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https://github.com/the-metabolic-disassembler/metadisassembler


https://github.com/the-metabolic-disassembler/metadisassembler

bioinformatics cheminformatics chemoinformatics pip python3 rdkit

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README

          

# The Metabolic Disassembler

The Metabolic Disassembler is a Python package to automatically predict a combination of biosynthetic units in a natural product. This software would help to reveal the starting materials of the target natural product.

## Installation

Install The Metabolic Disassembler with `pip`.

```bash
$ pip install metadisassembler
```

## Requirements

- Python (3.6)
- [RDKit](https://www.rdkit.org) (version 2019.09.2.0)
- [NetworkX](https://networkx.github.io/documentation/stable/) (version 2.2)
- [CairoSVG](https://cairosvg.org) (version 2.4.2)
- [Pillow (PIL)](https://pillow.readthedocs.io/en/stable/) (version 6.2.1)
- [Pandas](https://pandas.pydata.org) (version 0.25.3)
- [Matplotlib](https://matplotlib.org) (version 3.1.2)

## Command Line Usage

```bash
% metadisassembler -h
usage: metadisassembler [-h] [-t TIME] [--hide] [-c] query

positional arguments:
query MDL_Molfile, SMILES, InChI, KEGG_COMPOUND_ID,
KNApSAcK_COMPOUND_ID

optional arguments:
-h, --help show this help message and exit
-t TIME, --time TIME set a time limit [s] [default: 300]
--hide hide stereochemistry [default: False]
-c, --color output color allocation information [default: False]
```

## Basic Usage
A usage example by Jupyter Notebook can be seen [here](https://github.com/the-metabolic-disassembler/metadisassembler/blob/master/jupyter_usecase/basic_usage.ipynb).

## Google Colab

You can try the process from installation to the basic usage in Google Colab. [![colab-logo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/the-metabolic-disassembler/metadisassembler/blob/master/jupyter_usecase/basic_usage_in_colab.ipynb)

## Citation
Amano K, Matsumoto T, Tanaka K, Funatsu K, Kotera M. Metabolic disassembler for understanding and predicting the biosynthetic units of natural products. *BMC Bioinformatics* **20**, 728 (2019) [doi:10.1186/s12859-019-3183-9](https://doi.org/10.1186/s12859-019-3183-9)