In [3]:
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import RDConfig
from rdkit.Chem.Draw import IPythonConsole 
from rdkit.Chem import Draw
import numpy as np
from IPython.display import display,Image
In [18]:
import rdkit.Chem.Lipinski as Lipinksy

ibu=Chem.MolFromSmiles('CC(C)CC1=CC=C(C=C1)C(C)C(=O)O')
AllChem.Compute2DCoords(ibu)
display(ibu)
#параметры для правила Лепински
print('Параметры для правила Лепински')
print(Lipinksy.NumHDonors(ibu))
print(Lipinksy.NumHAcceptors(ibu))
print(Lipinksy.rdMolDescriptors.CalcExactMolWt(ibu))
print(Lipinksy.rdMolDescriptors.CalcCrippenDescriptors(ibu)[0])

Параметры для правила Лепински
1
1
206.130679816
3.073200000000001

Примеры модификации ибупрофена для клик-реакции и результат самой реакции¶

In [5]:
ibu=Chem.MolFromSmiles('C#CC(C)CC1=CC=C(C=C1)C(C)C(=O)O')
AllChem.Compute2DCoords(ibu)
display(ibu)
In [6]:
ibu=Chem.MolFromSmiles('N1C=C(N=N1)C(C)CC1=CC=C(C=C1)C(C)C(=O)O')
AllChem.Compute2DCoords(ibu)
display(ibu)

Загрузка азидов¶

In [9]:
import pubchempy as pcp

structures = []
per_page = 10**5

for smiles in ["N=N=N", "NN#N", "N#NN"]:
    for i in range(200):
        try:
            a = pcp.get_properties(
              properties="CanonicalSMILES", 
              identifier=smiles, namespace="smiles", 
              searchtype="substructure",
              RingsNotEmbedded=True,
              listkey_count=per_page, listkey_start=i*per_page
            )
        except:
            break
        print("Retrieved page {} of {} search".format(i+1, smiles))
        structures.extend(a)

Retrieved page 1 of N=N=N search
Retrieved page 2 of N=N=N search
Retrieved page 3 of N=N=N search
Retrieved page 1 of NN#N search
Retrieved page 1 of N#NN search
In [10]:
compounds = {str(i['CID']).strip():i['CanonicalSMILES'].strip() for i in structures}

smiles = list(compounds.values())
In [20]:
# Всего соединений
len(compounds)
Out[20]:
262245

Преобразование и получение валидных структур, соответствующих правилам¶

In [12]:
res = []
for smi in smiles:
    if len(res) > 25:
        break
    if '=N[N+]#N' in smi:
        newsmi=smi.replace('=N[N+]#N','N1C=C(N=N1)C(C)CC1=CC=C(C=C1)C(C)C(=O)O')
    else:
        continue
    try:
        newmol=Chem.MolFromSmiles(newsmi)
        if (Lipinksy.NumHDonors(newmol)<=5) and (Lipinksy.NumHAcceptors(newmol)<=10) and (Lipinksy.rdMolDescriptors.CalcExactMolWt(newmol) < 500) and (Lipinksy.rdMolDescriptors.CalcCrippenDescriptors(newmol)[0] <= 5):
            AllChem.Compute2DCoords(newmol)
            res.append(newmol)
    except:
        pass
    
from IPython.display import display

to_print = [res[8], res[1], res[2], res[10], res[11], res[21], res[22], res[23], res[25]]
display(Draw.MolsToGridImage(to_print, useSVG=True, molsPerRow=3, subImgSize=(300, 200)))

Similarity maps¶

In [13]:
from rdkit.Chem.Draw import SimilarityMaps

fp = SimilarityMaps.GetMorganFingerprint(res[4], fpType='bv')

fig, maxweight = SimilarityMaps.GetSimilarityMapForFingerprint(ibu, res[5], SimilarityMaps.GetMorganFingerprint)

3D view¶

In [16]:
import nglview as nv

m3d=Chem.AddHs(res[5])
Chem.AllChem.EmbedMolecule(m3d)
AllChem.MMFFOptimizeMolecule(m3d,maxIters=500,nonBondedThresh=200 )
display(m3d)











    
    































In [ ]: