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Different ways to fragment a molecule on some bonds
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have put explicit bonds in the SMILES definition to facilitate comprehension:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"mol = Chem.MolFromSmiles(\"O-C-C-C-C-N\")\n",
"mol1 = Chem.Mol(mol)\n",
"mol2 = Chem.Mol(mol)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x14c205f0bdf8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Chem.FragmentOnBonds()` will fragment all specified bond indices <i>at once</i>, and return a single molecule with all specified cuts applied. By default, `addDummies=True`, so empty valences are filled with dummy atoms:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"mol1_f = Chem.FragmentOnBonds(mol1, (0, 2, 4))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x14c24f02ce40>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol1_f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This molecule can be split into individual fragments using `Chem.GetMolFrags()`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=600x400 at 0x14C206763B38>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MolsToGridImage(Chem.GetMolFrags(mol1_f, asMols=True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Chem.FragmentOnSomeBonds()` will fragment according to all permutations of `numToBreak` bonds at a time (`numToBreak` defaults to 1), and return tuple of molecules with `numToBreak` cuts applied. By default, `addDummies=True`, so empty valences are filled with dummy atoms:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"mol2_f_tuple = Chem.FragmentOnSomeBonds(mol2, (0, 2, 4))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<rdkit.Chem.rdchem.Mol at 0x14c24f0cd190>,\n",
" <rdkit.Chem.rdchem.Mol at 0x14c24f0cd240>,\n",
" <rdkit.Chem.rdchem.Mol at 0x14c24f0cd030>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol2_f_tuple"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EAFiNEAIArEYIAQBWI4QAAKsRQgCA1QghAMBqhBAAYDVCCACwGiEEAFiNEAIArEYIAQBWI4QAAKsRQgCA1QghAMBqhBAAYDVCCACwGiEEAFiNEAIArEYIAQBWI4QAAKsRQgCA1QghAMBqhBAAYDVCCACwGiEEAFiNEAIArEYIAQBWI4QAAKsRQgCA1QghAMBqhBAAYDVCCACwGiEEAFiNEAIArEYIAQBWI4QAAKsRQgCA1QghAMBqhBAAYDVCCACw2v8BfrfBJHFYDMUAAAAASUVORK5CYII=\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x14c24f0cd190>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol2_f_tuple[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x14c24f0cd240>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol2_f_tuple[1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x14c24f0cd030>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol2_f_tuple[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, you can manually cut bonds using `Chem.RWMol.RemoveBonds`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"rwmol = Chem.RWMol(mol)\n",
"for b_idx in sorted([0, 2, 4], reverse=True):\n",
" b = rwmol.GetBondWithIdx(b_idx)\n",
" rwmol.RemoveBond(b.GetBeginAtomIdx(), b.GetEndAtomIdx())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then call `Chem.GetMolFrags()` to get sanitized fragments where empty valences were filled with implicit hydrogens:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=600x400 at 0x14C206763550>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MolsToGridImage(Chem.GetMolFrags(rwmol, asMols=True))"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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