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ptosco/UnspecifedUnknownStereo.ipynb

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UnspecifedUnknownStereo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem.Draw import rdMolDraw2D\n",
"from IPython.display import SVG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stereo annotations can be added setting `MolDrawOptions.addStereoAnnotation` to `True`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def draw(mol, **kwargs):\n",
" mol_draw = rdMolDraw2D.PrepareMolForDrawing(mol)\n",
" draw_opt = rdMolDraw2D.MolDrawOptions()\n",
" draw_opt.addStereoAnnotation = True\n",
" draw_opt.addAtomIndices = True\n",
" drawer = rdMolDraw2D.MolDraw2DSVG(400, 300)\n",
" drawer.SetDrawOptions(draw_opt)\n",
" drawer.DrawMolecule(mol_draw, **kwargs)\n",
" drawer.FinishDrawing()\n",
" return SVG(drawer.GetDrawingText())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"mol = Chem.MolFromSmiles(\"CC(C)(C1=CC(=C(C(=C1)Br)O)Br)C(=CC(C(=O)O)Br)CC(=O)O\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"q = Chem.MolFromSmarts(\"C=CCBr\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"b = mol.GetBondBetweenAtoms(*mol.GetSubstructMatch(q)[:2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The RDKit has indeed labelled the double bond as stereochemically unspecified:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rdkit.Chem.rdchem.BondType.DOUBLE"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.GetBondType()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rdkit.Chem.rdchem.BondStereo.STEREONONE"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.GetStereo()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
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"<!-- END OF HEADER -->\n",
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"text/plain": [
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},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"draw(mol)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you change the double bond stereochemistry specifier to `BondStereo.STEREOANY` the RDKit will actually draw a crossed bond:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"b.SetStereo(Chem.BondStereo.STEREOANY)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"draw(mol)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you do specify the bond stereochemistry, the double bond is marked as `E` both in the data structures and in the drawing:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"mol_stereo = Chem.MolFromSmiles(\"CC(C)(C1=CC(=C(C(=C1)Br)O)Br)/C(=C/C(C(=O)O)Br)/CC(=O)O\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"b_stereo = mol_stereo.GetBondBetweenAtoms(*mol_stereo.GetSubstructMatch(q)[:2])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rdkit.Chem.rdchem.BondType.DOUBLE"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b_stereo.GetBondType()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rdkit.Chem.rdchem.BondStereo.STEREOE"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b_stereo.GetStereo()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"<!-- END OF HEADER -->\n",
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"<path class=\"bond-3\" d=\"M 143.063,130.128 L 124.5,151.719\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
"<path class=\"bond-22\" d=\"M 153.21,130.806 L 139.759,92.4174\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-5\" d=\"M 86.7193,154.104 L 73.2682,115.715\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"draw(mol_stereo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the `==` operator to compare molecules is not a sensible thing to do. Even a copy of the molecule will return `False` as they are actually two different instances of the same structure:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"smi = Chem.MolFromSmiles('CC(C)(C1=CC(=C(C(=C1)Br)O)Br)C(=CC(C(=O)O)Br)CC(=O)O')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"smi2 = Chem.Mol(smi)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi == smi2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"smi2 = Chem.MolFromSmiles('CC(C)(C1=CC(=C(C(=C1)Br)O)Br)C(=CC(C(=O)O)Br)CC(=O)O')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi == smi2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the same reason this fails (but it would fail even if molecules were actually the same):"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"isosmi = Chem.MolFromSmiles('CC(C)(C1=CC(=C(C(=C1)Br)O)Br)/C(=C/C(C(=O)O)Br)/CC(=O)O')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi == isosmi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing canonical SMILES is indeed a good way to assess identity between structures."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Regarding substructure matches, [as reported in the documentation](http://rdkit.org/docs/source/rdkit.Chem.rdchem.html?highlight=hassubstruct#rdkit.Chem.rdchem.Mol.HasSubstructMatch), by default they are not stereospecific; they will be if you specify the `useChirality` flag:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi.HasSubstructMatch(isosmi)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi.HasSubstructMatch(isosmi, useChirality=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isosmi.HasSubstructMatch(smi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This still correctly evaluates to `True` as the query does not specify any chirality."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isosmi.HasSubstructMatch(smi, useChirality=True)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"isosmi2 = Chem.MolFromSmiles('CC(C)(C1=CC(=C(C(=C1)Br)O)Br)\\\\C(=C/C(C(=O)O)Br)\\\\CC(=O)O')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x7fa6abe7ac10>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isosmi2"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isosmi.HasSubstructMatch(isosmi2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It correctly evaluates to `False` when the query specifies opposite stereochemistry:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isosmi.HasSubstructMatch(isosmi2, useChirality=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"smi.HasSubstructMatch(isosmi) and isosmi.HasSubstructMatch(smi)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x7f72fc320940>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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