Drkcore

10 09 2023 chemoinformatics Tweet

Visualizing Fukui Function with RDKit

The Utility of the HSAB Principle via the Fukui Function in Biological Systems was interesting stuff, so I tried to visualize the Fukui function.

from fffp import *
from rdkit.Chem import AllChem, Draw
from IPython.display import Image, display
from rdkit.Chem.Draw import SimilarityMaps
Rivaroxaban = "C1COCC(=O)N1C2=CC=C(C=C2)N3CC(OC3=O)CNC(=O)C4=CC=C(S4)Cl"
m = rdkit_optimize(Rivaroxaban)
fp = f_plus(m, basis_sets="6-31G")
fg_fp = SimilarityMaps.GetSimilarityMapFromWeights(m, fp, colorMap='RdBu')
fm = f_minus(m, basis_sets="6-31G")
fg_fm = SimilarityMaps.GetSimilarityMapFromWeights(m, fm, colorMap='RdBu')

fffp is a library that handles Fukui functions in GAMESS, but it is not yet publicly available.

The area around the S1 pocket of Rivaroxaban(Fig. 1) is more susceptible to nucleophilic reactions/binding, while the area around the S4 pocket with the morpholine group(Fig. 2) seems to be more compatible with electrophilic reactions.

Fplus Fig. 1 : F+(r) of Rivaroxaban

Fminus Fig. 2: F-(r) of Rivaroxaban

About

  • もう5年目(wishlistありマス♡)
  • 最近はPythonとDeepLearning
  • 日本酒自粛中
  • ドラムンベースからミニマルまで
  • ポケモンGOゆるめ

Tag

Python Deep Learning javascript chemoinformatics Emacs sake and more...

Ad

© kzfm 2003-2021