accfg 0.0.2

Molecule functional group extraction and comparison

AccFG: Accurate Functional Group Extraction and Molecular Structure Comparison

Table of Contents

• Introduction
• Installation
• Usage
  • FG extraction
  • FG extraction visualization
  • Molecular structure comparison
  • Molecular structure comparison visualization
• Run

Introduction

AccFG is a tool for precise functional group (FG) extraction and molecular structure comparison.

Installation

We provide two methods to install AccFG:

Installation by pip (recommended)

pip install accfg

Installation from GitHub repository

To install AccFG, follow these steps:

1. Clone/download the repository:

   git clone https://github.com/xuanliugit/AccFG.git
   

2. Navigate to the project directory:

   cd AccFG
   

3. Install the required dependencies:

   conda create --name accfg python=3.10
   conda activate accfg
   pip install -r requirements.txt
   

4. Quick start:

   # Get functional groups from SMILES
   python run_accfg.py 'CN(C)/N=N/C1=C(NC=N1)C(=O)N'
   
   # Compare two molecules
   python run_accfg.py 'CNC(=O)Cc1nc(-c2ccccc2)cs1' --compare_smi 'CCNCCc1nc2ccccc2s1'
   

The FG dictionary is stored in ./accfg/fgs_common.csv and ./accfg/fgs_heterocycle.csv

Usage

FG extraction

To extract functional groups:

# example.py
from accfg import AccFG

afg = AccFG(print_load_info=True)
smi = 'CN(C)/N=N/C1=C(NC=N1)C(=O)N'

fgs,fg_graph = afg.run(smi, show_atoms=True, show_graph=True)

print_fg_tree(fg_graph, fgs.keys(), show_atom_idx=True)
'''
├──Primary amide: ((10, 12, 11),)
...
'''
print(fgs)
'''
{'Primary amide': [(10, 12, 11)], 'Triazene': [(1, 3, 4)], 'imidazole': [(5, 9, 8, 7, 6)]}
'''

User-defined FGs Example:

# example.py
from accfg import AccFG

my_fgs_dict = {'Cephem': 'O=C(O)C1=CCS[C@@H]2CC(=O)N12', 'Thioguanine': 'Nc1nc(=S)c2[nH]cnc2[nH]1'}
my_afg = AccFG(user_defined_fgs=my_fgs_dict,print_load_info=True)

cephalosporin_C = 'CC(=O)OCC1=C(N2[C@@H]([C@@H](C2=O)NC(=O)CCC[C@H](C(=O)O)N)SC1)C(=O)O'
fgs,fg_graph = my_afg.run(cephalosporin_C, show_atoms=True, show_graph=True)

print_fg_tree(fg_graph, fgs.keys(), show_atom_idx=True) # This will print the FG tree

'''
├──Primary aliphatic amine: ((21,),)
├──...
'''

To print functional groups:

print(fgs) # Show top level FGs
'''
{'Primary aliphatic amine': [(21,)],
 'Carboxylic acid': [(22, 23, 24)],
 'Carboxylic ester': [(1, 2, 3, 4)],
 'Secondary amide': [(15, 16, 14, 13)],
 'Cephem': [(8, 7, 9, 6, 5, 27, 26, 25, 13, 11, 12, 10)]}
'''

FG extraction visualization

from accfg import draw_mol_with_fgs, molimg

molimg(draw_mol_with_fgs(cephalosporin_C, afg=my_afg, img_size=(900,900)))

This will show image with FGs highlighted

Molecular structure comparison

from accfg import AccFG, compare_mols, draw_compare_mols

smi_1,smi_2 = ('CNC(=O)Cc1nc(-c2ccccc2)cs1','CCNCCc1nc2ccccc2s1')
diff = compare_mols(smi_1, smi_2)
print(diff) # This print the structure difference
'''
(([('Secondary amide', 1, [(2, 3, 1)]),
   ...
'''

draw_RascalMCES(smi_1, smi_2) # This draw the RascalMCES comparison

Molecular structure comparison visualization

img = img_grid(draw_compare_mols(smi_1, smi_2),num_columns=2)
with open('results/compare_mols.png', 'wb') as f:
    img.save(f, format='PNG')
img

Run

To run the BBBP dataset, Lipophilicity dataset, BACE dataset, and CHEMBL drugs, simply run

python run_data.py

The result is in ./molecule_data. The code to process the data is in exam_data.py

All other examples in the manuscript is in example.ipynb.