PyMolinfo 1.8.4

PyMolinfo provides comprehensive molecular information and analysis.

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 above for a quick start.

Binder

Test this package by launching our example notebooks on Binder:

| Description | Launch Binder |

| --- | --- |

| Load a sdf file | |

| Visualize a compound | |

| Check and count functional groups | |

| Create custom functional groups | |

Google Colab

You can use the following code to run PyMolinfo in Google Colab:

| Version | Google Colab |

| --- | --- |

| 1.6.0 | |

| < 1.6.0 | |

Installation

Install molinfo with pip

  pip install PyMolinfo

Documentation

Import package as:

import pyMolinfo as mi

# check version

print(mi.__version__)

Examples

• Create a graph

# 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:

# visualize compound by sdf file

mi.g3d(sdf_file)

• Check the availability of functional groups:

# check functional groups

res, comp1 = mi.check_functional_group(sdf_file, res_format='dataframe')

print(res)

• Calculate angle/distance between atoms

# 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)

• Create custom functional groups:

[atom1-element][atom1-number][bond-type][atom2-element][atom2-number]

| Bond Types | Format |

|:----------|:----------|

| single bond CC | C1-C2 |

| double bond CC | C1=C2 |

| triple bond CC | C1#C2 |

How to create a custom functional group?

| Name | Symbol | Format |

|:-----------|:------------:|-------------:|

| cyanide-1 | CCN | ["N1#C2"] |

| custom_fg | NCH | ["N1-C2", "C2-H3"] |

| NC=O | NC=O | ["N1-C2", "C2=O3"] |

And coded as:

# C1-C2#N3

custom_functional_group = [

    {'cyanide': ["C1-C2", "C2#N3"]},

]



# define different custom functional groups as:

# N#C

# NCH

# NCO

custom_functional_group = [

    {'N#C': ["N1#C2"]},

    {'custom_fg': ["N1-C2", "C2-H3"]},

    {'NC=O': ["N1-C2", "C2=O3"]},

]



# 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

Authors

@sinagilassi