hot.csv：One-hot fingerprint for the input file generated by ring_total_list.
 3）number.csv：PUFp for input files generated by ring_total_list (see paper for details).
 4）adjacent_matrix.csv：Adjacency matrix.
 5）node_matrix.csv：Node matrix.
 6）index_data.csv：Polymer-units number for each data.
3.The output file obtained by running polymer-unit-classify. py is as follows（python polymer-unit-classify.py）:
 1）ring_df.csv ：Record the classification label of each polymer-units.
 2）type_frame.csv：Record the polymer-units type contained in each data.

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PURS provides the following functions: 1. Polymer-units in a series of SMILES are identified. 2. Establish a database based on polymer-units of input data. 3. Classify the polymer-Unit in the database and provide the structure file of polymer-Unit. 4. Generate polymer-unit molecular fingerprints that can be used for machine learning eigenvalues.

Readme

MIT license