# SMiPoly
## 1. What is SMiPoly?
"SMiPoly (**S**mall **M**olecules **i**nto **Poly**mers)" is rule-based virtual library generator for discovery of functional polymers. It is consist of two submodules, "monc.py" and "polg.py".
"monc.py" is a monomer classifier from a list of small molecules, and "polg.py" is a polymer repeating unit generator from the classified monomer list.
## 2. Current version and requirements
current version = 0.1.0
requirements
- pyhon 3.7, 3.8, 3.9, 3.10, 3.11, 3.12
- rdkit >= 2020.09.1.0 #(2019.09.3 is unavailable)
- numpy >= 1.20.2
- pandas >= 1.2.4
## 3. Installation and usage
### 3-1. Installatin
SMiPoly can be installed with pip or conda.
### 3-1-1. Install with pip
Create new virtual environment and activate it.
To install this package, run as follows.
```sh
$pip install smipoly
```
### 3-1-2. Install with conda
Add the channel "conda-forge" if it have not been enable.
```sh
$conda config --add channels conda-forge
```
Create a new environment.
```sh
$conda create -n "YOUR_NEW_ENVIRONMNT_NAME" python
or
$conda create -n "YOUR_NEW_ENVIRONMNT_NAME" python="required version (ex. 3.10)"
```
Then activate it.
```sh
$conda activate "YOUR_NEW_ENVIRONMNT_NAME"
```
And install SMiPoly.
```sh
$conda install smipoly
```
Or after create and activate a new environment,
```sh
$conda install -c conda-forge smipoly
```
### 3-2. Quick start
Download 'sample_data/202207_smip_monset.csv' and 'sample_script/sample_smip_demo.ipynb' from [SMiPoly repository](https://github.com/PEJpOhno/SMiPoly) to the same directry on your computer.
Then run sample_smip_demo.ipynb. To run this demo script, Jupyter Notebook is required.
## 4. Module contents
### 4-1. monc.py
The functions of monc.py is as follows.
- extract monomers from a list of small molecules.
- classify extracted monomers into each monomer class.
The chemical structure of the small molecule compounds should be expressed in simplified molecular input line entry system (SMILES) and given as pandas DataFrame.
**Functions**
smip.**monc.moncls**(*df, smiColn, minFG = 2, maxFG = 4, dsp_rsl=False*)
smip.**monc.olecls**(*df, smiColn, minFG = 1, maxFG = 4, dsp_rsl=False*)
ARGUMENTS:
- df: name of the object DataFrame
- smicoln: The column label of the SMILES column, given as a *str*.
- minFG: minimum number of the polymerizable functional groups in the monomer for successive polymerization (default for moncls, 2: 2 or more; for olecls, 1: 1 or more)
- maxFG: maxmum nimber of the polymerizable functional groups in the monomer for successive polymerization (default 4: 4 or less)
- dsp_rsl: display classified result (default False)
**Defined monomer class**
By the function "moncls"
- vinylidene
- cyclic olefin
- epoxide and diepoxide
- lactone
- lactam
- hydroxy carboxylic acid
- amino acid
- cyclic carboxylic acid anhydride and bis(cyclic carboxylic acid anhydride)
- hindered phenol
- dicarboxylic acid and acid halide
- diol
- diamine and primary diamine
- diisocyanate
- bis(halo aryl)sulfone
- bis(fluoro aryl)ketone
By the function "olecls"
(The following class of compounds are also belong to the class "vinylidene" and / or "cyclic olefin".)
- acryl
- styryl
- allyl
- conjugated dienes
- vinyl ether
- vinyl ester
- maleic imide derivatives
### 4-2. polg.py
"polg.py" gives all synthesizable polymer repeating units starting from the classified monomer list generated by "monc.py".
For chain polymerization (polyolefins and some polyether), it gives homo and binary-copolymers. For successive (or step) polymerization, it gives homopolymer only.
**Function**
smip.**polg.biplym**(*df, targ = \['all'\], Pmode = 'a', dsp_rsl=False*)
ARGUMENTS:
- df: name of the DataFrame of classified monomers generated by *monm.moncls*.
- targ: targetted polymer class. When present, it can be a list of *str*. The selectable elements are 'polyolefin', 'polyester', 'polyether', 'polyamide', 'polyimide', 'polyurethane', 'polyoxazolidone' and 'all' (default = ['all'])
- Pmod: generate all isomers of the polymer repeating unit ('a') or the polymer repeating unit of its representation ('r'). (default = 'a')
- dsp_rsl: display the DataFrame of the generated polymers. (default False)
**Defined polymer class**
- polyolefin, polycyclic olefin and their binary copolymers
- polyester (from lactone, hydroxy carboxylic acid, dicarboxylic acid + diol, diol + CO and cyclic carboxylic acid anhydride + epoxide)
- polyether (from epoxide, hindered phenol, bis(halo aryl)sulfone + diol and bis(fluoro aryl)ketone + diol)
- polyamide (from lactam, amino acid and dicarboxylic acid + diamine)
- polyimide (bis(cyclic carboxylic acid anhydride + primary diamine)
- polyurethane (diisocyanate + diol)
- polyoxazolidone (diepoxide + diisocyanate)
### 4-3 Sample data
The sample dataset './sample_data/202207_smip_monset.csv' includes common 1,083 monomers collected from published documents such as scientific articles, catalogues and so on.
### 4-4. Utilities
By using the files in './utilities' directory, one can modify or add the definition of monomers, the rules of polymerization reactions and polymer classes.
To apply the new rule(s), replace the old './smipoly/rules' directory by the new one. The files must be run according to the number assigned the head of the each filename.
- 1_MonomerDefiner.ipynb: definitions of monomers
- 2_Ps_rxnL.ipynb: rules of polymerization reactions
- 3_Ps_GenL.ipynb: definitions of polymer classes with combinations of starting monomer(s) and polymerization reaction
## 5. Copyright and license
Copyright (c) 2022 Mitsuru Ohno
Released under the BSD-3 license, license that can be found in the LICENSE file.
## 6. Publications
SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions
Mitsuru Ohno, Yoshihiro Hayashi, Qi Zhang, Yu Kaneko, and Ryo Yoshida
*Journal of Chemical Information and Modeling* **2023** *63* (17), 5539-5548
DOI: 10.1021/acs.jcim.3c00329
https://doi.org/10.1021/acs.jcim.3c00329
(version 0.0.1 was used)
## 7. Related projects
RadonPy (Fully automated calculation for a comprehensive set of polymer properties)
https://github.com/RadonPy/RadonPy
## 8. Directry configuration
```sh
SMiPoly
├── src
│ └── smipoly
│ ├── __init__.py
│ ├── _version.py
│ ├── smip
│ │ ├── __init__.py
│ │ ├── funclib.py
│ │ ├── monc.py
│ │ └── polg.py
│ └── rules
│ ├── excl_lst.json
│ ├── mon_dic_inv.json
│ ├── mon_dic.json
│ ├── mon_lst.json
│ ├── mon_vals.json
│ ├── ps_class.json
│ ├── ps_gen.pkl
│ └── ps.rxn.pkl
├── LICENSE
├── pyproject.toml
├── setup.py
├── setup.cfg
├── README.md
├── sample_data
│ └── 202207_smip_monset.csv
├── sample_script
│ └── sample_smip_demo.ipynb
└── utilities
├── 1_MonomerDefiner.ipynb
├── 2_Ps_rxnL.ipynb
├── 3_Ps_GenL.ipynb
└── rules/
```
## Reference
https://future-chem.com/rdkit-chemical-rxn/
https://www.daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html
https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html
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"description": "# SMiPoly\r\n\r\n\r\n\r\n## 1. What is SMiPoly? \r\n\"SMiPoly (**S**mall **M**olecules **i**nto **Poly**mers)\" is rule-based virtual library generator for discovery of functional polymers. It is consist of two submodules, \"monc.py\" and \"polg.py\". \r\n\"monc.py\" is a monomer classifier from a list of small molecules, and \"polg.py\" is a polymer repeating unit generator from the classified monomer list. \r\n\r\n## 2. Current version and requirements\r\ncurrent version = 0.1.0 \r\nrequirements\r\n - pyhon 3.7, 3.8, 3.9, 3.10, 3.11, 3.12 \r\n - rdkit >= 2020.09.1.0 #(2019.09.3 is unavailable) \r\n - numpy >= 1.20.2 \r\n - pandas >= 1.2.4 \r\n\r\n## 3. Installation and usage\r\n### 3-1. Installatin \r\nSMiPoly can be installed with pip or conda. \r\n### 3-1-1. Install with pip \r\nCreate new virtual environment and activate it.\r\nTo install this package, run as follows.\r\n\r\n```sh\r\n$pip install smipoly\r\n```\r\n### 3-1-2. Install with conda \r\n\r\nAdd the channel \"conda-forge\" if it have not been enable. \r\n\r\n```sh\r\n$conda config --add channels conda-forge\r\n```\r\n\r\nCreate a new environment. \r\n```sh\r\n$conda create -n \"YOUR_NEW_ENVIRONMNT_NAME\" python \r\nor \r\n$conda create -n \"YOUR_NEW_ENVIRONMNT_NAME\" python=\"required version (ex. 3.10)\"\r\n```\r\nThen activate it. \r\n```sh\r\n$conda activate \"YOUR_NEW_ENVIRONMNT_NAME\"\r\n```\r\nAnd install SMiPoly. \r\n```sh\r\n$conda install smipoly\r\n```\r\n\r\nOr after create and activate a new environment, \r\n```sh\r\n$conda install -c conda-forge smipoly\r\n```\r\n\r\n### 3-2. Quick start\r\nDownload 'sample_data/202207_smip_monset.csv' and 'sample_script/sample_smip_demo.ipynb' from [SMiPoly repository](https://github.com/PEJpOhno/SMiPoly) to the same directry on your computer.\r\nThen run sample_smip_demo.ipynb. To run this demo script, Jupyter Notebook is required.\r\n\r\n## 4. Module contents \r\n### 4-1. monc.py \r\nThe functions of monc.py is as follows. \r\n - extract monomers from a list of small molecules.\r\n - classify extracted monomers into each monomer class.\r\n\r\nThe chemical structure of the small molecule compounds should be expressed in simplified molecular input line entry system (SMILES) and given as pandas DataFrame. \r\n\r\n**Functions** \r\nsmip.**monc.moncls**(*df, smiColn, minFG = 2, maxFG = 4, dsp_rsl=False*) \r\nsmip.**monc.olecls**(*df, smiColn, minFG = 1, maxFG = 4, dsp_rsl=False*) \r\n\r\nARGUMENTS: \r\n\r\n - df: name of the object DataFrame \r\n - smicoln: The column label of the SMILES column, given as a *str*. \r\n - minFG: minimum number of the polymerizable functional groups in the monomer for successive polymerization (default for moncls, 2: 2 or more; for olecls, 1: 1 or more) \r\n - maxFG: maxmum nimber of the polymerizable functional groups in the monomer for successive polymerization (default 4: 4 or less) \r\n - dsp_rsl: display classified result (default False) \r\n\r\n**Defined monomer class** \r\nBy the function \"moncls\" \r\n - vinylidene \r\n - cyclic olefin \r\n - epoxide and diepoxide \r\n - lactone \r\n - lactam \r\n - hydroxy carboxylic acid \r\n - amino acid \r\n - cyclic carboxylic acid anhydride and bis(cyclic carboxylic acid anhydride) \r\n - hindered phenol \r\n - dicarboxylic acid and acid halide \r\n - diol \r\n - diamine and primary diamine \r\n - diisocyanate \r\n - bis(halo aryl)sulfone \r\n - bis(fluoro aryl)ketone \r\n\r\nBy the function \"olecls\" \r\n(The following class of compounds are also belong to the class \"vinylidene\" and / or \"cyclic olefin\".) \r\n - acryl \r\n - styryl \r\n - allyl \r\n - conjugated dienes \r\n - vinyl ether \r\n - vinyl ester \r\n - maleic imide derivatives \r\n\r\n### 4-2. polg.py \r\n\"polg.py\" gives all synthesizable polymer repeating units starting from the classified monomer list generated by \"monc.py\". \r\nFor chain polymerization (polyolefins and some polyether), it gives homo and binary-copolymers. For successive (or step) polymerization, it gives homopolymer only.\r\n\r\n**Function** \r\nsmip.**polg.biplym**(*df, targ = \\['all'\\], Pmode = 'a', dsp_rsl=False*) \r\n\r\nARGUMENTS: \r\n\r\n - df: name of the DataFrame of classified monomers generated by *monm.moncls*. \r\n - targ: targetted polymer class. When present, it can be a list of *str*. The selectable elements are 'polyolefin', 'polyester', 'polyether', 'polyamide', 'polyimide', 'polyurethane', 'polyoxazolidone' and 'all' (default = ['all']) \r\n - Pmod: generate all isomers of the polymer repeating unit ('a') or the polymer repeating unit of its representation ('r'). (default = 'a') \r\n - dsp_rsl: display the DataFrame of the generated polymers. (default False) \r\n\r\n**Defined polymer class** \r\n - polyolefin, polycyclic olefin and their binary copolymers \r\n - polyester (from lactone, hydroxy carboxylic acid, dicarboxylic acid + diol, diol + CO and cyclic carboxylic acid anhydride + epoxide) \r\n - polyether (from epoxide, hindered phenol, bis(halo aryl)sulfone + diol and bis(fluoro aryl)ketone + diol) \r\n - polyamide (from lactam, amino acid and dicarboxylic acid + diamine) \r\n - polyimide (bis(cyclic carboxylic acid anhydride + primary diamine) \r\n - polyurethane (diisocyanate + diol) \r\n - polyoxazolidone (diepoxide + diisocyanate) \r\n\r\n### 4-3 Sample data\r\nThe sample dataset './sample_data/202207_smip_monset.csv' includes common 1,083 monomers collected from published documents such as scientific articles, catalogues and so on.\r\n\r\n### 4-4. Utilities \r\nBy using the files in './utilities' directory, one can modify or add the definition of monomers, the rules of polymerization reactions and polymer classes. \r\nTo apply the new rule(s), replace the old './smipoly/rules' directory by the new one. The files must be run according to the number assigned the head of the each filename. \r\n\r\n - 1_MonomerDefiner.ipynb: definitions of monomers \r\n - 2_Ps_rxnL.ipynb: rules of polymerization reactions \r\n - 3_Ps_GenL.ipynb: definitions of polymer classes with combinations of starting monomer(s) and polymerization reaction \r\n\r\n## 5. Copyright and license \r\nCopyright (c) 2022 Mitsuru Ohno \r\nReleased under the BSD-3 license, license that can be found in the LICENSE file. \r\n\r\n\r\n## 6. Publications \r\nSMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions \r\nMitsuru Ohno, Yoshihiro Hayashi, Qi Zhang, Yu Kaneko, and Ryo Yoshida \r\n*Journal of Chemical Information and Modeling* **2023** *63* (17), 5539-5548 \r\nDOI: 10.1021/acs.jcim.3c00329 \r\nhttps://doi.org/10.1021/acs.jcim.3c00329 \r\n(version 0.0.1 was used) \r\n\r\n## 7. Related projects \r\nRadonPy (Fully automated calculation for a comprehensive set of polymer properties) \r\nhttps://github.com/RadonPy/RadonPy \r\n\r\n## 8. Directry configuration \r\n\r\n```sh\r\nSMiPoly\r\n\u251c\u2500\u2500 src\r\n\u2502 \u2514\u2500\u2500 smipoly\r\n\u2502 \u251c\u2500\u2500 __init__.py\r\n\u2502 \u251c\u2500\u2500 _version.py\r\n\u2502 \u251c\u2500\u2500 smip\r\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\r\n\u2502 \u2502 \u251c\u2500\u2500 funclib.py\r\n\u2502 \u2502 \u251c\u2500\u2500 monc.py\r\n\u2502 \u2502 \u2514\u2500\u2500 polg.py\r\n\u2502 \u2514\u2500\u2500 rules\r\n\u2502 \u251c\u2500\u2500 excl_lst.json\r\n\u2502 \u251c\u2500\u2500 mon_dic_inv.json\r\n\u2502 \u251c\u2500\u2500 mon_dic.json\r\n\u2502 \u251c\u2500\u2500 mon_lst.json\r\n\u2502 \u251c\u2500\u2500 mon_vals.json\r\n\u2502 \u251c\u2500\u2500 ps_class.json\r\n\u2502 \u251c\u2500\u2500 ps_gen.pkl\r\n\u2502 \u2514\u2500\u2500 ps.rxn.pkl\r\n\u251c\u2500\u2500 LICENSE\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u251c\u2500\u2500 setup.py\r\n\u251c\u2500\u2500 setup.cfg\r\n\u251c\u2500\u2500 README.md\r\n\u251c\u2500\u2500 sample_data\r\n\u2502 \u2514\u2500\u2500 202207_smip_monset.csv\r\n\u251c\u2500\u2500 sample_script\r\n\u2502 \u2514\u2500\u2500 sample_smip_demo.ipynb\r\n\u2514\u2500\u2500 utilities\r\n \u251c\u2500\u2500 1_MonomerDefiner.ipynb\r\n \u251c\u2500\u2500 2_Ps_rxnL.ipynb\r\n \u251c\u2500\u2500 3_Ps_GenL.ipynb\r\n \u2514\u2500\u2500 rules/\r\n```\r\n\r\n## Reference \r\nhttps://future-chem.com/rdkit-chemical-rxn/ \r\nhttps://www.daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html \r\nhttps://www.daylight.com/dayhtml/doc/theory/theory.smarts.html \r\n",
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