def _mols2imageStream(mols, f, format, size, legend, highlightMatch=None):
"""Return an input stream for the molecule as drawn"""
highlights = None
if highlightMatch:
pattern = MolFromSmarts(highlightMatch)
highlights = [mol.GetSubstructMatch(pattern) for mol in mols]
kek = True
if mols[0].HasProp("_drawingBondsWedged"):
kek=False
fit = False
options = DrawingOptions()
subim = (size,size)
if size >150:
subim = (size *2,size *2)
options.coordScale = 3
options.bondLineWidth = 3.6
options.dblBondOffset = 0.435
options.atomLabelFontSize = 60
if kek:
options.bondLineWidth = 4.5
options.dblBondOffset = 0.6
options.atomLabelFontSize = 150
fit = True
elif kek:
options.dblBondOffset = 0.4
image = Draw.MolsToGridImage(mols,molsPerRow=min(len(mols),4),subImgSize=subim,
kekulize=kek,highlightAtomLists=highlights, fitImage=fit,
options=options
)
image.save(f, format)
def GenerateImageFile(ValidMols, MolNames, AtomLists, BondLists):
"""Generate a non SVG image file."""
Outfile = OptionsInfo["Outfile"]
NumOfMolsPerRow = OptionsInfo["NumOfMolsPerRow"]
Width = OptionsInfo["MolImageWidth"]
Height = OptionsInfo["MolImageHeight"]
# Setup drawing options...
UpdatedDrawingOptions = DrawingOptions()
UpdatedDrawingOptions.atomLabelFontSize = int(
OptionsInfo["AtomLabelFontSize"])
UpdatedDrawingOptions.bondLineWidth = float(OptionsInfo["BondLineWidth"])
MolsImage = Draw.MolsToGridImage(ValidMols,
molsPerRow=NumOfMolsPerRow,
subImgSize=(Width, Height),
legends=MolNames,
highlightAtomLists=AtomLists,
highlightBondLists=BondLists,
useSVG=False,
kekulize=OptionsInfo["Kekulize"],
options=UpdatedDrawingOptions)
MiscUtil.PrintInfo("\nGenerating image file %s..." % Outfile)
if MiscUtil.CheckFileExt(Outfile, "pdf"):
if MolsImage.mode == 'RGBA':
MolsImage = MolsImage.convert('RGB')
MolsImage.save(Outfile)
def generate_png(mol, pngpath, logfile=devnull, size=300):
with stdout_redirected(to=sys.stdout, stdout=sys.stderr):
with stdout_redirected(to=logfile, stdout=sys.stdout):
nhmol = RemoveHs(mol,
implicitOnly=False,
updateExplicitCount=True,
sanitize=False)
SanitizeMol(nhmol, catchErrors=True)
op = DrawingOptions()
op.atomLabelFontSize = size / 25
MolToFile(PrepareMolForDrawing(nhmol,forceCoords=True,addChiralHs=True),\
pngpath,fitImage=True,size=(size, size),options=op)
def _mols2imageStream(mols, f, format, size, legend, highlightMatch=None):
"""Return an input stream for the molecule as drawn"""
highlights = None
if highlightMatch:
pattern = MolFromSmarts(highlightMatch)
highlights = [mol.GetSubstructMatch(pattern) for mol in mols]
kek = True
if mols[0].HasProp("_drawingBondsWedged"):
kek = False
fit = False
options = DrawingOptions()
subim = (size, size)
if size > 150:
subim = (size * 2, size * 2)
options.coordScale = 3
options.bondLineWidth = 3.6
options.dblBondOffset = 0.435
options.atomLabelFontSize = 60
if kek:
options.bondLineWidth = 4.5
options.dblBondOffset = 0.6
options.atomLabelFontSize = 150
fit = True
elif kek:
options.dblBondOffset = 0.4
image = Draw.MolsToGridImage(mols,
molsPerRow=min(len(mols), 4),
subImgSize=subim,
kekulize=kek,
highlightAtomLists=highlights,
fitImage=fit,
options=options)
image.save(f, format)
from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions
# 二、芳香性
芳香性是一个简单又复杂的话题。无论是实验化学家还是理论化学家都无法给出一个明确的定义
rdkit除了通过模式匹配已知的芳香体系进行识别 ， 还定义了一些识别方向体系的规则 。
这些具体规则如下 ：
1.芳香性仅仅和环上的原子和键有关 。
2.芳香性键必须由2两个芳香性原子组成 。
3.由2个芳香性原子组成的键不一定就是芳香键。
m = Chem.MolFromSmiles('C1=CC2=C(C=C1)C1=CC=CC=C21')
opts = DrawingOptions()
opts.atomLabelFontSize = 400
opts.includeAtomNumbers = True
opts.dblBondLengthFrac = 0.8
opts.includeAtomNumbers = True
opts.dotsPerAngstrom = 1000
img = Draw.MolToImage(m, options=opts, size=(500, 500))
img.save(
'/drug_development/studyRdkit/st_rdcit/img/mol41.jpg'
)
# 基于RDKit方向模型判断其上的原子和键的芳香性
atom3 = m.GetAtomWithIdx(3).GetIsAromatic()
atom6 = m.GetAtomWithIdx(6).GetIsAromatic()
bond_36 = m.GetBondBetweenAtoms(3, 6).GetIsAromatic()
def gen_plot(QCA, fnm):
try:
d = np.loadtxt(fnm, usecols=(5, 2, 6), skiprows=3)
if d.size == 0:
return
except StopIteration:
print("No data for", fnm)
return
entry = np.genfromtxt(fnm,
usecols=(0, ),
skip_header=HEADER_LEN,
dtype=np.dtype("str"))[0]
smiles = np.genfromtxt(fnm,
usecols=(7, ),
skip_header=HEADER_LEN,
dtype=np.dtype("str"),
comments=None)[0]
scanned = np.genfromtxt(fnm,
usecols=(4, ),
skip_header=HEADER_LEN,
dtype=np.dtype("str"))[0]
scanned = eval('[' + scanned.replace('-', ',') + ']')
scanned = [x + 1 for x in scanned]
#print(scanned)
print(fnm, smiles)
mol_id = np.genfromtxt(fnm,
usecols=(1, ),
skip_header=HEADER_LEN,
dtype=np.dtype("str"))
with open(fnm, 'r') as fd:
ds_name = fd.readline().strip("\n")
method = fd.readline().strip("\n")
basis = fd.readline().strip("\n")
scan = d[:, 0]
qm_abs_min = d[:, 1].min()
qmene = d[:, 1] * const.hartree2kcalmol
qmene -= qmene.min()
mmene = d[:, 2]
mmene -= mmene.min()
mm_min_pt = scan[np.argsort(mmene)[0]]
qm_min_pt = scan[np.argsort(qmene)[0]]
qcmol = QCA.db.get(mol_id[0]).get("data")
try:
mol = rdutil.mol.build_from_smiles(smiles)
molnoindex = rdutil.mol.build_from_smiles(
re.sub(":[1-9][0-9]*", "", smiles))
except Exception as e:
print(e)
return
AllChem.ComputeGasteigerCharges(mol)
totalcharge = 0.0
for i, atom in enumerate(mol.GetAtoms()):
totalcharge += float(atom.GetProp('_GasteigerCharge'))
atom_map = rdutil.mol.atom_map(mol)
inv = {val: key for key, val in atom_map.items()}
#print(atom_map)
scanned = [inv[i] for i in scanned]
#scanned = [rdutil.mol.atom_map_invert( atom_map)[i] for i in scanned]
#print(scanned)
molflat = rdutil.mol.build_from_smiles(smiles)
try:
ret = rdutil.mol.embed_qcmol_3d(mol, qcmol)
#print("EMBED WAS RET:", ret)
#AllChem.GenerateDepictionMatching3DStructure(molflat, mol)
AllChem.Compute2DCoords(molnoindex)
except Exception as e:
print("Could not generate conformation:")
print(e)
return
options = DrawingOptions()
options.atomLabelFontSize = 110
options.atomLabelFontFace = "sans"
options.dotsPerAngstrom = 400
options.bondLineWidth = 8
options.coordScale = 1
png = Draw.MolToImage(molnoindex,
highlightAtoms=scanned,
highlightColor=[0, .8, 0],
size=(500 * 12, 500 * 4),
fitImage=True,
options=options)
fig = plt.figure(figsize=(8, 3.5), dpi=300, constrained_layout=True)
matplotlib.rc("font", **{"size": 10})
gs = GridSpec(2, 2, figure=fig)
ax1 = fig.add_subplot(gs[:2, 0])
ax1.plot(scan, qmene, 'bo-', lw=1, ms=2, label="QM")
ax1.plot(scan, mmene, 'ro-', lw=1, ms=2, label="MM")
ax1.set_ylabel("Energy (kcal/mol)")
ax1.set_xlabel("Scan value")
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.legend(loc='best')
ax2 = fig.add_subplot(gs[0, 1])
ax2.imshow(png)
ax2.axis('off')
ax3 = fig.add_subplot(gs[1, 1])
kwargs = {"fontsize": 8, "fontname": "Monospace"}
#ax3.text(.5,1.1,"{:^60s}".format(re.sub(":[1-9][0-9]*","",smiles)), fontsize=4, fontname="Monospace", ha='center')
ax3.text(0, .9, "{:<16s}{:>39s}".format("QCA Dataset:", ds_name), **kwargs)
ax3.text(0, .8, "{:<16s}{:>39s}".format("QCA Entry:",
entry.split("-")[1]), **kwargs)
ax3.text(0, .7, "{:<16s}{:>39s}".format("MM spec:",
"Parsley unconstrained"), **kwargs)
ax3.text(0, .6, "{:<16s}{:>39s}".format("MM charges:",
"ANTECHAMBER AM1-BCC"), **kwargs)
ax3.text(
0, .5, "{:<16s}{:>39s}".format("Total charge",
"{:6.2f}".format(totalcharge)),
**kwargs)
ax3.text(
0, .4, "{:<16s}{:>39s}".format("QM spec:",
"{}/{}".format(method, basis)),
**kwargs)
ax3.text(
0, .3, "{:<16s}{:>39s}".format("QM Absolute min:",
"{:16.13e} au".format(qm_abs_min)),
**kwargs)
ax3.text(
0, .2, "{:<16s}{:>39s}".format("QM Min point:",
"{:8.2f}".format(qm_min_pt)), **kwargs)
ax3.text(
0, .1, "{:<16s}{:>39s}".format("MM Min point:",
"{:8.2f}".format(mm_min_pt)), **kwargs)
ax3.text(0, .0, "{:<16s}{:>39s}".format("OFFSB ver", "1.0"), **kwargs)
ax3.axis('off')
fig.savefig(re.sub("\.*$", ".png", fnm))
fig = None
plt.close("all")
return
