def do_images(xs, molecules, conditions, samples, images_per_condition,
output_dir):
os.makedirs(os.path.join(output_dir, 'valid'), exist_ok=True)
os.makedirs(os.path.join(output_dir, 'accurate'), exist_ok=True)
os.makedirs(os.path.join(output_dir, 'invalid'), exist_ok=True)
for c in range(conditions):
image_mols = random.sample(
list(
zip(xs[c * samples:(c + 1) * samples],
molecules[c * samples:(c + 1) * samples])),
images_per_condition)
for i, (x, a) in enumerate(image_mols):
m = get_mol(x, a[0])
try:
err = Chem.rdmolops.SanitizeMol(m, catchErrors=True)
if err == 0 and is_valid(x, a[0]):
if get_is_connected(x, a[0]):
Draw.MolToImageFile(
m,
os.path.join(output_dir, 'accurate',
'c_{}_all_{}.png'.format(c, i)))
else:
Draw.MolToImageFile(
m,
os.path.join(output_dir, 'valid',
'c_{}_all_{}.png'.format(c, i)))
else:
Draw.MolToImageFile(
m,
os.path.join(output_dir,
'c_{}_all_{}.png'.format(c, i)))
except ValueError:
continue
def converter(file_name,save_name):
mols = [ mol for mol in Chem.SDMolSupplier( file_name ) ]
outname = save_name + ".smi"
out_file = open( outname, "w" )
for mol in mols:
smi = Chem.MolToSmiles(mol)
#print(smi)
name = mol.GetProp("_Name")
out_file.write( "{}\t{}\n".format(smi, name ))
m = Chem.MolFromSmiles(smi)
m_qed = Chem.QED.qed(m)
m_LogP = round(Descriptors.MolLogP(mol), 4)
print(file_name,end = " ")
print("->",m_qed,m_LogP) #Chem.QED.properties(m)
Draw.MolToImageFile(m,save_name+".png",size=(300, 300))
m = Chem.AddHs(m)
AllChem.EmbedMolecule( m,randomSeed=3 )
try :
#AllChem.MMFFOptimizeMolecule(m)
#Chem.MolToMolFile(m,file_name+".mol")
#out_file.close()
return smi,m_qed,m_LogP
except ValueError:
print("Rdkit not opt mol")
return 0
import os
from rdkit import Chem
from rdkit.Chem import Draw
# get a path
def GetPath(file):
path = sys.path[0]
path = os.path.normpath(path)
return os.path.join(path, file)
SMILES1 = "O"
SMILES2 = "CCO"
SMILES3 = "O=C=O"
SMILES4 = "C#N"
SMILES5 = "C1CCCCC1"
SMILES6 = "CC"
SMILES7 = "C=C"
SMILES8 = "C#C"
SMILES9 = "CC(=O)OCC"
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES1), GetPath("水.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES2), GetPath("乙醇.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES3), GetPath("二氧化碳.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES4), GetPath("氰化氢.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES5), GetPath("环已烷.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES6), GetPath("乙烷.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES7), GetPath("乙烯.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES8), GetPath("乙炔.jpg"))
Draw.MolToImageFile(Chem.MolFromSmiles(SMILES9), GetPath("乙酸乙酯.jpg"))
from rdkit import Chem from rdkit.Chem import Draw smi = 'CCCc1nn(C)c2C(=O)NC(=Nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4' m = Chem.MolFromSmiles(smi) Draw.MolToImageFile(m,"mol.jpg")
def plot_mol_matrix():
import cairosvg
import seaborn as sns
import matplotlib.pyplot as plt
smiles = 'CN(C)C(=N)NC(=N)N' #'CC(C)NC1=CC=CO1' #'CC1=C(SC(=C1)C(=O)NCC2=NOC=C2)Br'
bond, atoms = smiles_to_adj(smiles, 'qm9')
bond = bond[0]
atoms = atoms[0]
# def save_mol_png(mol, filepath, size=(100, 100)):
# Draw.MolToFile(mol, filepath, size=size)
Draw.MolToImageFile(Chem.MolFromSmiles(smiles), 'mol.pdf')
# save_mol_png(Chem.MolFromSmiles(smiles), 'mol.png')
svg = Draw.MolsToGridImage([Chem.MolFromSmiles(smiles)],
legends=[],
molsPerRow=1,
subImgSize=(250, 250),
useSVG=True)
# highlightAtoms=vhighlight) # , useSVG=True
cairosvg.svg2pdf(bytestring=svg.encode('utf-8'), write_to="mol.pdf")
cairosvg.svg2png(bytestring=svg.encode('utf-8'), write_to="mol.png")
# sns.set()
# ax = sns.heatmap(1-atoms)
# with sns.axes_style("white"):
fig, ax = plt.subplots(figsize=(2, 3.4))
# sns.palplot(sns.diverging_palette(240, 10, n=9))
ax = sns.heatmap(atoms,
linewidths=.5,
ax=ax,
annot_kws={"size": 18},
cbar=False,
xticklabels=False,
yticklabels=False,
square=True,
cmap="vlag",
vmin=-1,
vmax=1,
linecolor='black')
# ,cmap=sns.diverging_palette(240, 10, n=9)) #"YlGnBu" , square=True
plt.show()
fig.savefig('atom.pdf')
fig.savefig('atom.png')
for i, x in enumerate(bond):
fig, ax = plt.subplots(figsize=(5, 5))
# sns.palplot(sns.diverging_palette(240, 10, n=9))
ax = sns.heatmap(x,
linewidths=.5,
ax=ax,
annot_kws={"size": 18},
cbar=False,
xticklabels=False,
yticklabels=False,
square=True,
cmap="vlag",
vmin=-1,
vmax=1,
linecolor='black')
# ,cmap=sns.diverging_palette(240, 10, n=9)) #"YlGnBu" , square=True
plt.show()
fig.savefig('bond{}.pdf'.format(i))
fig.savefig('bond{}.png'.format(i))
img = Draw.ReactionToImage(
rxn
)
img.save(
'/drug_development/studyRdkit/st_rdcit/img/mol31.jpg'
)
# 反应模板如下图所示：
# 从反应模板中，我们看到主要的变化是Cl变成羰基氧，N上多了一个甲基
# >注：这是一个逆反应模板
# 反应物如下图所示 ：
mol = Chem.MolFromSmiles(
'CC(C)(Nc1nc(Cl)c(-c2ccc(F)cc2)c(-c2ccncc2)n1)c1ccccc1')
Draw.MolToImageFile(
mol,
"/drug_development/studyRdkit/st_rdcit/img/mol32.jpg",
size=(350, 300),
legend='CC(C)(Nc1nc(Cl)c(-c2ccc(F)cc2)c(-c2ccncc2)n1)c1ccccc1'
)
# .创建具体反应规则的引擎对象rxn = AllChem.ReactionFromSmarts(tem)
# .输入反应物，借助引擎产生反应rxn.RunReactants([productmol])
def getrxns(rxn, product_smi):
"""
获取反应规则的引擎对象
product_smi 反应物
"""
product_mol = Chem.MolFromSmiles(product_smi)
reactions = rxn.RunReactants([product_mol])
rxns = []
for reaction in reactions:
width,
bottom=bottom,
color=col,
antialiased=True)
bottom += sort[length - i][1]
if col != 'black':
c_index += 0.3
bottoms.append(bottom)
except:
raise
ax.set_xticks(keys)
ax.set_xticklabels(keys)
plt.ylabel('Absolute substructure frequency')
plt.xlabel('Fingerprint bit')
text_yoffset = 5
for i in keys:
text_xoffset = -0.125 if uniques[i] < 100 else -0.185
plt.text(i + text_xoffset,
bottoms[i] + text_yoffset,
uniques[i],
weight='bold')
plt.savefig('substructures.png', dpi=400)
# RDKit throws errors, don't know why
for struct_ix in range(len(draw_structs)):
Draw.MolToImageFile(Chem.MolFromSmarts(draw_structs[struct_ix]),
f'substruct_{struct_ix}.png',
kekulize=False)
#! /usr/bin/python
# coding: utf-8
# @Time: 2020-05-29 14:36:04
# @Author: zeoy
# rdkit 修改分子
# 一、引入所需库
from rdkit import Chem
from rdkit.Chem import Draw
# 二、增删H原子
mol = Chem.MolFromSmiles('OC1C2C1CC2')
# 画分子结构
Draw.MolToImageFile(
mol,
'/drug_development/studyRdkit/st_rdcit/img/mol5.jpg'
)
# 2.1 增加H原子函数解析
# 将氢添加到分子图上
rdkit.Chem.rdmolops.AddHs(
(Mol)mol # 要修饰的分子
[, (bool) explicitOnly=False # （可选）如果设置了此切换，则仅将显式Hs添加到分子中。默认值为0（添加隐式和显式Hs）。
[, (bool) addCoords=False # (可选) 如果设置了此开关，则Hs将设置3D坐标。默认值为0（无3D坐标）。
[, (AtomPairsParameters) onlyOnAtoms=None # （可选）如果提供了此序列，则仅将这些原子视为具有添加的Hs
[, (bool)addResidueInfo=False # （可选）如果为true，则将残基信息添加到氢原子（对PDB文件有用）。
]]]]
)
# 2.2 增加H原子
from optimizer import get_mol
import torch
from rdkit import Chem
from rdkit.Chem import Draw
x = torch.FloatTensor([[1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])
a = torch.LongTensor([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 3],
[0, 0, 3, 1]])
m = get_mol(x, a)
Chem.rdmolops.SanitizeMol(m)
s = Chem.MolToSmiles(m)
print(s)
m = Chem.MolFromSmiles(s)
for a in m.GetAtoms():
print(a.GetSymbol(), a.GetExplicitValence())
for b in m.GetBonds():
print(b)
Draw.MolToImageFile(m, 'images/kill_me.png')
# coding=utf-8
import io
from PIL import Image
from rdkit import Chem
from rdkit.Chem import Draw
smi = 'CCCc1nn(C)c2C(=O)NC(=Nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4'
m = Chem.MolFromSmiles(smi)
Draw.MolToImageFile(m, 'mol.jpg')
import base64
from io import BytesIO
# img_buffer = BytesIO()
# a.save(img_buffer, format='JPEG')
# byte_data = img_buffer.getvalue()
# base64_str = base64.b64encode(byte_data)
# print(base64_str)
# 图片转字节
with open('mol.jpg', 'rb') as fp:
tu = base64.b64encode(fp.read())
print(tu)
img = Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(
200, 200), legends=['' for x in mols])
img.save('/drug_development/studyRdkit/st_rdcit/img/mol13.jpg')
# > 注：ReplaceSubstructs()替换操作返回的是分子对象操作列表，如果分子只有一个地方匹配到，则返回一个分子的列表。
# 如果分子中有2个地方匹配到，则返回2个分子的列表。为了标准化smiles，可以将得到的分子mol化->smiles->mol，然后在进行可视化
# 2.3 SAR分析-core可视化
# Chem.ReplaceSidechains(m1,core) : 我们需要定义分子对象，骨架分子； 然后执行ReplaceSidechains函数，删除侧链就能得到骨架可视化。
# 定义嘧啶为核心结构，对其骨架进行可视化
m1 = Chem.MolFromSmiles('BrCCc1cncnc1C(=O)O')
core = Chem.MolFromSmiles('c1cncnc1')
tmp = Chem.ReplaceSidechains(m1, core)
Chem.MolToSmiles(tmp)
Draw.MolToImageFile(
tmp, '/drug_development/studyRdkit/st_rdcit/img/mol14.jpg')
# 2.4 SAR分析-sidechain可视化
m1 = Chem.MolFromSmiles('BrCCc1cncnc1C(=O)O')
core = Chem.MolFromSmiles('c1cncnc1')
tmp = Chem.ReplaceCore(m1, core)
Draw.MolToImageFile(
tmp, '/drug_development/studyRdkit/st_rdcit/img/mol15.jpg')
# >注：侧链的编号默认是从1开始的，这取决于算法找到侧链的先后顺序。
# 也可以根据侧链连接到骨架上的原子进行编号tmp=CHem.ReqlaceCore(m1, core)
tmp = Chem.ReplaceCore(m1, core, labelByIndex=True)
Draw.MolToImageFile(
tmp, '/drug_development/studyRdkit/st_rdcit/img/mol16.jpg')
# 2.5 拆分手段
# 三、SMARTS 支持的扩展
# # 3.1 杂化方式查询
# 杂化方式在SMARTS 中通过^符号进行定义。 如：
# 1.^0 匹配S 杂化的原子
# 2.^1 匹配SP 杂化的原子
# 3.^2 匹配SP2 杂化的原子
# 4.^3 匹配SP3 杂化的原子
# 5.^4 匹配SP3D 杂化的原子
# 6.^5 匹配SP3D2 杂化的原子
aspirin = Chem.MolFromSmiles('CC(=O)OC1=CC=CC=C1C(=O)O')
Draw.MolToImageFile(
aspirin,
'/drug_development/studyRdkit/st_rdcit/img/mol52.jpg',
legend='aspirin'
)
# 阿司匹林
# sp2杂化的原子
sp2_atoms = aspirin.GetSubstructMatches(Chem.MolFromSmarts('[^2]'))
# sp3杂化的原子
sp3_atoms = aspirin.GetSubstructMatches(Chem.MolFromSmarts('[^3]'))
print('sp2 atoms', sp2_atoms)
# sp2 atoms ((1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,), (10,), (11,), (12,))
print('sp3 atoms', sp3_atoms) # sp3 atoms ((0,),)
# 对于分子阿司匹林，只有0号原子是sp3杂化，其他原子都是sp2杂化
# >注：苯酚中的氧都是sp2杂化，所以羟基氧才具有更强的酸性。COO中的两个氧都是sp2杂化 羧基中也有类似苯环的共轭体系，并且羧酸中羟基氢酸性更强，共轭更明显，更应该是sp2。 醇羟基中的氧是sp3杂化。
# # 3.2 配位键
# rdkit的SMARTS通过 -> 和 < -符号表示配位键 ， 箭头的方向代表电子转译的方向
# rdkit smiles支持和扩展
# 一、引入所需库
import os
from rdkit import Chem
from rdkit.Chem import Draw
# rdkit涵盖了Daylight SMILES所有的标准功能以及一些有用的扩展，下面是扩展的部分内容
# 二、芳香性
# 和氧同族的Te(碲 ， 拼音 ： dì ， 原子序数52 ， 是银白色的类金属 ） 元素也可能具有芳香性 ， 当其连接2个芳香原子时 ， 它贡献2个pi电子 。
m = Chem.MolFromSmiles('OC(=O)c1[te]ccc1')
Draw.MolToImageFile(
m,
'/drug_development/studyRdkit/st_rdcit/img/mol48.jpg',
legend='tellurophene-2-carboxylic acid'
)
# 碲吩-2甲酸分子
# Te原子的编号是4，下面检查其芳香性
aromatic_atom4 = m.GetAtomWithIdx(4).GetIsAromatic()
print('atom4 is aromatic', aromatic_atom4) # atom4 is aromatic True
# 三 配位键
rdkit通过 -> 和 < -来支持配位键表示 ， 箭头的方向非常重要 ， 代表了谁提供电子
配位键不会影响起始原子的价态 ， 只会影响指向原子的价态
cu_mol = Chem.MolFromSmiles('[Cu](Cl)Cl')
bipy = Chem.MolFromSmiles('C1(C2=NC=CC=C2)=CC=CC=N1')
bipycu = Chem.MolFromSmiles('c1cccn->2c1-c1n->3cccc1.[Cu]23(Cl)Cl')
mols = [cu_mol, bipy, bipycu]
img = Draw.MolsToGridImage(
mols,
def draw_glycine():
Draw.MolToImageFile(Chem.MolFromFASTA('G'), 'glycine.png')
# 如果两个环上有公用的原子
if nInCommon and (includeSpiro or nInCommon > 1):
# 公用的原子，说明两个环是并在一起的
# 将两个环的原子去重合并
ringAts = ringAts.union(system)
print(ringAts)
else:
nSystems.append(system)
nSystems.append(ringAts)
systems = nSystems
return systems
mol = Chem.MolFromSmiles('CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3')
Draw.MolToImageFile(
mol,
"/drug_development/studyRdkit/st_rdcit/img/mol42.jpg",
)
ringInfo = GetRingSystems(mol)
print(ringInfo)
# # 4.2 环外原子对芳香环（Aromatic）的影响
# >注：环键上连接的负电性原子会“窃取”环原子的价电子，且这些亚原子，提供了使环芳香性所必须的元素。
# 使用稠环来增加芳香度可能导致单个环不是芳香的情况，但稠环系统是芳香性的。其中一个例子就是azulene（甘菊蓝）
# 下面的例子，展示了两个稠环和环外双键的影响
m = Chem.MolFromSmiles('O=C1C=CC(=O)C2=C1OC=CO2')
Draw.MolToImageFile(
m,
"/drug_development/studyRdkit/st_rdcit/img/mol43.jpg",
)
isAromatic6 = m.GetAtomWithIdx(6).GetIsAromatic()
print(isAromatic6) # True
def getNearestNeighbors(query,
n,
NNDataPath,
FPPath=None,
resPath=None,
idx=0):
""" get the n nearest neighbors
query: bin string with query fingerprint
returns an ordered list with the n top neighbors (each one in a dict):
[ {
"id" : ID,
"expVal" : ExpValues,
"similarity" : TanimotoSimilarity,
"smi" : smiles,
"imgPath" : imgPath,
"MeanInhib" : Mean Inhib. }, ... ]
It will saves the images in resPath:
NN_1.png #1 neighbor
NN_2.png #2 neighbor
...
NN_n.png #n neighbor
"""
if not query or not n or not NNDataPath or not FPPath:
return []
#if resPath and not os.path.isdir(resPath):
# os.makedirs(resPath)
# get the correct header
file = open(NNDataPath, "r")
header = file.readline().strip().split('\t')
file.close()
if "Molecule SMILES" not in header or "Compound Name" not in header:
print "NN dataset ", NNDataPath, " have not the correct header. It must contain 'Molecule SMILES' and 'Compound Name' attributes."
return []
# Index will have to be sum 1 because the TS will be prepended
idxID = header.index("Compound Name") + 1
idxExpVal = len(header)
idxSMILES = header.index("Molecule SMILES") + 1
idxSimilarity = 0
Nbits = 2048
cmdStr = 'echo "' + query + '" | fpin ' + FPPath + " " + NNDataPath + ' 0.0 ' + str(
n)
status, output = commands.getstatusoutput(cmdStr)
if status:
print status
print output
raise Exception(str(output))
# TS SMILES AZID DATE expRes
# output = "0.7117 CCCC(C)C1(C(=O)NC(=O)NC1=O)CC AZ10046012 2009-12-02 3.480007"
TS = []
for ts in output.split("\n"):
TS.append(ts.strip().split('\t'))
# in TS:
# TS[n][0] - tanimoto similarity
# TS[n][1] - SMILES
# TS[n][2] - AZID
# TS[n][-1]- expRes
res = []
timeStamp = str(time.time()).replace(".", '')
for fidx, nn in enumerate(TS):
ID = nn[idxID]
if miscUtilities.isNumber(nn[idxExpVal]):
expVal = str(round(float(nn[idxExpVal]), 2))
else:
expVal = nn[idxExpVal]
SMILES = nn[idxSMILES]
if resPath and os.path.isdir(resPath):
imgPath = os.path.join(
resPath, "NN" + str(idx) + "_" + str(fidx + 1) + "_" +
timeStamp + ".png")
mol = Chem.MolFromSmiles(SMILES)
# save the respective imgPath...
Draw.MolToImageFile(mol,
imgPath,
size=(300, 300),
kekulize=True,
wedgeBonds=True)
else:
imgPath = ""
res.append({
"id": ID,
"expVal": expVal,
"similarity": nn[idxSimilarity],
"smi": SMILES,
"imgPath": imgPath,
"MeanInhib": ''
})
return res
from rdkit import Chem
from rdkit.Chem import Draw
if __name__ == '__main__':
smi = 'CCCc1nn(C)c2C(=O)NC(=Nc12)c3cc(ccc3OCC)S(=O)(=O)N4CCN(C)CC4'
m = Chem.MolFromSmiles(smi)
Draw.MolToImageFile(m, "mol.jpg", size=(200, 300))
def createSignImg(self,
smi,
signature,
atomColor,
imgPath,
endHeight=None):
colors = []
print "Creating signature image..."
if not signature or not atomColor or not smi:
print "Missing inputs:", str([smi, signature, atomColor])
return "", "", [], []
if hasattr(self.model, "specialType") and self.model.specialType == 1:
# Create an Orange ExampleTable with a smiles attribute
smilesAttr = orange.EnumVariable("SMILEStoPred", values=[smi])
myDomain = orange.Domain([smilesAttr], 0)
smilesData = dataUtilities.DataTable(myDomain, [[smi]])
preCalcData = None
startHeight = 0
dataSign, cmpdSignDict, cmpdSignList, sdfStr = getSignatures.getSignatures(
smilesData,
startHeight,
endHeight,
preCalcData,
returnAtomID=True)
cmpdSignList = cmpdSignList[0]
CLabDesc = []
# create a mol file
tmpFile = miscUtilities.generateUniqueFile(desc="NN", ext="mol")
file = open(tmpFile, "w")
molStr = ""
for line in sdfStr[0]:
if "$$$$" in line:
break
molStr += line
file.write(line)
file.close()
else:
CLabDesc, cmpdSignList, tmpFile, molStr = self.getClabDescSignList(
smi, getMolFile=True)
if not cmpdSignList or not tmpFile:
print "Couldn't get the cmpd list or the mol file"
return "", "", [], []
# create an RDKit mol
mol = Chem.MolFromMolFile(tmpFile, True, False)
if not mol:
mol = Chem.MolFromMolFile(tmpFile, False, False)
if not mol:
print "Could not create mol for: ", smi
return "", "", [], []
adj = GetAdjacencyMatrix(mol)
# find the NN
hights = []
for i in miscUtilities.Range(0, len(cmpdSignList), mol.GetNumAtoms()):
hList = cmpdSignList[i:i + mol.GetNumAtoms()]
if len(hList):
hights.append(cmpdSignList[i:i + mol.GetNumAtoms()])
atoms = []
hight = None
for idx, h in enumerate(hights):
if signature in h:
for i, a in enumerate(h):
if a == signature:
atoms.append(i)
hight = idx
break
if len(atoms) == 0:
print "ERROR: Could not find the atom for ", signature
return "signatureNOTfound", "", [], []
#print "IniAtoms: ",atoms
visitedAtoms = []
for n in range(hight):
for atom in copy.deepcopy(atoms):
if atom not in visitedAtoms:
lNN = findNeighbors(atom, adj)
visitedAtoms.append(atom)
for lnn in lNN:
if lnn not in atoms:
atoms.append(lnn)
atoms.sort()
os.system("rm " + tmpFile)
#Specify the atom colors
colors = [atomColor] * len(atoms)
if not imgPath:
return "", molStr, atoms, colors
try:
#Draw the image
MolDrawing.elemDict = defaultdict(lambda: (0, 0, 0))
Draw.MolToImageFile(mol,
imgPath,
size=(300, 300),
kekulize=True,
wedgeBonds=True,
highlightAtoms=atoms)
#Color the Highlighted atoms with the choosen atomColor.
# Only using one color
if atomColor == 'r':
rgb = (255, 0, 0)
elif atomColor == 'g':
rgb = (0, 255, 0)
else:
rgb = (0, 0, 255) #Blue
img = Image.open(imgPath)
img = img.convert("RGBA")
pixdata = img.getdata()
newData = list()
for item in pixdata:
if item[0] == 255 and item[1] == 0 and item[2] == 0:
newData.append(rgb + (255, ))
else:
newData.append(item)
img.putdata(newData)
img.save(imgPath)
if os.path.isfile(imgPath):
return imgPath, molStr, atoms, colors
else:
return "", molStr, atoms, colors
except:
return "", molStr, atoms, colors
print('smi=', hierarch.smiles) # smi= CCC(=O)OCCOc1ccccc1
# 每个节点使用smiles键控的字典跟踪其子节点
ks = hierarch.children.keys()
print(sorted(ks))
# ['*C(=O)CC', '*CCOC(=O)CC', '*CCOc1ccccc1', '*OCCOc1ccccc1', '*c1ccccc1']
# # 3.2 BRICS方法
# RDKit 还提供了另一种把分子切成片段的方法——BRICS方法。 BRICS方法主要是根据可合成的的键对分子进行切断，因此其返回的数据结构是来自于该分子的不同分子片段， 虚拟原子（*）是告诉我们是如何切断的。
# 对下图中的分子进行BRICS分解
smi = 'C=CC(=O)N1CCC(CC1)C2CCNC3=C(C(=NN23)C4=CC=C(C=C4)OC5=CC=CC=C5)C(=O)N'
m = Chem.MolFromSmiles(smi)
Draw.MolToImageFile(
m,
"/drug_development/studyRdkit/st_rdcit/img/mol34.jpg",
size=(600, 400),
legend=
'zanubrutinib(C=CC(=O)N1CCC(CC1)C2CCNC3=C(C(=NN23)C4=CC=C(C=C4)OC5=CC=CC=C5)C(=O)N)'
)
frags = (BRICS.BRICSDecompose(m))
print(frags)
mols = []
for fsmi in frags:
mols.append(Chem.MolFromSmiles(fsmi))
img = Draw.MolsToGridImage(mols,
molsPerRow=3,
subImgSize=(200, 200),
legends=['' for x in mols])
from rdkit.Chem import ChemicalFeatures
from rdkit.Chem.Pharm2D.SigFactory import SigFactory
from rdkit.Chem.Pharm2D import Generate, Gobbi_Pharm2D
# 二、化学性质
# 建立一个化学性质对象,通过该对象可以得到分子的化学性质
fdefName = os.path.join(
RDConfig.RDDataDir,
'/drug_development/studyRdkit/st_rdcit/data/BaseFeatures.fdef')
factory = ChemicalFeatures.BuildFeatureFactory(fdefName)
smi = 'C=CC(=O)N1CCC(CC1)C2CCNC3=C(C(=NN23)C4=CC=C(C=C4)OC5=CC=CC=C5)C(=O)N'
m = Chem.MolFromSmiles(smi)
Draw.MolToImageFile(
m,
"/drug_development/studyRdkit/st_rdcit/img/mol38.jpg",
)
# 使用特征工厂搜索特征
feats = factory.GetFeaturesForMol(m)
print(len(feats)) # 16
# 搜索到的每个特征都包含了改特征家族（例如受体、供体等）特征类别、该特征对应的原子、特征对应的序号等
for f in feats:
print(
f.GetFamily(), # 特征家族信息
f.GetType(), # 特征类型信息
f.GetAtomIds() # 特征对应原子
)
# Donor SingleAtomDonor (4,)
# Donor SingleAtomDonor (13,)
# rdkit支持从Smiles、mol、sdf文件中读入分子获取分子对象。
# Smiles、mol通常用于保存单个分子；而sdf格式是作为分子库形式设计的。
# 因此读入sdf得到的是分子迭代器，读入Smiles、mol文件得到分子对象。
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
# 一、读分子操作
# 1.1、读入smiles
smi = 'CC(C)OC(=O)C(C)NP(=O)(OCC1C(C(C(O1)N2C=CC(=O)NC2=O)(C)F)O)OC3=CC=CC=C3'
mol = Chem.MolFromSmiles(smi) # 将Smiles转换为mol对象
# 将Mol分子画出结构图，并存储在相应地址
Draw.MolToImageFile(
mol, # mol分子对象
"/drug_development/studyRdkit/st_rdcit/img/mol2.jpg" # 分子结构图存储地址
)
print('mol的类型=', type(mol)) # mol的类型=<class 'rdkit.Chem.rdchem.Mol'>
# 1.2、读入mol文件
# 将mol文件转换为mol对象
mol3 = Chem.MolFromMolFile(
'/drug_development/studyRdkit/st_rdcit/data/952883.mol')
# 将Mol分子画出结构图，并存储在相应地址
Draw.MolToImageFile(
mol3, # mol分子对象
"/drug_development/studyRdkit/st_rdcit/img/mol3.jpg" # 分子结构图存储地址
)
print('mol3的类型=', type(mol)) # mol3的类型=<class 'rdkit.Chem.rdchem.Mol'>
