https://github.com/sinagilassi/pymolinfo
Advanced molecular analysis by converting molecular structures into graph representations.
https://github.com/sinagilassi/pymolinfo
chemistry graph-networks molecular-graph molecular-visualization pymolinfo
Last synced: 10 months ago
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Advanced molecular analysis by converting molecular structures into graph representations.
- Host: GitHub
- URL: https://github.com/sinagilassi/pymolinfo
- Owner: sinagilassi
- License: mit
- Created: 2024-08-13T23:18:08.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-08T02:50:52.000Z (about 1 year ago)
- Last Synced: 2025-05-16T20:08:48.632Z (about 1 year ago)
- Topics: chemistry, graph-networks, molecular-graph, molecular-visualization, pymolinfo
- Language: Jupyter Notebook
- Homepage: https://molinfo.readthedocs.io/en/latest/
- Size: 652 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🌟 PyMolinfo
   
**PyMolInfo** (previously molinfo) is a Python package designed for advanced molecular analysis by converting molecular structures into graph representations. This package enables researchers and chemists to load various molecular file formats, transform them into graphs, and extract valuable information through graph-based methods.
## ✨ Features
* `File Format Support`: Load molecular data from multiple file formats, including SDF and JSON (soon).
* `Graph Conversion`: Transform molecular structures into graph representations for detailed analysis.
* `Functional Group Identification`: Detect and analyze functional groups within the molecular graph.
* `Distance Measurement`: Compute distances between atoms and bonds in the molecular graph.
* `Bond Angle Calculation`: Measure angles between bonds using graph-based methods.
## 🚀 Getting Started
To use PyMolinfo, simply install the package and import it into your Python script. Refer to the example code snippets for a quick start.
## 📚 Binder
Test this package by launching our example notebooks on Binder:
- **Load a sdf file**: [](https://mybinder.org/v2/gh/sinagilassi/Molinfo/cb4f3c4b58501786da3dc5a2413a67720f01d579?urlpath=lab%2Ftree%2Fnotebook%2Fdoc-1.ipynb)
- **Visualize a compound**: [](https://mybinder.org/v2/gh/sinagilassi/Molinfo/cb4f3c4b58501786da3dc5a2413a67720f01d579?urlpath=lab%2Ftree%2Fnotebook%2Fdoc-2.ipynb)
- **Check and count functional groups**: [](https://mybinder.org/v2/gh/sinagilassi/Molinfo/cb4f3c4b58501786da3dc5a2413a67720f01d579?urlpath=lab%2Ftree%2Fnotebook%2Fdoc-3.ipynb)
- **Create custom functional groups**: [](https://mybinder.org/v2/gh/sinagilassi/Molinfo/cb4f3c4b58501786da3dc5a2413a67720f01d579?urlpath=lab%2Ftree%2Fnotebook%2Fdoc-4.ipynb)
## 🌐 Google Colab
You can use the following code to run `PyMolinfo` in Google Colab:
- **Version 1.6.0**: [](https://colab.research.google.com/drive/1-mkVbXbznEJGeKWdQKtJT8xkWb2Bcvw_?usp=sharing)
- **Version < 1.6.0**: [](https://colab.research.google.com/drive/1rQXg92p_jxviVfKJFf_-1qQwmOgrMLUD?usp=sharing)
## 🛠️ Installation
Install molinfo with pip
```python
pip install PyMolinfo
```
## 📖 Documentation
For detailed documentation, please visit [molinfo.readthedocs.io/en/latest/](https://molinfo.readthedocs.io/en/latest/).
## 💡 Examples
Import package as:
```python
import pyMolinfo as mi
# check version
print(mi.__version__)
```
* Create a compound
```python
# sdf file
comp1 = mi.compound(sdf_file)
# sdf string
comp1 = mi.compound(sdf_string)
# compound by cid
comp1 = mi.compound_by_cid(241)
# compound by inchi
comp1 = mi.compound_by_inchi('InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H')
# log
# print(comp1)
# pp(comp1.atom_bond_block)
# print("-"*100)
# pp(comp1.atom_bond_block_1d)
# print("-"*100)
# pp(comp1.atom_xyz)
# NOTE: functional groups
print(comp1.functional_groups)
```
* Create a graph
➡️ Create a graph from a `compound`:
```python
# NOTE: create graph
graph_comp1 = comp1.create_graph()
print(graph_comp1)
```
➡️ Create a graph from a `sdf file`:
```python
# sdf file
sdf_file_name_1 = 'test\Structure2D_COMPOUND_CID_261.sdf'
sdf_file = os.path.join(os.getcwd(), sdf_file_name_1)
# create graph
res = mi.create_graph(sdf_file)
print(type(res))
print(res)
```
* Display a graph:
```python
# plot_mode: Literal['plotly', 'matplotlib'] = 'plotly
# view graph
mi.view_graph(graph_1)
```
* Display a compound:
```python
# visualize compound by sdf file
mi.g3d(sdf_file)
```
🖼️ **1-Naphthaleneacetic acid**
```python
# visualize compound by sdf file
mi.g3d(sdf_file, display_bond_length=True)
```
🖼️ **1-Naphthaleneacetic acid**
* Check the availability of functional groups:
```python
# check functional groups
res, comp1 = mi.check_functional_group(sdf_file, res_format='dataframe')
print(res)
```
* Calculate angle/distance between atoms
```python
# distance matrix
res_distance = comp1.distance_matrix(dataframe=True)
print(res_distance)
# distance between two atoms
distance = comp1.distance_atoms(['O1', 'C2'])
print(distance)
# angle between atoms
angle = comp1.angle_atoms(['O1', 'C2', 'H3'])
print(angle)
# dihedral angle
dihedral = comp1.d_angle_atoms(['H6', 'O1', 'C2', 'H3'])
print(dihedral)
```
## Creating Custom Functional Groups
To create custom functional groups, you need to define the bonds between atoms using the following format:
`[atom1-element][atom1-number][bond-type][atom2-element][atom2-number]`
Here are the formats for different bond types:
- **Single Bond**: Represented as `C1-C2` where `C1` and `C2` are the atoms connected by a single bond.
- **Double Bond**: Represented as `C1=C2` where `C1` and `C2` are the atoms connected by a double bond.
- **Triple Bond**: Represented as `C1#C2` where `C1` and `C2` are the atoms connected by a triple bond.
### Examples
1. **Cyanide Group**: A cyanide group can be represented as `N1#C2`.
```python
custom_functional_group = [
{'cyanide': ["N1#C2"]},
]
```
2. **Custom Functional Group**: A custom functional group with a single and a double bond can be represented as `N1-C2` and `C2=O3`.
```python
custom_functional_group = [
{'custom_fg': ["N1-C2", "C2=O3"]},
]
```
3. **Multiple Functional Groups**: You can define multiple functional groups in a list.
```python
custom_functional_group = [
{'N#C': ["N1#C2"]},
{'custom_fg': ["N1-C2", "C2-H3"]},
{'NC=O': ["N1-C2", "C2=O3"]},
]
```
Once you have defined your custom functional groups, you can create and visualize them as follows:
```python
# create custom graph
custom_g = mi.create_custom_functional_groups(custom_functional_group)
# visualize custom graph
# custom_g.d("cyanide")
# find custom functional groups in a compound
res = mi.check_functional_group(
sdf_file, functional_groups=[custom_g])
print(res)
```
## ❓ FAQ
For any question, contact me on [LinkedIn](https://www.linkedin.com/in/sina-gilassi/)
## 👥 Authors
[@sinagilassi](https://www.github.com/sinagilassi)