Skip to content

Instantly share code, notes, and snippets.

@ptosco

ptosco/o3a_score_only.ipynb

Created November 26, 2019 21:47
Show Gist options
  • Save ptosco/40c2530c67d9c0930b8efbc8c92da0be to your computer and use it in GitHub Desktop.
Save ptosco/40c2530c67d9c0930b8efbc8c92da0be to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
o3a_score_only.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem\n",
"from rdkit.Chem.Draw import IPythonConsole\n",
"import random\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ache_2 = \"\"\"\\\n",
"1-3e\n",
" Cerius2 12110213523D 1 1.00000 \n",
"Structure written by Cerius2 SD Exporter\n",
" 60 63 0 0 0 0 0 0 0 0999 V2000\n",
" 5.1741 63.7666 67.2298 C 0 0 0 0 0 0\n",
" 4.3855 65.0953 67.1679 C 0 0 0 0 0 0\n",
" 5.2770 66.2628 67.6398 C 0 0 0 0 0 0\n",
" 4.5857 67.6253 67.4818 C 0 0 0 0 0 0\n",
" 4.1925 67.8073 66.0805 N 0 3 0 0 0 0\n",
" 3.1996 66.8221 65.6392 C 0 0 0 0 0 0\n",
" 3.8229 65.4102 65.7568 C 0 0 0 0 0 0\n",
" 4.3302 69.1509 65.5151 C 0 0 0 0 0 0\n",
" 4.2612 69.1992 64.0021 C 0 0 0 0 0 0\n",
" 3.0583 69.6122 63.4103 C 0 0 0 0 0 0\n",
" 2.9137 69.6073 62.0214 C 0 0 0 0 0 0\n",
" 3.9685 69.1928 61.2101 C 0 0 0 0 0 0\n",
" 5.1698 68.7828 61.7855 C 0 0 0 0 0 0\n",
" 5.3166 68.7866 63.1737 C 0 0 0 0 0 0\n",
" 5.6679 63.6802 68.2153 H 0 0 0 0 0 0\n",
" 5.9893 63.7965 66.4837 H 0 0 0 0 0 0\n",
" 3.5310 65.0411 67.8689 H 0 0 0 0 0 0\n",
" 5.5824 66.1197 68.6906 H 0 0 0 0 0 0\n",
" 6.2068 66.2717 67.0398 H 0 0 0 0 0 0\n",
" 3.7145 67.7004 68.1590 H 0 0 0 0 0 0\n",
" 5.2829 68.4214 67.7923 H 0 0 0 0 0 0\n",
" 2.2727 66.8895 66.2384 H 0 0 0 0 0 0\n",
" 2.9028 66.9965 64.5928 H 0 0 0 0 0 0\n",
" 4.6504 65.3441 65.0247 H 0 0 0 0 0 0\n",
" 3.0777 64.6579 65.4492 H 0 0 0 0 0 0\n",
" 3.5737 69.8400 65.9317 H 0 0 0 0 0 0\n",
" 5.3143 69.5451 65.8234 H 0 0 0 0 0 0\n",
" 2.2062 69.9310 64.0444 H 0 0 0 0 0 0\n",
" 1.9555 69.9248 61.5679 H 0 0 0 0 0 0\n",
" 3.8512 69.1849 60.1094 H 0 0 0 0 0 0\n",
" 6.0072 68.4449 61.1453 H 0 0 0 0 0 0\n",
" 6.2681 68.4425 63.6279 H 0 0 0 0 0 0\n",
" 4.3640 62.4886 66.9831 C 0 0 0 0 0 0\n",
" 3.3310 62.2808 67.9859 N 0 0 0 0 0 0\n",
" 3.5065 61.3987 69.0541 C 0 0 0 0 0 0\n",
" 4.5000 60.6868 69.1648 O 0 0 0 0 0 0\n",
" 2.3360 61.2610 69.9910 C 0 0 0 0 0 0\n",
" 1.6635 62.3620 70.5477 C 0 0 0 0 0 0\n",
" 0.5511 62.1510 71.3717 C 0 0 0 0 0 0\n",
" 0.0779 60.8542 71.6144 N 0 0 0 0 0 0\n",
" 0.7186 59.7598 71.0147 C 0 0 0 0 0 0\n",
" 1.8345 59.9629 70.1974 C 0 0 0 0 0 0\n",
" 3.8980 62.5103 65.9824 H 0 0 0 0 0 0\n",
" 5.0400 61.6176 66.9795 H 0 0 0 0 0 0\n",
" 1.9908 63.3965 70.3365 H 0 0 0 0 0 0\n",
" 0.0324 63.0186 71.8235 H 0 0 0 0 0 0\n",
" 0.3312 58.7358 71.1801 H 0 0 0 0 0 0\n",
" 2.3313 59.0997 69.7146 H 0 0 0 0 0 0\n",
" 4.9170 67.4352 65.6976 H 0 0 0 0 0 0\n",
" 2.1042 62.9814 67.6870 C 0 0 0 0 0 0\n",
" 1.8528 64.2567 68.2543 C 0 0 0 0 0 0\n",
" 2.5775 64.7082 68.9631 H 0 0 0 0 0 0\n",
" 0.6840 64.9680 67.9645 C 0 0 0 0 0 0\n",
" 0.5083 65.9542 68.4393 H 0 0 0 0 0 0\n",
" -0.2705 64.4289 67.0940 C 0 0 0 0 0 0\n",
" -1.2049 64.9815 66.8771 H 0 0 0 0 0 0\n",
" -0.0553 63.1639 66.5409 C 0 0 0 0 0 0\n",
" -0.8215 62.7141 65.8773 H 0 0 0 0 0 0\n",
" 1.1155 62.4477 66.8318 C 0 0 0 0 0 0\n",
" 1.2206 61.4326 66.3976 H 0 0 0 0 0 0\n",
" 1 2 1 0 0 0\n",
" 1 15 1 0 0 0\n",
" 1 16 1 0 0 0\n",
" 1 33 1 0 0 0\n",
" 2 3 1 0 0 0\n",
" 2 7 1 0 0 0\n",
" 2 17 1 0 0 0\n",
" 3 4 1 0 0 0\n",
" 3 18 1 0 0 0\n",
" 3 19 1 0 0 0\n",
" 4 5 1 0 0 0\n",
" 4 20 1 0 0 0\n",
" 4 21 1 0 0 0\n",
" 5 6 1 0 0 0\n",
" 5 8 1 0 0 0\n",
" 5 49 1 0 0 0\n",
" 6 7 1 0 0 0\n",
" 6 22 1 0 0 0\n",
" 6 23 1 0 0 0\n",
" 7 24 1 0 0 0\n",
" 7 25 1 0 0 0\n",
" 8 9 1 0 0 0\n",
" 8 26 1 0 0 0\n",
" 8 27 1 0 0 0\n",
" 9 10 1 0 0 0\n",
" 9 14 2 0 0 0\n",
" 10 11 2 0 0 0\n",
" 10 28 1 0 0 0\n",
" 11 12 1 0 0 0\n",
" 11 29 1 0 0 0\n",
" 12 13 2 0 0 0\n",
" 12 30 1 0 0 0\n",
" 13 14 1 0 0 0\n",
" 13 31 1 0 0 0\n",
" 14 32 1 0 0 0\n",
" 33 34 1 0 0 0\n",
" 33 43 1 0 0 0\n",
" 33 44 1 0 0 0\n",
" 34 35 1 0 0 0\n",
" 34 50 1 0 0 0\n",
" 35 36 2 0 0 0\n",
" 35 37 1 0 0 0\n",
" 37 38 2 0 0 0\n",
" 37 42 1 0 0 0\n",
" 38 39 1 0 0 0\n",
" 38 45 1 0 0 0\n",
" 39 40 2 0 0 0\n",
" 39 46 1 0 0 0\n",
" 40 41 1 0 0 0\n",
" 41 42 2 0 0 0\n",
" 41 47 1 0 0 0\n",
" 42 48 1 0 0 0\n",
" 50 51 1 0 0 0\n",
" 50 59 2 0 0 0\n",
" 51 52 1 0 0 0\n",
" 51 53 2 0 0 0\n",
" 53 54 1 0 0 0\n",
" 53 55 1 0 0 0\n",
" 55 56 1 0 0 0\n",
" 55 57 2 0 0 0\n",
" 57 58 1 0 0 0\n",
" 57 59 1 0 0 0\n",
" 59 60 1 0 0 0\n",
"M CHG 1 5 1\n",
"M END\n",
"> <ACTIVITY>\n",
"7.19\n",
"\n",
"> <SET>\n",
"2\n",
"\n",
"> <Min_dist>\n",
"0.09\n",
"\n",
"> <Avg_dist>\n",
"0.41\n",
"\n",
"$$$$\n",
"2-34\n",
" Cerius2 12110213523D 1 1.00000 \n",
"Structure written by Cerius2 SD Exporter\n",
" 51 53 0 0 0 0 0 0 0 0999 V2000\n",
" 5.1741 63.7666 67.2298 C 0 0 0 0 0 0\n",
" 4.3855 65.0953 67.1679 C 0 0 0 0 0 0\n",
" 5.2770 66.2628 67.6398 C 0 0 0 0 0 0\n",
" 4.5857 67.6253 67.4818 C 0 0 0 0 0 0\n",
" 4.1925 67.8073 66.0805 N 0 3 0 0 0 0\n",
" 3.1996 66.8221 65.6392 C 0 0 0 0 0 0\n",
" 3.8229 65.4102 65.7568 C 0 0 0 0 0 0\n",
" 4.3302 69.1509 65.5151 C 0 0 0 0 0 0\n",
" 4.2612 69.1992 64.0021 C 0 0 0 0 0 0\n",
" 3.4089 68.9383 62.7868 C 0 0 0 0 0 0\n",
" 4.8921 68.6128 62.7679 C 0 0 0 0 0 0\n",
" 5.6679 63.6802 68.2153 H 0 0 0 0 0 0\n",
" 5.9893 63.7965 66.4837 H 0 0 0 0 0 0\n",
" 3.5310 65.0411 67.8689 H 0 0 0 0 0 0\n",
" 5.5824 66.1197 68.6906 H 0 0 0 0 0 0\n",
" 6.2068 66.2717 67.0398 H 0 0 0 0 0 0\n",
" 3.7145 67.7004 68.1590 H 0 0 0 0 0 0\n",
" 5.2829 68.4214 67.7923 H 0 0 0 0 0 0\n",
" 2.2727 66.8895 66.2384 H 0 0 0 0 0 0\n",
" 2.9028 66.9965 64.5928 H 0 0 0 0 0 0\n",
" 4.6504 65.3441 65.0247 H 0 0 0 0 0 0\n",
" 3.0777 64.6579 65.4492 H 0 0 0 0 0 0\n",
" 3.5737 69.8400 65.9317 H 0 0 0 0 0 0\n",
" 5.3143 69.5451 65.8234 H 0 0 0 0 0 0\n",
" 2.6592 68.1505 62.6898 H 0 0 0 0 0 0\n",
" 4.3640 62.4886 66.9831 C 0 0 0 0 0 0\n",
" 3.3310 62.2808 67.9859 N 0 0 0 0 0 0\n",
" 3.5065 61.3987 69.0541 C 0 0 0 0 0 0\n",
" 4.5000 60.6868 69.1648 O 0 0 0 0 0 0\n",
" 2.3360 61.2610 69.9910 C 0 0 0 0 0 0\n",
" 2.1816 60.0574 70.7052 C 0 0 0 0 0 0\n",
" 1.0937 59.8814 71.5627 C 0 0 0 0 0 0\n",
" 0.1451 60.9030 71.7211 C 0 0 0 0 0 0\n",
" 0.3027 62.1039 71.0182 C 0 0 0 0 0 0\n",
" 1.3932 62.2913 70.1618 C 0 0 0 0 0 0\n",
" 3.8980 62.5103 65.9824 H 0 0 0 0 0 0\n",
" 5.0400 61.6176 66.9795 H 0 0 0 0 0 0\n",
" 1.9897 62.5131 67.4053 C 0 0 0 0 0 0\n",
" 2.9204 59.2427 70.5798 H 0 0 0 0 0 0\n",
" 0.9794 58.9301 72.1176 H 0 0 0 0 0 0\n",
" -0.7170 60.7611 72.4002 H 0 0 0 0 0 0\n",
" -0.4380 62.9164 71.1493 H 0 0 0 0 0 0\n",
" 1.5043 63.2620 69.6464 H 0 0 0 0 0 0\n",
" 4.9170 67.4352 65.6976 H 0 0 0 0 0 0\n",
" 1.2417 61.8253 67.8173 H 0 0 0 0 0 0\n",
" 1.6688 63.5555 67.5482 H 0 0 0 0 0 0\n",
" 1.9674 62.3016 66.3226 H 0 0 0 0 0 0\n",
" 5.5427 69.2852 62.1965 H 0 0 0 0 0 0\n",
" 4.4766 70.2822 63.8998 H 0 0 0 0 0 0\n",
" 3.1023 69.8390 62.2409 H 0 0 0 0 0 0\n",
" 5.2248 67.5746 62.6917 H 0 0 0 0 0 0\n",
" 1 2 1 0 0 0\n",
" 1 12 1 0 0 0\n",
" 1 13 1 0 0 0\n",
" 1 26 1 0 0 0\n",
" 2 3 1 0 0 0\n",
" 2 7 1 0 0 0\n",
" 2 14 1 0 0 0\n",
" 3 4 1 0 0 0\n",
" 3 15 1 0 0 0\n",
" 3 16 1 0 0 0\n",
" 4 5 1 0 0 0\n",
" 4 17 1 0 0 0\n",
" 4 18 1 0 0 0\n",
" 5 6 1 0 0 0\n",
" 5 8 1 0 0 0\n",
" 5 44 1 0 0 0\n",
" 6 7 1 0 0 0\n",
" 6 19 1 0 0 0\n",
" 6 20 1 0 0 0\n",
" 7 21 1 0 0 0\n",
" 7 22 1 0 0 0\n",
" 8 9 1 0 0 0\n",
" 8 23 1 0 0 0\n",
" 8 24 1 0 0 0\n",
" 9 10 1 0 0 0\n",
" 9 11 1 0 0 0\n",
" 9 49 1 0 0 0\n",
" 10 11 1 0 0 0\n",
" 10 25 1 0 0 0\n",
" 10 50 1 0 0 0\n",
" 11 48 1 0 0 0\n",
" 11 51 1 0 0 0\n",
" 26 27 1 0 0 0\n",
" 26 36 1 0 0 0\n",
" 26 37 1 0 0 0\n",
" 27 28 1 0 0 0\n",
" 27 38 1 0 0 0\n",
" 28 29 2 0 0 0\n",
" 28 30 1 0 0 0\n",
" 30 31 2 0 0 0\n",
" 30 35 1 0 0 0\n",
" 31 32 1 0 0 0\n",
" 31 39 1 0 0 0\n",
" 32 33 2 0 0 0\n",
" 32 40 1 0 0 0\n",
" 33 34 1 0 0 0\n",
" 33 41 1 0 0 0\n",
" 34 35 2 0 0 0\n",
" 34 42 1 0 0 0\n",
" 35 43 1 0 0 0\n",
" 38 45 1 0 0 0\n",
" 38 46 1 0 0 0\n",
" 38 47 1 0 0 0\n",
"M CHG 1 5 1\n",
"M END\n",
"> <ACTIVITY>\n",
"4.42\n",
"\n",
"> <SET>\n",
"1\n",
"\n",
"> <Min_dist>\n",
"999.9\n",
"\n",
"> <Avg_dist>\n",
"999.9\n",
"\n",
"$$$$\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"suppl = Chem.SDMolSupplier()\n",
"suppl.SetData(ache_2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"prb = suppl[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ref = suppl[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's shuffle the `prb` atoms to make sure the alignment algorithm is doind its job:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"atom_idx_list = list(range(prb.GetNumAtoms()))\n",
"random.seed(1)\n",
"random.shuffle(atom_idx_list)\n",
"prb = AllChem.RenumberAtoms(prb, atom_idx_list)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"prb = Chem.AddHs(prb, addCoords=True)\n",
"ref = Chem.AddHs(ref, addCoords=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"w = Chem.SDWriter(\"ache_3dqsar_ren.sdf\")\n",
"w.write(ref)\n",
"w.write(prb)\n",
"w.close()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"o3a = AllChem.GetO3A(prb, ref, options=3, maxIters=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the score \"in place\":"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"179.47884843543616"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"o3a.Score()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the RMSD between matching atoms:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.36493268557365655"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sqrt(sum([(prb.GetConformer().GetAtomPosition(i)\n",
" - ref.GetConformer().GetAtomPosition(j)).LengthSq()\n",
" for i, j in o3a.Matches()]) / len(o3a.Matches()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the matches:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2, 14)\n",
"(5, 17)\n",
"(6, 2)\n",
"(7, 6)\n",
"(9, 8)\n",
"(10, 20)\n",
"(11, 10)\n",
"(13, 15)\n",
"(14, 11)\n",
"(15, 1)\n",
"(16, 21)\n",
"(17, 18)\n",
"(18, 7)\n",
"(20, 19)\n",
"(21, 12)\n",
"(22, 13)\n",
"(23, 4)\n",
"(24, 9)\n",
"(25, 3)\n",
"(26, 22)\n",
"(29, 16)\n",
"(30, 5)\n"
]
}
],
"source": [
"[print((i+1, j+1)) for i, j in sorted(o3a.Matches())];"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The match seems correct:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"data:image/png;base64,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\">"
]
}
],
"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
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment