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ptosco/EmbedMultipleCoordMap.ipynb

Created January 25, 2019 19:15
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EmbedMultipleCoordMap notebook
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EmbedMultipleCoordMap.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem import (rdDistGeom,\n",
" rdDepictor, rdMolAlign, rdMolTransforms, rdmolops)\n",
"from rdkit.Chem.Draw import IPythonConsole\n",
"import py3Dmol"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"mol = Chem.MolFromMolBlock(\n",
"\"\"\"Untitled\n",
" RDKit 3D\n",
" \n",
" 71 74 0 0 0 0 0 0 0 0999 V2000\n",
" 9.6369 -1.5816 -0.4538 O 0 5 0 0 0 0 0 0 0 0 0 0\n",
" 9.2303 -0.3807 -0.2633 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 9.5332 0.6344 -0.9854 O 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 8.2967 -0.1411 0.9097 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 6.8663 -0.2160 0.3704 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 5.9989 0.8500 0.4803 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 4.7201 0.7725 -0.0198 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 4.2834 -0.3754 -0.6406 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 5.1405 -1.4474 -0.7519 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 6.4200 -1.3648 -0.2510 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 2.8696 -0.4259 -1.1981 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1.8257 -1.1216 -0.2911 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0.4579 -0.4915 -0.4046 N 0 3 0 0 0 0 0 0 0 0 0 0\n",
" 0.3051 0.7102 0.4947 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -1.0550 1.3967 0.2648 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -2.2149 0.4047 0.4938 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -3.5516 1.1154 0.3398 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -3.6597 2.3384 -0.1560 N 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -4.9822 2.7066 -0.1222 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.6589 3.8678 -0.4994 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.0104 3.8882 -0.3066 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.6867 2.7902 0.2442 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.0204 1.6533 0.6115 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.6449 1.6396 0.4122 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -4.7772 0.6249 0.7039 N 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.0969 -0.6936 1.2805 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.7544 -1.6506 0.2687 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.1570 -1.3508 0.1409 O 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.8157 -2.1358 -0.8719 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -8.0664 -3.5888 -0.4305 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -2.0347 -0.8020 -0.4527 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -0.6674 -1.4778 -0.2106 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 8.6674 1.2130 1.5547 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 8.5347 -1.1968 2.0090 C 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 6.3050 1.6912 0.9277 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 4.1053 1.5561 0.0654 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 4.8338 -2.2874 -1.1997 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 7.0359 -2.1479 -0.3394 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 2.8966 -0.9793 -2.2632 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 2.5261 0.7049 -1.4059 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 2.1769 -1.0592 0.8554 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1.7860 -2.2827 -0.5948 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1.1795 1.5072 0.2972 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0.3639 0.3457 1.6366 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -1.0987 1.8051 -0.8626 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -1.1697 2.3186 1.0246 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -2.1182 0.0217 1.6263 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.1755 4.6506 -0.8916 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.5289 4.7038 -0.5629 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -8.6774 2.8395 0.3711 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.5001 0.8690 1.0046 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.8561 -0.5420 2.1976 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -4.0999 -1.2012 1.7107 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.6221 -2.7718 0.6720 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -5.2093 -1.5356 -0.7938 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.2075 -2.1167 -1.9063 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -8.8901 -1.6361 -1.0610 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -8.8349 -4.1035 -1.1949 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -7.0425 -4.2136 -0.4354 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -8.5401 -3.5931 0.6717 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -2.8938 -1.6177 -0.2712 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -2.0897 -0.4196 -1.5890 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -0.6317 -1.8910 0.9154 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" -0.5640 -2.4018 -0.9707 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 9.8139 1.1999 1.9081 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 8.5102 2.0951 0.7565 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 7.9603 1.4124 2.5038 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 9.6845 -1.1767 2.3518 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 8.2672 -2.2854 1.5804 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 7.8330 -0.9616 2.9539 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0.3745 -0.1941 -1.2622 H 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 2 1 1 0\n",
" 3 2 2 0\n",
" 4 2 1 0\n",
" 5 4 1 0\n",
" 6 5 2 0\n",
" 7 6 1 0\n",
" 8 7 2 0\n",
" 9 8 1 0\n",
" 10 9 2 0\n",
" 5 10 1 0\n",
" 11 8 1 0\n",
" 12 11 1 0\n",
" 13 12 1 0\n",
" 14 13 1 0\n",
" 15 14 1 0\n",
" 16 15 1 0\n",
" 17 16 1 0\n",
" 18 17 2 0\n",
" 19 18 1 0\n",
" 20 19 2 0\n",
" 21 20 1 0\n",
" 22 21 2 0\n",
" 23 22 1 0\n",
" 24 23 2 0\n",
" 19 24 1 0\n",
" 25 24 1 0\n",
" 17 25 1 0\n",
" 26 25 1 0\n",
" 27 26 1 0\n",
" 28 27 1 0\n",
" 29 28 1 0\n",
" 30 29 1 0\n",
" 31 16 1 0\n",
" 32 31 1 0\n",
" 13 32 1 0\n",
" 33 4 1 0\n",
" 34 4 1 0\n",
" 6 35 1 0\n",
" 7 36 1 0\n",
" 9 37 1 0\n",
" 10 38 1 0\n",
" 11 39 1 0\n",
" 11 40 1 0\n",
" 12 41 1 0\n",
" 12 42 1 0\n",
" 14 43 1 0\n",
" 14 44 1 0\n",
" 15 45 1 0\n",
" 15 46 1 0\n",
" 16 47 1 0\n",
" 20 48 1 0\n",
" 21 49 1 0\n",
" 22 50 1 0\n",
" 23 51 1 0\n",
" 26 52 1 0\n",
" 26 53 1 0\n",
" 27 54 1 0\n",
" 27 55 1 0\n",
" 29 56 1 0\n",
" 29 57 1 0\n",
" 30 58 1 0\n",
" 30 59 1 0\n",
" 30 60 1 0\n",
" 31 61 1 0\n",
" 31 62 1 0\n",
" 32 63 1 0\n",
" 32 64 1 0\n",
" 33 65 1 0\n",
" 33 66 1 0\n",
" 33 67 1 0\n",
" 34 68 1 0\n",
" 34 69 1 0\n",
" 34 70 1 0\n",
" 13 71 1 0\n",
"M CHG 2 1 -1 13 1\n",
"M END\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x146b65506350>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol2D = Chem.RemoveHs(mol)\n",
"rdDepictor.Compute2DCoords(mol2D)\n",
"mol2D"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"query = Chem.MolFromSmarts(\"C1C[N+]CCC1c1nc2ccccc2n1\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the bit that we want to constrain:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x146b65506c10>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"match = mol.GetSubstructMatch(query)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14, 13, 12, 31, 30, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"match"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"conf = mol.GetConformer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate the `coord_map` that we will constrain:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"coord_map = {m: conf.GetAtomPosition(m) for m in match}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function does the multiple embedding, and it aligns\n",
"the generated conformers on the constrained scaffold after embedding. \n",
"As there may be conformational enantiomers I pick the mirror image\n",
"which yields the lowest RMSD. \n",
"Note that I had to pick a quite special seed to trigger\n",
"the behaviour I am going to describe in the following."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_confs_as_mol(mol, coord_map={}):\n",
" mol_confs = Chem.Mol(mol)\n",
" rdDistGeom.EmbedMultipleConfs(mol_confs,\n",
" randomSeed = 82208,\n",
" coordMap = coord_map,\n",
" useBasicKnowledge=True,\n",
" useExpTorsionAnglePrefs=True)\n",
" mol_template = Chem.Mol(mol)\n",
" mol_template.RemoveAllConformers()\n",
" confs_as_mols = []\n",
" for c in mol_confs.GetConformers():\n",
" molal = Chem.Mol(mol_template)\n",
" cids = [molal.AddConformer(c, assignId=True) for i in range(2)]\n",
" rmsd, trans = min([rdMolAlign.GetAlignmentTransform(molal, mol,\n",
" prbCid=cids[i], atomMap=list(zip(match, match)),\n",
" reflect=bool(i)) for i in (0, 1)])\n",
" conf_as_mol = Chem.Mol(mol_template)\n",
" conf_as_mol.AddConformer(c, assignId=True)\n",
" rdMolTransforms.TransformConformer(conf_as_mol.GetConformer(), trans)\n",
" confs_as_mols.append(conf_as_mol)\n",
" return confs_as_mols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function to merge a series of mols into one"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def merge_mols(mols):\n",
" mol = mols[0]\n",
" for m in mols[1:]:\n",
" mol = rdmolops.CombineMols(mol, m)\n",
" return mol"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function to display results using `py3Dmol`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def drawit(m, p=None, confId=-1):\n",
" mb = Chem.MolToMolBlock(m, confId=confId)\n",
" if p is None:\n",
" p = py3Dmol.view(width=400, height=400)\n",
" p.removeAllModels()\n",
" p.addModel(mb, 'sdf')\n",
" p.setStyle({'stick':{}})\n",
" p.setBackgroundColor('0xeeeeee')\n",
" p.zoomTo()\n",
" return p.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's do a multiple embedding without any constraint. \n",
"As expected, the scaffold shows high variability across conformations as it was not constrained."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"normal = get_confs_as_mol(mol)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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0\\n316315 2 0\\n311316 1 0\\n317314 1 0\\n318317 1 0\\n319318 1 0\\n320319 1 0\\n321320 1 0\\n322321 1 0\\n323322 1 0\\n324323 2 0\\n325324 1 0\\n326325 2 0\\n327326 1 0\\n328327 2 0\\n329328 1 0\\n330329 2 0\\n325330 1 0\\n331330 1 0\\n323331 1 0\\n332331 1 0\\n333332 1 0\\n334333 1 0\\n335334 1 0\\n336335 1 0\\n337322 1 0\\n338337 1 0\\n319338 1 0\\n339310 1 0\\n340310 1 0\\nM CHG 8 1 -1 13 1 35 -1 47 1 69 -1 81 1 103 -1 115 1\\nM CHG 8 137 -1 149 1 171 -1 183 1 205 -1 217 1 239 -1 251 1\\nM CHG 4 273 -1 285 1 307 -1 319 1\\nM END\\n\",\"sdf\");\n",
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"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drawit(merge_mols(normal))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This time we do a constraned embedding. As expected, 3D coordinates are much more consistent on the constrained scaffold, as expected. \n",
"However, at a closer look, you may see that there is an outlier:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with_coordmap = get_confs_as_mol(mol, coord_map)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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0\\n240239 1 0\\n241240 2 0\\n242240 1 0\\n243242 1 0\\n244243 2 0\\n245244 1 0\\n246245 2 0\\n247246 1 0\\n248247 2 0\\n243248 1 0\\n249246 1 0\\n250249 1 0\\n251250 1 0\\n252251 1 0\\n253252 1 0\\n254253 1 0\\n255254 1 0\\n256255 2 0\\n257256 1 0\\n258257 2 0\\n259258 1 0\\n260259 2 0\\n261260 1 0\\n262261 2 0\\n257262 1 0\\n263262 1 0\\n255263 1 0\\n264263 1 0\\n265264 1 0\\n266265 1 0\\n267266 1 0\\n268267 1 0\\n269254 1 0\\n270269 1 0\\n251270 1 0\\n271242 1 0\\n272242 1 0\\n274273 1 0\\n275274 2 0\\n276274 1 0\\n277276 1 0\\n278277 2 0\\n279278 1 0\\n280279 2 0\\n281280 1 0\\n282281 2 0\\n277282 1 0\\n283280 1 0\\n284283 1 0\\n285284 1 0\\n286285 1 0\\n287286 1 0\\n288287 1 0\\n289288 1 0\\n290289 2 0\\n291290 1 0\\n292291 2 0\\n293292 1 0\\n294293 2 0\\n295294 1 0\\n296295 2 0\\n291296 1 0\\n297296 1 0\\n289297 1 0\\n298297 1 0\\n299298 1 0\\n300299 1 0\\n301300 1 0\\n302301 1 0\\n303288 1 0\\n304303 1 0\\n285304 1 0\\n305276 1 0\\n306276 1 0\\n308307 1 0\\n309308 2 0\\n310308 1 0\\n311310 1 0\\n312311 2 0\\n313312 1 0\\n314313 2 0\\n315314 1 0\\n316315 2 0\\n311316 1 0\\n317314 1 0\\n318317 1 0\\n319318 1 0\\n320319 1 0\\n321320 1 0\\n322321 1 0\\n323322 1 0\\n324323 2 0\\n325324 1 0\\n326325 2 0\\n327326 1 0\\n328327 2 0\\n329328 1 0\\n330329 2 0\\n325330 1 0\\n331330 1 0\\n323331 1 0\\n332331 1 0\\n333332 1 0\\n334333 1 0\\n335334 1 0\\n336335 1 0\\n337322 1 0\\n338337 1 0\\n319338 1 0\\n339310 1 0\\n340310 1 0\\nM CHG 8 1 -1 13 1 35 -1 47 1 69 -1 81 1 103 -1 115 1\\nM CHG 8 137 -1 149 1 171 -1 183 1 205 -1 217 1 239 -1 251 1\\nM CHG 4 273 -1 285 1 307 -1 319 1\\nM END\\n\",\"sdf\");\n",
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"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drawit(merge_mols(with_coordmap))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In fact, the first 9 molecules are all chairs, with almost identical 3D coordinates:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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0\\n223222 1 0\\n224223 2 0\\n225224 1 0\\n226225 2 0\\n227226 1 0\\n228227 2 0\\n223228 1 0\\n229228 1 0\\n221229 1 0\\n230229 1 0\\n231230 1 0\\n232231 1 0\\n233232 1 0\\n234233 1 0\\n235220 1 0\\n236235 1 0\\n217236 1 0\\n237208 1 0\\n238208 1 0\\n240239 1 0\\n241240 2 0\\n242240 1 0\\n243242 1 0\\n244243 2 0\\n245244 1 0\\n246245 2 0\\n247246 1 0\\n248247 2 0\\n243248 1 0\\n249246 1 0\\n250249 1 0\\n251250 1 0\\n252251 1 0\\n253252 1 0\\n254253 1 0\\n255254 1 0\\n256255 2 0\\n257256 1 0\\n258257 2 0\\n259258 1 0\\n260259 2 0\\n261260 1 0\\n262261 2 0\\n257262 1 0\\n263262 1 0\\n255263 1 0\\n264263 1 0\\n265264 1 0\\n266265 1 0\\n267266 1 0\\n268267 1 0\\n269254 1 0\\n270269 1 0\\n251270 1 0\\n271242 1 0\\n272242 1 0\\n274273 1 0\\n275274 2 0\\n276274 1 0\\n277276 1 0\\n278277 2 0\\n279278 1 0\\n280279 2 0\\n281280 1 0\\n282281 2 0\\n277282 1 0\\n283280 1 0\\n284283 1 0\\n285284 1 0\\n286285 1 0\\n287286 1 0\\n288287 1 0\\n289288 1 0\\n290289 2 0\\n291290 1 0\\n292291 2 0\\n293292 1 0\\n294293 2 0\\n295294 1 0\\n296295 2 0\\n291296 1 0\\n297296 1 0\\n289297 1 0\\n298297 1 0\\n299298 1 0\\n300299 1 0\\n301300 1 0\\n302301 1 0\\n303288 1 0\\n304303 1 0\\n285304 1 0\\n305276 1 0\\n306276 1 0\\nM CHG 8 1 -1 13 1 35 -1 47 1 69 -1 81 1 103 -1 115 1\\nM CHG 8 137 -1 149 1 171 -1 183 1 205 -1 217 1 239 -1 251 1\\nM CHG 2 273 -1 285 1\\nM END\\n\",\"sdf\");\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drawit(merge_mols(with_coordmap[:9]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, the 10th molecule is a boat:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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},
"metadata": {},
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}
],
"source": [
"drawit(with_coordmap[9])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am not complaining, especially since one could easily filter out any conformation which upon alignment has an RMSD higher than a threshold. \n",
"However, I think this can be useful for future reference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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