{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Import the predictor" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath(os.path.join('..')))\n", "from Code.predictor import ScopeBO\n", "import numpy as np\n", "import pandas as pd\n", "from rdkit import Chem\n", "from rdkit.Chem import Draw" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating the search space" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we will generate a search space for the reaction of phosphines with alkyl iodides and start generating a scope." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Provide a list of samples for the first reactant" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Smiles for the first reactant:\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# provide the list of smiles for the alkyl iodide starting materials\n", "smiles1 = [\"CI\",\"CCI\",\"CCCI\",\"CCCCI\", \"C1CCCCC1I\",\"CC(C)I\",\"CC(C)(C)I\",\"CCC(C)(C)I\",\"CC(C)C(C)I\",\"OCCI\",\"ClCCI\",\"BrCCI\",\n", " \"ClCCCI\",\"BrCCCI\",\"CC(Br)CCI\"]\n", "\n", "# instead of providing the full list here, the list can also be read in from a csv file.\n", "# here is some example code for this (assuming the smiles are in the first column and there is a column header in the first line\n", "# (smiles starting on line 2))\n", "# if you want to use this, remove the hash at the beginning of the next line\n", "#smiles1 = pd.read_csv(\"filename.csv\",index_col=0,header=0).index.to_list()\n", "\n", "# visualize the molecules\n", "mol1 = [Chem.MolFromSmiles(smiles) for smiles in smiles1]\n", "print(\"Smiles for the first reactant:\")\n", "Draw.MolsToGridImage(mol1,molsPerRow=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate features using the smiles list" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This might take several minutes or even hours. Please stand by.\n", "Atom properties will be calculated for the following common substructure:\n" ] }, { "data": { "image/jpeg": 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SASAVolumeHOMOLUMOIPEADipoleC_1_BVC_1_SASAC_1_P_int...C_1_electrophilicityC_1_nucleophilicityC_1_radicalFukuiI_1_BVI_1_SASAI_1_P_intI_1_chargeI_1_electrophilicityI_1_nucleophilicityI_1_radicalFukui
ClCCI244.784847129.563460-0.421480-0.2547659.534655-1.0181800.8809550.4512794.52135925.338060...0.032131-0.0282240.0019530.19542892.17489819.467851-0.1228100.3675370.3620500.364793
BrCCI252.281108136.373085-0.424785-0.2659529.586052-0.4410510.1300450.4562354.27092426.443056...0.009899-0.028330-0.0092150.19433092.38129119.622270-0.1237480.3141340.3695060.341820
ClCCCI268.323472151.663739-0.410241-0.2400269.102629-1.5267621.4101440.4817525.40316025.106049...0.045916-0.0214740.0122210.20824288.21836819.926290-0.1461510.3390100.3417090.340360
BrCCCI276.758056159.516491-0.413236-0.2464819.143685-0.8651261.5236590.4740055.19164926.413554...0.025324-0.0206340.0023450.20650888.58142920.066888-0.1433700.2657310.3429160.304323
CC(Br)CCI300.675829182.962702-0.409901-0.2433278.987588-0.8682151.6280030.4889715.20239226.653934...0.020991-0.022475-0.0007420.20775588.26571020.336449-0.1468990.2515070.3247870.288147
CI186.64263484.788431-0.408318-0.23140910.177248-2.2804071.4514840.23267421.62050518.480703...0.029576-0.033723-0.0020740.162144100.84227918.404048-0.1386250.4714350.5740600.522747
CCI217.742410109.615562-0.404139-0.2330209.716175-2.0356351.5631010.3886166.43015021.681898...0.074667-0.0187430.0279620.19009793.14210519.075110-0.1558080.4434810.4644700.453975
CCCI244.670753133.235606-0.402517-0.2308919.407517-2.0242081.6023530.4425875.53200823.660328...0.072486-0.0238130.0243370.20514888.77889419.580352-0.1580430.4355970.4025160.419056
CCCCI274.839894157.324369-0.402234-0.2309299.090307-1.9971491.6206440.4497725.19476424.533272...0.072709-0.0212090.0257500.20282689.36958319.741944-0.1584530.4334990.3548480.394174
C1CCCCC1I293.073124185.722856-0.398283-0.2318738.843737-1.7548791.7492830.6119720.07695333.073760...0.102410-0.0023240.0500430.23404781.68074320.516212-0.1754030.4053330.3313800.368357
CC(C)I242.902322133.228637-0.400897-0.2351969.495922-1.7966491.6529880.5172500.24000627.931948...0.1065700.0049390.0557550.22075985.23692619.767512-0.1711180.4180760.4426520.430364
CC(C)(C)I262.917309156.223658-0.397556-0.2375759.334148-1.5738001.7246100.6269680.0000000.000000...0.1298500.0287600.0793050.25285777.96676220.472219-0.1847350.3952990.4452180.420258
CCC(C)(C)I284.212017178.359852-0.396083-0.2364749.075118-1.5504471.7533610.6739120.0000000.000000...0.1266190.0185800.0725990.27016173.60409120.819582-0.1865640.3883400.3826810.385511
CC(C)C(C)I285.875882178.408991-0.397896-0.2332149.058807-1.7330051.7292070.6204410.00705933.988431...0.101887-0.0012010.0503430.24301779.14576920.495287-0.1765940.4041070.3631350.383621
OCCI231.727427119.121559-0.414516-0.2429959.658411-1.7496811.2219790.4261275.79637922.940803...0.069161-0.0165150.0263230.20131989.46414119.340989-0.1401410.4421380.4004230.421281
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15 rows × 21 columns

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" ], "text/plain": [ " SASA Volume HOMO LUMO IP EA \\\n", "ClCCI 244.784847 129.563460 -0.421480 -0.254765 9.534655 -1.018180 \n", "BrCCI 252.281108 136.373085 -0.424785 -0.265952 9.586052 -0.441051 \n", "ClCCCI 268.323472 151.663739 -0.410241 -0.240026 9.102629 -1.526762 \n", "BrCCCI 276.758056 159.516491 -0.413236 -0.246481 9.143685 -0.865126 \n", "CC(Br)CCI 300.675829 182.962702 -0.409901 -0.243327 8.987588 -0.868215 \n", "CI 186.642634 84.788431 -0.408318 -0.231409 10.177248 -2.280407 \n", "CCI 217.742410 109.615562 -0.404139 -0.233020 9.716175 -2.035635 \n", "CCCI 244.670753 133.235606 -0.402517 -0.230891 9.407517 -2.024208 \n", "CCCCI 274.839894 157.324369 -0.402234 -0.230929 9.090307 -1.997149 \n", "C1CCCCC1I 293.073124 185.722856 -0.398283 -0.231873 8.843737 -1.754879 \n", "CC(C)I 242.902322 133.228637 -0.400897 -0.235196 9.495922 -1.796649 \n", "CC(C)(C)I 262.917309 156.223658 -0.397556 -0.237575 9.334148 -1.573800 \n", "CCC(C)(C)I 284.212017 178.359852 -0.396083 -0.236474 9.075118 -1.550447 \n", "CC(C)C(C)I 285.875882 178.408991 -0.397896 -0.233214 9.058807 -1.733005 \n", "OCCI 231.727427 119.121559 -0.414516 -0.242995 9.658411 -1.749681 \n", "\n", " Dipole C_1_BV C_1_SASA C_1_P_int ... \\\n", "ClCCI 0.880955 0.451279 4.521359 25.338060 ... \n", "BrCCI 0.130045 0.456235 4.270924 26.443056 ... \n", "ClCCCI 1.410144 0.481752 5.403160 25.106049 ... \n", "BrCCCI 1.523659 0.474005 5.191649 26.413554 ... \n", "CC(Br)CCI 1.628003 0.488971 5.202392 26.653934 ... \n", "CI 1.451484 0.232674 21.620505 18.480703 ... \n", "CCI 1.563101 0.388616 6.430150 21.681898 ... \n", "CCCI 1.602353 0.442587 5.532008 23.660328 ... \n", "CCCCI 1.620644 0.449772 5.194764 24.533272 ... \n", "C1CCCCC1I 1.749283 0.611972 0.076953 33.073760 ... \n", "CC(C)I 1.652988 0.517250 0.240006 27.931948 ... \n", "CC(C)(C)I 1.724610 0.626968 0.000000 0.000000 ... \n", "CCC(C)(C)I 1.753361 0.673912 0.000000 0.000000 ... \n", "CC(C)C(C)I 1.729207 0.620441 0.007059 33.988431 ... \n", "OCCI 1.221979 0.426127 5.796379 22.940803 ... \n", "\n", " C_1_electrophilicity C_1_nucleophilicity C_1_radicalFukui \\\n", "ClCCI 0.032131 -0.028224 0.001953 \n", "BrCCI 0.009899 -0.028330 -0.009215 \n", "ClCCCI 0.045916 -0.021474 0.012221 \n", "BrCCCI 0.025324 -0.020634 0.002345 \n", "CC(Br)CCI 0.020991 -0.022475 -0.000742 \n", "CI 0.029576 -0.033723 -0.002074 \n", "CCI 0.074667 -0.018743 0.027962 \n", "CCCI 0.072486 -0.023813 0.024337 \n", "CCCCI 0.072709 -0.021209 0.025750 \n", "C1CCCCC1I 0.102410 -0.002324 0.050043 \n", "CC(C)I 0.106570 0.004939 0.055755 \n", "CC(C)(C)I 0.129850 0.028760 0.079305 \n", "CCC(C)(C)I 0.126619 0.018580 0.072599 \n", "CC(C)C(C)I 0.101887 -0.001201 0.050343 \n", "OCCI 0.069161 -0.016515 0.026323 \n", "\n", " I_1_BV I_1_SASA I_1_P_int I_1_charge I_1_electrophilicity \\\n", "ClCCI 0.195428 92.174898 19.467851 -0.122810 0.367537 \n", "BrCCI 0.194330 92.381291 19.622270 -0.123748 0.314134 \n", "ClCCCI 0.208242 88.218368 19.926290 -0.146151 0.339010 \n", "BrCCCI 0.206508 88.581429 20.066888 -0.143370 0.265731 \n", "CC(Br)CCI 0.207755 88.265710 20.336449 -0.146899 0.251507 \n", "CI 0.162144 100.842279 18.404048 -0.138625 0.471435 \n", "CCI 0.190097 93.142105 19.075110 -0.155808 0.443481 \n", "CCCI 0.205148 88.778894 19.580352 -0.158043 0.435597 \n", "CCCCI 0.202826 89.369583 19.741944 -0.158453 0.433499 \n", "C1CCCCC1I 0.234047 81.680743 20.516212 -0.175403 0.405333 \n", "CC(C)I 0.220759 85.236926 19.767512 -0.171118 0.418076 \n", "CC(C)(C)I 0.252857 77.966762 20.472219 -0.184735 0.395299 \n", "CCC(C)(C)I 0.270161 73.604091 20.819582 -0.186564 0.388340 \n", "CC(C)C(C)I 0.243017 79.145769 20.495287 -0.176594 0.404107 \n", "OCCI 0.201319 89.464141 19.340989 -0.140141 0.442138 \n", "\n", " I_1_nucleophilicity I_1_radicalFukui \n", "ClCCI 0.362050 0.364793 \n", "BrCCI 0.369506 0.341820 \n", "ClCCCI 0.341709 0.340360 \n", "BrCCCI 0.342916 0.304323 \n", "CC(Br)CCI 0.324787 0.288147 \n", "CI 0.574060 0.522747 \n", "CCI 0.464470 0.453975 \n", "CCCI 0.402516 0.419056 \n", "CCCCI 0.354848 0.394174 \n", "C1CCCCC1I 0.331380 0.368357 \n", "CC(C)I 0.442652 0.430364 \n", "CC(C)(C)I 0.445218 0.420258 \n", "CCC(C)(C)I 0.382681 0.385511 \n", "CC(C)C(C)I 0.363135 0.383621 \n", "OCCI 0.400423 0.421281 \n", "\n", "[15 rows x 21 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ScopeBO.generate_features(smiles1, # list of smiles (see prior cell)\n", " common_core=\"CI\", # provide the SMARTS for the shared atoms for which atom descriptors will be calculated\n", " filename=\"reactant1.csv\") # name for the created file with the descriptors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Smiles list for the second reactant" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Smiles for the second reactant:\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# smiles list for the second reactant (phosphines)\n", "smiles2 = [\"P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3\",\n", " \"CC(C)(C)P(C(C)(C)C)C(C)(C)C\",\n", " \"P(C)(C)C\",\n", " \"P(CC)(CC)CC\",\n", " \"P(CCC)(CCC)CCC\",\n", " \"P(C(C)C)(C(C)C)C(C)C\"]\n", "\n", "# look at the structures\n", "mol1 = [Chem.MolFromSmiles(smiles) for smiles in smiles2]\n", "print(\"Smiles for the second reactant:\")\n", "Draw.MolsToGridImage(mol1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate features for the second reactant" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This might take several minutes or even hours. Please stand by.\n", "Atom properties will be calculated for the following common substructure:\n" ] }, { "data": { "image/jpeg": 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P(C(C)C)(C(C)C)C(C)C364.585150263.550839-0.355563-0.1533867.520335-3.1519060.6202330.6380170.07200222.730716...-0.055786-0.028966-0.0423760.6391310.01200030.934305-0.004396-0.053739-0.028450-0.041094
\n", "

6 rows × 35 columns

\n", "
" ], "text/plain": [ " SASA Volume HOMO \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 476.095803 335.813928 -0.366304 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C 393.599493 322.301921 -0.344248 \n", "P(C)(C)C 238.105644 129.102449 -0.369419 \n", "P(CC)(CC)CC 318.408940 199.428774 -0.365679 \n", "P(CCC)(CCC)CCC 408.510955 271.575185 -0.365362 \n", "P(C(C)C)(C(C)C)C(C)C 364.585150 263.550839 -0.355563 \n", "\n", " LUMO IP EA \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 -0.271541 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.153942 7.143167 -3.028522 \n", "P(C)(C)C -0.146797 8.230202 -3.527439 \n", "P(CC)(CC)CC -0.152998 7.922281 -3.281948 \n", "P(CCC)(CCC)CCC -0.152770 7.825907 -3.224675 \n", "P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 -3.151906 \n", "\n", " Dipole C_1_BV C_1_SASA \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.589617 0.723974 1.562037 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.529619 0.743253 0.000000 \n", "P(C)(C)C 0.745594 0.300963 15.755368 \n", "P(CC)(CC)CC 0.652916 0.478286 3.564288 \n", "P(CCC)(CCC)CCC 0.679366 0.532290 1.822806 \n", "P(C(C)C)(C(C)C)C(C)C 0.620233 0.638017 0.072002 \n", "\n", " C_1_P_int ... C_2_electrophilicity \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 32.958548 ... 0.003254 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.000000 ... -0.034989 \n", "P(C)(C)C 19.594742 ... -0.107096 \n", "P(CC)(CC)CC 23.715433 ... -0.079295 \n", "P(CCC)(CCC)CCC 28.380926 ... -0.078090 \n", "P(C(C)C)(C(C)C)C(C)C 22.730716 ... -0.055786 \n", "\n", " C_2_nucleophilicity C_2_radicalFukui \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.017481 -0.007113 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.015835 -0.025412 \n", "P(C)(C)C -0.074265 -0.090681 \n", "P(CC)(CC)CC -0.045760 -0.062527 \n", "P(CCC)(CCC)CCC -0.042809 -0.060449 \n", "P(C(C)C)(C(C)C)C(C)C -0.028966 -0.042376 \n", "\n", " C_3_BV C_3_SASA C_3_P_int \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.723990 1.564037 33.614078 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.743126 0.000000 0.000000 \n", "P(C)(C)C 0.300905 15.760368 19.681154 \n", "P(CC)(CC)CC 0.475761 3.597905 24.233962 \n", "P(CCC)(CCC)CCC 0.531249 2.237860 26.825917 \n", "P(C(C)C)(C(C)C)C(C)C 0.639131 0.012000 30.934305 \n", "\n", " C_3_charge C_3_electrophilicity \\\n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.016737 0.003259 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.036755 -0.034988 \n", "P(C)(C)C -0.101231 -0.107096 \n", "P(CC)(CC)CC -0.051128 -0.078738 \n", "P(CCC)(CCC)CCC -0.053282 -0.078345 \n", "P(C(C)C)(C(C)C)C(C)C -0.004396 -0.053739 \n", "\n", " C_3_nucleophilicity C_3_radicalFukui \n", "P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.017483 -0.007112 \n", "CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.015834 -0.025411 \n", "P(C)(C)C -0.074265 -0.090681 \n", "P(CC)(CC)CC -0.046153 -0.062445 \n", "P(CCC)(CCC)CCC -0.043254 -0.060800 \n", "P(C(C)C)(C(C)C)C(C)C -0.028450 -0.041094 \n", "\n", "[6 rows x 35 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ScopeBO.generate_features(smiles2, # list of smiles (see prior cell)\n", " common_core=\"[C,c]P([C,c])[C,c]\", # provide the SMARTS for the shared atoms for which atom descriptors will be calculated\n", " filename=\"reactant2.csv\") # name for the created file with the descriptors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the search space using the generated features. This will be also generate suggestions for three initial scope entries by random sampling. We however recommend to select the first three examples manually by choosing three entries that are assumed to perform well in the reaction and to start the algorithmic suggestions after the results for these samples have been added to the searchspace. See the SI of our publication for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that you can also use the main scope suggestion function (run — see below) to get initial random suggestions if you already have a search space file ready." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Now doing feature preprocessing.\n", "The following features were removed: ['AlkI_Volume', 'AlkI_IP', 'AlkI_C_1_nucleophilicity', 'AlkI_C_1_radicalFukui', 'AlkI_I_1_BV', 'AlkI_I_1_SASA', 'AlkI_I_1_P_int', 'AlkI_I_1_charge', 'Phos_EA', 'Phos_Dipole', 'Phos_C_1_BV', 'Phos_C_1_charge', 'Phos_C_1_nucleophilicity', 'Phos_C_1_radicalFukui', 'Phos_P_1_BV', 'Phos_P_1_SASA', 'Phos_P_1_P_int', 'Phos_P_1_electrophilicity', 'Phos_P_1_nucleophilicity', 'Phos_P_1_radicalFukui', 'Phos_C_2_BV', 'Phos_C_2_SASA', 'Phos_C_2_P_int', 'Phos_C_2_charge', 'Phos_C_2_electrophilicity', 'Phos_C_2_nucleophilicity', 'Phos_C_2_radicalFukui', 'Phos_C_3_BV', 'Phos_C_3_SASA', 'Phos_C_3_P_int', 'Phos_C_3_charge', 'Phos_C_3_electrophilicity', 'Phos_C_3_nucleophilicity', 'Phos_C_3_radicalFukui']\n", "The final search space has 22 features.\n", "Generation of reaction space completed!\n", "\n", "Suggesting initial scope entries by random sampling...\n", "Generated 3 initial samples using random sampling (random seed = 42). Run finished!\n", "Suggested samples are indicated by priority = 1.\n", "\n", "These are the suggested substrate combinations:\n" ] }, { "data": { "image/png": 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", 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" ], "text/plain": [ " AlkI_SASA AlkI_HOMO \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 262.917309 -0.397556 \n", "CCCCI.P(CC)(CC)CC 274.839894 -0.402234 \n", "ClCCCI.P(C)(C)C 268.323472 -0.410241 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 244.784847 -0.421480 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 293.073124 -0.398283 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 300.675829 -0.409901 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 300.675829 -0.409901 \n", "CC(Br)CCI.P(CC)(CC)CC 300.675829 -0.409901 \n", "CC(Br)CCI.P(C)(C)C 300.675829 -0.409901 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 231.727427 -0.414516 \n", "\n", " AlkI_LUMO AlkI_EA \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.237575 -1.573800 \n", "CCCCI.P(CC)(CC)CC -0.230929 -1.997149 \n", "ClCCCI.P(C)(C)C -0.240026 -1.526762 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.254765 -1.018180 \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.231873 -1.754879 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.243327 -0.868215 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 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"C1CCCCC1I.P(CCC)(CCC)CCC 0.076953 33.073760 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 5.202392 26.653934 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 5.202392 26.653934 \n", "CC(Br)CCI.P(CC)(CC)CC 5.202392 26.653934 \n", "CC(Br)CCI.P(C)(C)C 5.202392 26.653934 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 5.796379 22.940803 \n", "\n", " AlkI_C_1_charge \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.084700 \n", "CCCCI.P(CC)(CC)CC 0.001953 \n", "ClCCCI.P(C)(C)C -0.001758 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.006075 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.043606 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.001671 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.001671 \n", "CC(Br)CCI.P(CC)(CC)CC -0.001671 \n", "CC(Br)CCI.P(C)(C)C -0.001671 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.017554 \n", "\n", " AlkI_C_1_electrophilicity ... \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.129850 ... \n", "CCCCI.P(CC)(CC)CC 0.072709 ... \n", "ClCCCI.P(C)(C)C 0.045916 ... \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.032131 ... \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.102410 ... \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 0.020991 ... \n", "CC(Br)CCI.P(CCC)(CCC)CCC 0.020991 ... \n", "CC(Br)CCI.P(CC)(CC)CC 0.020991 ... \n", "CC(Br)CCI.P(C)(C)C 0.020991 ... \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 0.069161 ... \n", "\n", " Phos_Volume Phos_HOMO \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "CCCCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "ClCCCI.P(C)(C)C 129.102449 -0.369419 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "CC(Br)CCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "CC(Br)CCI.P(C)(C)C 129.102449 -0.369419 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "\n", " Phos_LUMO Phos_IP \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "CCCCI.P(CC)(CC)CC -0.152998 7.922281 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\n", "\n", " Phos_C_1_electrophilicity \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "CCCCI.P(CC)(CC)CC -0.079000 \n", "ClCCCI.P(C)(C)C -0.107096 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.078216 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.078216 \n", "CC(Br)CCI.P(CC)(CC)CC -0.079000 \n", "CC(Br)CCI.P(C)(C)C -0.107096 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "\n", " Phos_P_1_charge yield \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.136051 PENDING \n", "CCCCI.P(CC)(CC)CC -0.106588 PENDING \n", "ClCCCI.P(C)(C)C -0.094885 PENDING \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.126537 PENDING \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.136051 PENDING \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.126537 PENDING \n", "CC(Br)CCI.P(CC)(CC)CC -0.106588 PENDING \n", "CC(Br)CCI.P(C)(C)C -0.094885 PENDING \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.136051 PENDING \n", "\n", " priority \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 1.0 \n", "CCCCI.P(CC)(CC)CC 1.0 \n", "ClCCCI.P(C)(C)C 1.0 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 0 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 0 \n", "CC(Br)CCI.P(CC)(CC)CC 0 \n", "CC(Br)CCI.P(C)(C)C 0 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 0 \n", "\n", "[90 rows x 24 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = ScopeBO().create_reaction_space(\n", " reactants = {'reactant1.csv': \"AlkI\",'reactant2.csv': \"Phos\"}, # names of the files containing the descriptors + labels features\n", " feature_processing=True, # do feature preprocessing\n", " filename=\"reaction_space.csv\", # name of the created search space\n", " objectives=['yield'], # name(s) of the objective column(s)\n", " )\n", "\n", "# look at the search space\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The search space contains columns for the features as well as the objective columns (here: yield) and a priority column." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# explanation of the priority values:\n", "# 1: suggested samples\n", "# 0.9–0.5: alternative suggestions if a suggested sample cannot be run (lower priority values indicate lower ranking)\n", "# 0: neutral samples\n", "# -1: samples that were removed from consideration by the Vendi pruning\n", "# -2: samples that have already been evaluated\n", "\n", "# in the first round, the only priority values are 1 and 0 as no samples have been evaluated yet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding experimental data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AlkI_SASAAlkI_HOMOAlkI_LUMOAlkI_EAAlkI_DipoleAlkI_C_1_BVAlkI_C_1_SASAAlkI_C_1_P_intAlkI_C_1_chargeAlkI_C_1_electrophilicity...Phos_VolumePhos_HOMOPhos_LUMOPhos_IPPhos_C_1_SASAPhos_C_1_P_intPhos_C_1_electrophilicityPhos_P_1_chargeyieldpriority
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CCCCI.P(CC)(CC)CC274.839894-0.402234-0.230929-1.9971491.6206440.4497725.19476424.5332720.0019530.072709...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588901.0
ClCCCI.P(C)(C)C268.323472-0.410241-0.240026-1.5267621.4101440.4817525.40316025.106049-0.0017580.045916...129.102449-0.369419-0.1467978.23020215.75536819.594742-0.107096-0.094885101.0
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..................................................................
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CC(Br)CCI.P(CCC)(CCC)CCC300.675829-0.409901-0.243327-0.8682151.6280030.4889715.20239226.653934-0.0016710.020991...271.575185-0.365362-0.1527707.8259071.82280628.380926-0.078216-0.126537PENDING0.0
CC(Br)CCI.P(CC)(CC)CC300.675829-0.409901-0.243327-0.8682151.6280030.4889715.20239226.653934-0.0016710.020991...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588PENDING0.0
CC(Br)CCI.P(C)(C)C300.675829-0.409901-0.243327-0.8682151.6280030.4889715.20239226.653934-0.0016710.020991...129.102449-0.369419-0.1467978.23020215.75536819.594742-0.107096-0.094885PENDING0.0
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90 rows × 24 columns

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" ], "text/plain": [ " AlkI_SASA AlkI_HOMO \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 262.917309 -0.397556 \n", "CCCCI.P(CC)(CC)CC 274.839894 -0.402234 \n", "ClCCCI.P(C)(C)C 268.323472 -0.410241 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 244.784847 -0.421480 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 293.073124 -0.398283 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 300.675829 -0.409901 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 300.675829 -0.409901 \n", "CC(Br)CCI.P(CC)(CC)CC 300.675829 -0.409901 \n", "CC(Br)CCI.P(C)(C)C 300.675829 -0.409901 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 231.727427 -0.414516 \n", "\n", " AlkI_LUMO AlkI_EA \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.237575 -1.573800 \n", "CCCCI.P(CC)(CC)CC -0.230929 -1.997149 \n", "ClCCCI.P(C)(C)C -0.240026 -1.526762 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.254765 -1.018180 \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.231873 -1.754879 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.243327 -0.868215 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.243327 -0.868215 \n", "CC(Br)CCI.P(CC)(CC)CC -0.243327 -0.868215 \n", "CC(Br)CCI.P(C)(C)C -0.243327 -0.868215 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.242995 -1.749681 \n", "\n", " AlkI_Dipole AlkI_C_1_BV \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 1.724610 0.626968 \n", "CCCCI.P(CC)(CC)CC 1.620644 0.449772 \n", "ClCCCI.P(C)(C)C 1.410144 0.481752 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.880955 0.451279 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 1.749283 0.611972 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 1.628003 0.488971 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 1.628003 0.488971 \n", "CC(Br)CCI.P(CC)(CC)CC 1.628003 0.488971 \n", "CC(Br)CCI.P(C)(C)C 1.628003 0.488971 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 1.221979 0.426127 \n", "\n", " AlkI_C_1_SASA AlkI_C_1_P_int \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.000000 0.000000 \n", "CCCCI.P(CC)(CC)CC 5.194764 24.533272 \n", "ClCCCI.P(C)(C)C 5.403160 25.106049 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 4.521359 25.338060 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.076953 33.073760 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 5.202392 26.653934 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 5.202392 26.653934 \n", "CC(Br)CCI.P(CC)(CC)CC 5.202392 26.653934 \n", "CC(Br)CCI.P(C)(C)C 5.202392 26.653934 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 5.796379 22.940803 \n", "\n", " AlkI_C_1_charge \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.084700 \n", "CCCCI.P(CC)(CC)CC 0.001953 \n", "ClCCCI.P(C)(C)C -0.001758 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.006075 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.043606 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.001671 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.001671 \n", "CC(Br)CCI.P(CC)(CC)CC -0.001671 \n", "CC(Br)CCI.P(C)(C)C -0.001671 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.017554 \n", "\n", " AlkI_C_1_electrophilicity ... \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.129850 ... \n", "CCCCI.P(CC)(CC)CC 0.072709 ... \n", "ClCCCI.P(C)(C)C 0.045916 ... \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.032131 ... \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.102410 ... \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 0.020991 ... \n", "CC(Br)CCI.P(CCC)(CCC)CCC 0.020991 ... \n", "CC(Br)CCI.P(CC)(CC)CC 0.020991 ... \n", "CC(Br)CCI.P(C)(C)C 0.020991 ... \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 0.069161 ... \n", "\n", " Phos_Volume Phos_HOMO \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "CCCCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "ClCCCI.P(C)(C)C 129.102449 -0.369419 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "CC(Br)CCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "CC(Br)CCI.P(C)(C)C 129.102449 -0.369419 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "\n", " Phos_LUMO Phos_IP \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "CCCCI.P(CC)(CC)CC -0.152998 7.922281 \n", "ClCCCI.P(C)(C)C -0.146797 8.230202 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.152770 7.825907 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.152770 7.825907 \n", "CC(Br)CCI.P(CC)(CC)CC -0.152998 7.922281 \n", "CC(Br)CCI.P(C)(C)C -0.146797 8.230202 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "\n", " Phos_C_1_SASA Phos_C_1_P_int \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.072002 22.730716 \n", "CCCCI.P(CC)(CC)CC 3.564288 23.715433 \n", "ClCCCI.P(C)(C)C 15.755368 19.594742 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 1.822806 28.380926 \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 0.072002 22.730716 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 1.822806 28.380926 \n", "CC(Br)CCI.P(CC)(CC)CC 3.564288 23.715433 \n", "CC(Br)CCI.P(C)(C)C 15.755368 19.594742 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 0.072002 22.730716 \n", "\n", " Phos_C_1_electrophilicity \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "CCCCI.P(CC)(CC)CC -0.079000 \n", "ClCCCI.P(C)(C)C -0.107096 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.078216 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.078216 \n", "CC(Br)CCI.P(CC)(CC)CC -0.079000 \n", "CC(Br)CCI.P(C)(C)C -0.107096 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "\n", " Phos_P_1_charge yield \\\n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.136051 1 \n", "CCCCI.P(CC)(CC)CC -0.106588 90 \n", "ClCCCI.P(C)(C)C -0.094885 10 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "C1CCCCC1I.P(CCC)(CCC)CCC -0.126537 PENDING \n", "... ... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C -0.136051 PENDING \n", "CC(Br)CCI.P(CCC)(CCC)CCC -0.126537 PENDING \n", "CC(Br)CCI.P(CC)(CC)CC -0.106588 PENDING \n", "CC(Br)CCI.P(C)(C)C -0.094885 PENDING \n", "OCCI.P(C(C)C)(C(C)C)C(C)C -0.136051 PENDING \n", "\n", " priority \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 1.0 \n", "CCCCI.P(CC)(CC)CC 1.0 \n", "ClCCCI.P(C)(C)C 1.0 \n", "ClCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.0 \n", "C1CCCCC1I.P(CCC)(CCC)CCC 0.0 \n", "... ... \n", "CC(Br)CCI.P(C(C)C)(C(C)C)C(C)C 0.0 \n", "CC(Br)CCI.P(CCC)(CCC)CCC 0.0 \n", "CC(Br)CCI.P(CC)(CC)CC 0.0 \n", "CC(Br)CCI.P(C)(C)C 0.0 \n", "OCCI.P(C(C)C)(C(C)C)C(C)C 0.0 \n", "\n", "[90 rows x 24 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# you can add results by opening your search space file by opening it in excel and replacing the PENDING labels\n", "# or you can also do it directly in the notebook as below\n", "\n", "# read in the search space\n", "df = pd.read_csv('./reaction_space.csv',index_col=0,header=0)\n", "\n", "# add the objective values (these are dummy values in this case)\n", "# as the search space is sorted by priority, you can simply replace the yield values for the first three lines \n", "# (since three samples were suggested)\n", "# the order is the same as in the print of run function above. note that the numbering starts at 0\n", "df['yield'].iat[0] = 1 # iat indicates the row in the table (0 = first row); let's say the yield for this reaction was 1%\n", "df['yield'].iat[1] = 90\n", "df['yield'].iat[2] = 10\n", "\n", "# same the file under a different name to keep track of the different rounds\n", "df.to_csv('./reaction_space_round1.csv')\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run with data. This will now use the main optimization function (run) to get the scope predictions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 3 existing scope entries.\n", "Cut 12 samples from the search space via Vendi pruning. 75 samples remain.\n", "Selecting sample 1 of 3...\n", "Selecting sample 2 of 3...\n", "Selecting sample 3 of 3...\n", "The run finished sucessfully!\n", "These are the suggested substrate combinations:\n" ] }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "These are the requested alternative suggestions, sorted by descending priority:\n" ] }, { "data": { "image/png": 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", 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", 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", 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CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C186.642634-0.408318-0.231409-2.2804071.4514840.23267421.62050518.480703-0.0471980.029576...322.301921-0.344248-0.1539427.1431670.0000000.000000-0.034990-0.138842PENDING0.8
..................................................................
C1CCCCC1I.P(CC)(CC)CC293.073124-0.398283-0.231873-1.7548791.7492830.6119720.07695333.0737600.0436060.102410...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588PENDING-1.0
CC(C)I.P(CC)(CC)CC242.902322-0.400897-0.235196-1.7966491.6529880.5172500.24000627.9319480.0490400.106570...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588PENDING-1.0
CCCCI.P(CC)(CC)CC274.839894-0.402234-0.230929-1.9971491.6206440.4497725.19476424.5332720.0019530.072709...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.10658890-2.0
ClCCCI.P(C)(C)C268.323472-0.410241-0.240026-1.5267621.4101440.4817525.40316025.106049-0.0017580.045916...129.102449-0.369419-0.1467978.23020215.75536819.594742-0.107096-0.09488510-2.0
CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C262.917309-0.397556-0.237575-1.5738001.7246100.6269680.0000000.0000000.0847000.129850...263.550839-0.355563-0.1533867.5203350.07200222.730716-0.056090-0.1360511-2.0
\n", "

90 rows × 24 columns

\n", "
" ], "text/plain": [ " AlkI_SASA AlkI_HOMO \\\n", "CI.P(CCC)(CCC)CCC 186.642634 -0.408318 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 274.839894 -0.402234 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 186.642634 -0.408318 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 252.281108 -0.424785 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 186.642634 -0.408318 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 293.073124 -0.398283 \n", "CC(C)I.P(CC)(CC)CC 242.902322 -0.400897 \n", "CCCCI.P(CC)(CC)CC 274.839894 -0.402234 \n", "ClCCCI.P(C)(C)C 268.323472 -0.410241 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 262.917309 -0.397556 \n", "\n", " AlkI_LUMO AlkI_EA \\\n", "CI.P(CCC)(CCC)CCC -0.231409 -2.280407 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.230929 -1.997149 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.231409 -2.280407 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.265952 -0.441051 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.231409 -2.280407 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.231873 -1.754879 \n", "CC(C)I.P(CC)(CC)CC -0.235196 -1.796649 \n", "CCCCI.P(CC)(CC)CC -0.230929 -1.997149 \n", "ClCCCI.P(C)(C)C -0.240026 -1.526762 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.237575 -1.573800 \n", "\n", " AlkI_Dipole AlkI_C_1_BV \\\n", "CI.P(CCC)(CCC)CCC 1.451484 0.232674 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.620644 0.449772 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.451484 0.232674 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.130045 0.456235 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 1.451484 0.232674 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 1.749283 0.611972 \n", "CC(C)I.P(CC)(CC)CC 1.652988 0.517250 \n", "CCCCI.P(CC)(CC)CC 1.620644 0.449772 \n", "ClCCCI.P(C)(C)C 1.410144 0.481752 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 1.724610 0.626968 \n", "\n", " AlkI_C_1_SASA AlkI_C_1_P_int \\\n", "CI.P(CCC)(CCC)CCC 21.620505 18.480703 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 5.194764 24.533272 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 21.620505 18.480703 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 4.270924 26.443056 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 21.620505 18.480703 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.076953 33.073760 \n", "CC(C)I.P(CC)(CC)CC 0.240006 27.931948 \n", "CCCCI.P(CC)(CC)CC 5.194764 24.533272 \n", "ClCCCI.P(C)(C)C 5.403160 25.106049 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.000000 0.000000 \n", "\n", " AlkI_C_1_charge \\\n", "CI.P(CCC)(CCC)CCC -0.047198 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.001953 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.047198 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.000588 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.047198 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.043606 \n", "CC(C)I.P(CC)(CC)CC 0.049040 \n", "CCCCI.P(CC)(CC)CC 0.001953 \n", "ClCCCI.P(C)(C)C -0.001758 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.084700 \n", "\n", " AlkI_C_1_electrophilicity ... \\\n", "CI.P(CCC)(CCC)CCC 0.029576 ... \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.072709 ... \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.029576 ... \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.009899 ... \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.029576 ... \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.102410 ... \n", "CC(C)I.P(CC)(CC)CC 0.106570 ... \n", "CCCCI.P(CC)(CC)CC 0.072709 ... \n", "ClCCCI.P(C)(C)C 0.045916 ... \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.129850 ... \n", "\n", " Phos_Volume Phos_HOMO \\\n", "CI.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 322.301921 -0.344248 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 199.428774 -0.365679 \n", "CC(C)I.P(CC)(CC)CC 199.428774 -0.365679 \n", "CCCCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "ClCCCI.P(C)(C)C 129.102449 -0.369419 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "\n", " Phos_LUMO Phos_IP \\\n", "CI.P(CCC)(CCC)CCC -0.152770 7.825907 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.153942 7.143167 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.152998 7.922281 \n", "CC(C)I.P(CC)(CC)CC -0.152998 7.922281 \n", "CCCCI.P(CC)(CC)CC -0.152998 7.922281 \n", "ClCCCI.P(C)(C)C -0.146797 8.230202 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "\n", " Phos_C_1_SASA Phos_C_1_P_int \\\n", "CI.P(CCC)(CCC)CCC 1.822806 28.380926 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.000000 0.000000 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 3.564288 23.715433 \n", "CC(C)I.P(CC)(CC)CC 3.564288 23.715433 \n", "CCCCI.P(CC)(CC)CC 3.564288 23.715433 \n", "ClCCCI.P(C)(C)C 15.755368 19.594742 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.072002 22.730716 \n", "\n", " Phos_C_1_electrophilicity \\\n", "CI.P(CCC)(CCC)CCC -0.078216 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.034990 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.079000 \n", "CC(C)I.P(CC)(CC)CC -0.079000 \n", "CCCCI.P(CC)(CC)CC -0.079000 \n", "ClCCCI.P(C)(C)C -0.107096 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "\n", " Phos_P_1_charge yield \\\n", "CI.P(CCC)(CCC)CCC -0.126537 PENDING \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.138842 PENDING \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.106588 PENDING \n", "CC(C)I.P(CC)(CC)CC -0.106588 PENDING \n", "CCCCI.P(CC)(CC)CC -0.106588 90 \n", "ClCCCI.P(C)(C)C -0.094885 10 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.136051 1 \n", "\n", " priority \n", "CI.P(CCC)(CCC)CCC 1.0 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.0 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.0 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.9 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.8 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC -1.0 \n", "CC(C)I.P(CC)(CC)CC -1.0 \n", "CCCCI.P(CC)(CC)CC -2.0 \n", "ClCCCI.P(C)(C)C -2.0 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -2.0 \n", "\n", "[90 rows x 24 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run the algorithm again (note that this uses the file that has been updated with the results)\n", "ScopeBO().run(\n", " objectives=['yield'], # reaction objectives (for multi-objective cases (e. g. yield and ee), use ['yield','ee'])\n", " filename='reaction_space_round1.csv' # name of the search space\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# if you have a minimization objective, provide the extra keyword objective_mode to the algorithm as below\n", "# (remove the hashes for each line after this one)\n", "\n", "#ScopeBO().run(\n", "# objectives=['yield','cost'],\n", "# objective_mode={'cost': 'min'}\n", "# filename=\"reaction_space.csv\"\n", "#)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look at the search space on a UMAP projection. The evaluated datapoints are colored by performance and pruned samples are crossed out." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Structures of the scope substrates:\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "UMAP projection of the reaction space:\n", "(Evaluated samples are shown as colored circles. Pruned samples are marked with X.)\n", "(Suggested samples are marked with squares and alternative suggestions with diamonds.)\n" ] }, { "data": { "image/png": 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9BrmGf0iyecDZg6rqhn2GAQBgXv4myW/NMtOS9FZOBQDA0Bw8wMxZvacAAGDU/jDJfgPOfifJ83rMcpW/7TD7oN5SAAAwUlX1iAz2Wcdzkzy6tba950gAAOPkdzrMvmKBrsX+IUmXh6srpwcAAAAAWESU0wMAQD+O6jD7N6216d6SXENr7X+SnNhhye2r6kY9xQEAWLKqal2Seww4/vbW2vl95rm61tqGJO/rsORmfWUBAGDuquqeGax46pVJPtVrGAAAhuGQAWaU0wMALGFVVUme1WHJU1trU33luUpr7atJvjzg+AFV9Rt95gEAYOFV1c2THJ+kZhndmuQRrbVf9R4KAGC83H3Aue1J3tlnkKu01jYneUeHJbfsKwsAAAAAAN0ppwcAgCGrqj2S3HHA8fOTfLDHODvz7x3n791LCgCApe1eSZYPOPuWPoPsxMc7zN6ktxQAAMzJjvchT8jsP/f/ZpIX9R4IAIBhGKSc/uzeUwAAMEr3yOAPD39na+3zfYa5hnd3mL1tbykAAFhwVXW9zBScrhlg/Dmtta/1HAkAYKxU1d5J9h5w/Kuttcv6zHMNJ3WYPbS3FAAAAAAAdKacHgAAhu+eSSYHnH1La217j1l25stJLuowf2RfQQAAlrBbDTi3Kcln+wyyE9/qMLtfbykAAJirNyY5cJaZK5I8trW2dQHyAAAwfwcPMHNW7ykAABilJww415K8uM8g1+L9HWYHubYFAGDX8c9JbjnA3Mmttdf0nAUAYBx1KXX/cm8prt03Osyu6y0FAAAAAACdKacHAIDhu0WH2Y/1luI6tNZaks93WDLIh4gBAPjfjhhw7rQRlYWe22F2TW8pAADorKqekuShA4w+p7X23b7zAAAwNIcMMKOcHgBgiaqq1UkeNuD4Ka21s/vMc02ttXOSXDjguAegAwAsEVX1iCRPGWD0ggz+sCUAALrZq8Psxb2luBattU1JNg44vqrPLAAAAAAAdKOcHgAAhu9mA85tSfLFPoPM4psdZg/uKwQAwBI26EOLvtVrip3b1GHWB4ABABaJqrpZkn8aYPQjrbV/6TsPAABDNVs5fUvyg4UIAgDASNwvgz84/NV9BrkOH0/yywFebUT5AAAYoqq6cZI3DjA6neRxrbVf9RwJAGBcre0w+8veUuzc5QPObeg1BQAAAAAAnSwbdQAAAFiCBi2nP721trXXJNft3A6z16+qVa21Lb2lAQBYemYrkrrKd3pNsXMrO8xu7C0FAAADq6oVSd6eZPUsoz9P8oT+EwEAMCxVtS7JnrOMnd9aG/SmfgAAdj0PHXDuvCSn9Zhjp1prjx/FuQAALLyqWpaZn0+vG2D8n1prn+o5EgDAOKsOs5O9pbgWVTWRZK8Bxy/pMwsAAAAAAN1MjDoAAAAsQYcNOHdWrylm1/VDHGt7SQEAsARV1eokywccH9V14e4dZn/VWwoAALp4eZLbDDD3pNbaRT1nAQBguAZ52OWof8YMAEBPqqqSPGDA8RNba63PPAAAkOSFSe44wNwZSV7UcxYAgHG3ucPs3r2luHb7ZvBCfOX0AAAAAACLiHJ6AAAYoqpam8FL3EddHLCl4/zKXlIAACxNu3WY/VlvKa7bwR1mldMDAIxYVd0/yTMHGP231topfecBAGDolNMDAIy3WyfZa8DZk/sMAgAAVXWrDFY4f2WSx7fWpnqOBAAw7i7qMHvz3lJcu3t2mD2nrxAAAAAAAHSnnB4AAIZr0GL6JPmf3lIM5nod57uW2QMAjLPdO8z+orcU1+1WHWbP7S0FAACzqqp9khyfpGYZPTvJX/QeCACAPgzyMMmze08BAMCo3HvAuV8m+UqfQQAAGG9VtSwzP59ePsD4y1trp/ebCACAJD/sMHvPqprts4bD9LsdZr/QWwoAAAAAADpbNuoAAACwxHQpp/9VbykGs0/H+U29pAAAWJq2JXnvAHNTrbWpvsPsxP0HnNue5Et9BgEAYOd23CR2fJJ9ZxndluSxrbXLew8FAEAfDhlg5qzeUwAAMCpHDjj3idbadK9JAAAYdy9IctsB5r6b5KU9ZwEAIElr7RdV9T9JbjzA+H5J7pvkY/2mSqpqvyRHd1jyxb6yAAAAAADQnXJ6AAAYri7l9Jf0lmIwN+sw+4sRlqYCAOxyWms/TvKIUefYmaraKzMfNh7Et1prHlQEADA6z0zygAHmjmutfa3vMAAA9EY5PQDAePutAee+0GsKAADGWlXdMsmLBhidTvIE95kAACyojyd58oCzL8oClNMn+Zskawac/XmSb/YXBQAAAACArpTTAwDAcC1LcsGAs7/oM8gA7t5h9oe9pQAAYBSemWTVgLMf7zMIAAA7V1W3SvL3A4x+bsA5AAAWr4Nn+f4VSc5biCAAACysqto/yQEDjn+5zywAAIyvqlqW5PgkKwYY/+fWmmtTAICF9a4MXk5/t6p6UmvtjX2Fqaq7JXlChyX/0Vrb1lceAAAAAAC6U04PAABD1Fr7WpIDR51jNjtuZrtDhyXf7isLAAALq6punuTZA45vT/KGHuMAALATVbUqyduTrJxldEOSP2itTfefCgCAPlTV6iT7zzL2w+u65tuxxx2S/FZmiu5vlGS/JGuSrE4ykWTzjteFSf4nMw8p/3KSr7fWtszvdwEAwDzcZsC5K5Oc3mMOAADG2zOT/OYAcxckObbnLAAA/F+fTPLdJIcPOP/aqvpBa+3Tww5SVTdO8r4kkwMucW8KAAAAAMAipJweAADG0zEZ/EMfSfLZnnIAALCAqmqPJO9Ocr0Bl5zcWvtxf4kAALgOr8pgN5E9rbV2bt9hAADo1cFJapaZs675haraJ8kjkjwqyZEZ/DOht7rGf95WVZ9N8p4k722t/WzAfQAAGI5By6R+1FqbGnTTqlqR5I5Jbp/k0B2vvZOszcxDjLYnuTzJZUnOzcwDjL6Z5MuttR8Oeg4AALu+qrpBBi+cf3ZrbVOfeQAA+L9aa62qjkvyrgGXXC/Jh6vqca219w0rR1UdmuSDSfbqsOyNrbWfDCsDAAAAAADDoZweAADGTFWtSfKsDkumk3yypzgAACyQqjowyclJbjHgkm1JXtZfIgAAdqaqHpLkKQOMvqO19tae4wAA0L+DB5j5dTl9Vd0kyV8neUySFUM4f3mSe+14vbqq3pnkVa21bwxhbwAAZjdoOf33ZhvY8fnAR2TmWvFuSVYNuPftr7HP+UlOSfKe1tqnBtwDAIBd1z8l2W2AudNaa+/oOwwAANeutfbuqvpokvsPuGR1kvdW1VuTPKu19ov5nF9VD0ry1iTrOiz7cZLnzOdcAAAAAAD6MTHqAAAAwII7LsneHeY/01r7aU9ZAABYAFX1+0m+nuQ3Oyx7SWvt9J4iAQCwE1V1QJI3DjD6kyR/0nMcAAAWxiEDzJxVVWuq6hVJvp/kmAynmP6alif5gyRfr6p3VdWNezgDAID/7dAB587a2Teqaq+qelWSi5Icn+R3Mngx/bU5MMmfJvlkVX2vqv6sqlbOYz8AABapqrpXkt8bYPTKJE/vOQ4AALM7JknXe37/IMn3qurFVbVf1wOr6siq+kiSD6RbMf32JH/UWtvY9UwAAAAAAPqnnB4AAMZIVd01yZ93XPaWHqIAANCzqlpZVY+tqi8leXuSfTos/3ySv+8nGQAAO1NVE0lOSLLnLKPTSf6wtXZp76EAAFgIg5TT753k20mek5kC+YXwyMwUFDxvx7UqAAD9uNGAc+dd8wtVtayqnp/knMx8NnDt8GL92s2SvDbJD6rqCVVVPZwBAMAIVNXyJK8bcPx1rbXv9JkHAIDZtdYuTnL/JJd0XLpXkpckOa+qTqmqZ1fVnXY8+HLyqqGq2q2qblRVv1tV/1RV30ryuR1ndoqa5ImttdM6rgMAAAAAYIFUa23UGQAAgAVQVQcl+VKSG3RYdkGSm7TWtvaTCgCAQVXVfkkubtfypm5VrUhyYJKDkxyR5J5J7pFk3RyO+laSe7XWfjX3tAAAzEVVvSDJ3w4w+vLW2vM77n1Mkv8aZLa1plwKAGABVdXHk9xn1Dlm8dkkj2mtnT/qIAAAS0lVLUtyRZLJ2WaTPLS19v6rrb1RkhOT3LmXcDv32SRPaq2dvcDnAgAwZFX1vCR/P8DopUkO9rlCAIDFo6pukeSjmbmXZL5ako1JVmU4D0ufSvKE1tqJQ9gLAAAAAICeLBt1AAAAoH9VtU+S/063YvpkpuRKMT0AwOLw4ySTVbUxyfSOry1LsibDe6/31CS/5wYyAICFV1V3SPKSAUZPT/LinuMAALCwDhl1gAHcLclXquro1trXRx0GAGAJuUEGK6ZPkguu+kVV3SfJezK3B5bP192SfLOq/qS19uYRnA8AwBBU1Q2S/PWA4y/3uUIAgMWltXZmVf1mkjclOXqe21WS3eefKsnMvS+Pbq19aUj7AQAAAADQk4lRBwAAAPpVVTdK8ukkN++49Iwk/zr0QAAAzMeyJNdPsueO17oMp5h+KslxSe7vBjIAgIVXVWuTnJhk+SyjW5I8xgMlAQCWjqpanuSgUecY0P5JPlNVvzPqIAAAS8i+HWZ/liRV9btJPpjRFNNfZVWS46vqdVXl3iQAgF3TS5OsGWDup0le3XMWAADmoLX289ba7yb5vSTnjjpPkuOT3FoxPQAAAADArsEHQAEAYAmrqnsk+UqSm3Vcuj3JU1trVw4/FQAAi8h0kvckuVVr7SWtte2jDgQAMKZel+SQAeae3Vr7ft9hAABYUDdOMjnqEB2sTvL+qvrtUQcBAFgi9u4we1lVHZXkvUlW9pSnqz9NcmJVDeOh6gAALJCqukWSxw84flxrbUufeQAAmJ/W2ruS3DTJU5P8cAQRvp/kbq21P2qtbRjB+QAAAAAAzIEPfwIAwBJUVRNJXpDkJZlbkcGxrbUvDDcVAACL0Pszc+139qiDAACMq6r6/SR/OMDoh1trr+87DwAAC26QhxTN5ookn07yqSTfS3JWkl8l2ZRkW2YK5fdNcpMkd0hy9yT3TLJ8juetSvKBqrpLa+3b80oOAMCg5fQtyQFJTsziuxfo9zKT79GjDgIAwMBensHuNTkryX/1nAUAgOGYzszPh9sIzv6NJA+vqh+21i4awfkAAAAAAMxBtTaK95QBAIC+VNWNk7w5yd3muMUpSR7S/MsCAMCiUlVXJFnZ0/anJ3l1khNaa9M9nQEAwDVU1Y2SfDPJullGf5bkVq21i+dx1jEZsDSgtVZzPQcAgG6q6hmZeW9uLs5I8s9J3tFau7zjueuTPD7JMzJTWj8XP0py+9baJXNcDwAw9jpcD25N8uMkh3XY/owkH07ypSRnJ7kgyebMlJCuTrJnkhsnuXmSI5Pce8fX5uqFrbW/m8d6AAAWQFXdI8lpA44/trV2Yo9xAACYp6qqJE9O8ldJDhpxnMuT/G2Sf2itbR1xFgAAAAAAZqGcHgAAloiqmkjyZ5n54MaaOW7z+ST3ba1tGVowAACGoudy+qucnuQvWmun9XwOAMDYq6rJJJ9JcpcBxo9urX1wnucdE+X0AACLTlW9JsnTOy67KMlzWmtvHcL5y5P8SZKXJtl9Dlu8t7X2iPnmAAAYV1X1V0leNsQttyY5PslrWmtndsyyPMkDkjw3M2X1XU0nuUdr7XNzWAsAwAKpqi8n+a0BRn+Y5Gatte09RwIAYI6q6tAkb0pyt1FnuYazkzy+tfalUQcBAAAAAGDnJkYdAAAAmL+quk2SLyZ5deZeTP+FJEcppgcAGGu3TfKpqnpZVSkkBQDo17EZrJj+DfMtpgcAYFE7uOP8KUluPoxi+iRprW1rrb0myS2TfHYOWzy8qh48jCwAAGNqtyHu9aEkh7fWntK1mD759bXhKa21uyZ5ZJJfddxiIskbdpTcAwCwCFXVwzJYMX2S/J1iegCAxauqnpTkW1l8xfRJcliSz1TVs0cdBAAAAACAnVNODwAAu7CqWl9Vr03ytQz+AeFrc1KS+7bWLhtOMgAAdnF/leR9VTXXBx8BAHAdququSV44wOhZSf6y5zgAAIzWIR1mX5LkIa21S4cdorV2XpJ7J/nPOSx/bVWtGHIkAIBxsXoIe0wleXJr7UGttXOGsF9aa+/JzMPNf9hx6S2SPG0YGQAAGK6qqiR/PeD4eUlO6DEOAADzUFXHJvmPJKtGneU6LE/yD1X1hqrSbwQAAAAAsAh58xYAAHZBVTVRVU9O8oMkf5Zkco5bTWemwODhrbXLh5UPAIAl4SFJ3uFDwAAAw1VV65O8LbO/p7ctyWO9bwcAsHTteO/tRgOOP6e1dlxrrfWVp7W2rbX2xCT/2nHpQUme0EMkAIBxsHye6y9N8tuttf8YQpb/ZccDjO6R5MKOS/+yqub7+wIAYPgenOQ2A86+vLW2rccsAADMUVX9S5Lj5rHFhUn+K8lTktwlMz/v3S0zn2lclWSvJLdO8tAkL03y2SRXzuO8p2RuD0kHAAAAAKBn1eN9SgAAQA+q6q5JXpPktvPc6sIkj2+tfXz+qQAAWGhVNZmZDwDvlmRtknVJDktyiySHZ+YmsgOHcNRrWmvPHMI+AAAkqap3JnnUAKMvaK39/RDPPSYzN5TNqrVWwzoXAICdq6obJfmfAUb/obX23J7j/FpVVZIPJXlAh2XnJrlJa226n1QAAEtTVf17kj+e4/LNSe7ZWvvaECP9H1V1rySnJunyvuExrbU39xQJAIA5qKqvJ/nNAUYvTvIbrbWpniMBANBRVf15klfNcfkpSV6d5FNdH4peVXskeVySpyc5eI7nv7i19tI5rgUAAAAAoAfK6QEAYBdRVQcmeUWSRw9hu/cmeUpr7ZdD2AsAgEWqqm6X5GE7Xjebx1aPbK29ZzipAADGV1U9IcmbBhj9TJLfHmaxp3J6AIDFp6oOS/LCWcYuSvLChS5931Eu8L0k+3RYdt/W2qk9RQIAWJKq6j+T/NEclz+2tXbiMPPsTIeHbl7l1NbaffvKAwBAN1V1dGbKSAehNBQAYBGqqnsm+XiSZR2Xfisz9xN/eQgZliV5YpK/T7K+4/LpzDxs87PzzQEAAAAAwHAopwcAgEWuqq6X5C+TvCDJmnlu9/MkT2+tvXPewQAA2KVU1UOTvDTJLeaw/CdJbtZau3y4qQAAxkdVHZrk9Mz+Ht9lSW7VWjtvyOcfE+X0AAB0UFWPT3J8hyVvbq0d008aAIClqarelOQJc1h6cmvtIUOOs1NVdXBmHl60fMAlVybZv7X2i/5SAQAwqKr6SpI7DDC6JckNXccBACwuVbU6ydlJDui49DVJnt1a2zbkPPsneUeSu3dc+qMkh7fWpoaZBwAAAACAuZkYdQAAAGDnquohSb6b5GWZXzF9y0zx1M0U0wMAjKfW2klJbpXkSUk2d1x+UGYelgQAwBxU1fIkJ2aw9/j+dNjF9AAAMEcnJPlBh/kH9hUEAGAJ2zqHNdNJnjPsINeltXZOkv/osGRZkvv1FAcAgA6q6t4ZrJg+SU5QTA8AsCg9J92L6Z/VWnvmsIvpk6S1dmGSeyd5a8elN0nyZ8POAwAAAADA3CinBwCARaiqDq+qjyc5KcmN57ndt5LcvbX2hNbar+afDgCAXVVrbbq19qYkd0xyccflf1ZV1+shFgDAOPh/SW4/wNyJrbUT+w4DAACDaK1NJ3lthyV7V9XhfeUBAFiipuaw5pTWWpeHCA3LGzrO37mXFAAAdPUXA861JK/qMwgAAN1V1X7p/rDKF7fW/rmHOL/WWrsyyeOTvLfj0udV1coeIgEAAAAA0JFyegAAWESqam1V/WNmCuXvM8/tfpnkz5LcrrX2uXmHAwBgyWitnZnkvkku77BsfZKH9RIIAGAJq6r7JHn2AKPnJfnTnuMAAEBX704y3WH+Ln0FAQBYouZSTv/moacYQGvtjCRf77DkTn1lAQBgMFV10yQPGHD8I6217/eZBwCAOXlykjUd5j+c5GU9Zflfdjzw/JgkZ3dYtneS3+slEAAAAAAAnSinBwCARaKqHpLku0n+IsmyeWy1LcmrkxzaWntda237EOIBALDE7CgOeE7HZX/URxYAgKWqqvZK8pYkNcvodJLHtdYu6z8VAAAMrrV2Uf4/9u48yra7rBP+90luEjJBmGeIgERkCAiIDMHIJPMQ4F4MXki6tdHWbrG7Fel+xaaHt8FW6W77RW0HtKPADUMARQFliA0YhgASIsgkUwAZZMpEpuf9o27sGHOTs0/tXftUnc9nrVrcW/U8v/3NqbqsWlXnfHfyvgErd54qCwDADnXJwPmLk7xpiiALOm3A7HdPlgIAgEX9dK7799VXevGUQQAAWNoPD5i9MMmPd3dPFebquvv8JD82cO2HpsgCAAAAAMAwyukBAGBmVXVMVf1+kjOS3HaTx706yV27+9nd/bXNpwMAYIf7jSQfGTB/QlUdOlUYAIAd6HeS3HKBuRd2959PHQYAAJZ01oDZO02WAgBgZxp6w8r3d/eFkyRZzJkDZg+vqptNlgQAgGtVVTdO8owFxz+d5E8mjAMAwBKq6v5JvnPAym9092emynMg3f3WJH82YOUhVXX0VHkAAAAAAFiMcnoAAJhRVZ2Q5INJnr7Jo/4iyQO7+8nd/bHNJwMAYB109+VJXjRg5bAk95goDgDATvS4BWbel+QXpg4CAACb8MEBszefLAUAwM705YHzZ0+SYnHnJLlgwPztpwoCAMB1elaSwxec/c3uvmLKMAAALOXhA+d/dZIUi/lvA2YPTXK/iXIAAAAAALAg5fQAADCTqnp2krckue0mjvlEkt3d/YDufucowQAAWDevSHLZgPn7ThUEAGBNfU+SS6qqp3xL8pJFAy1x/jGTPToAAKyCTw+YPWqyFAAAO9PQcvq/mSTFgvbfAP19A1ZuNFUWAAAOrKoOykY5/SIuTfLbE8YBAGB59xkw+67unvPnh29K8vUB816bAgAAAAAwM+X0AACwxarqkKr6vSQvSrJryWO+muSnk9ylu18xWjgAANZOd38tyfsHrNxpqiwAAAAAwEr60oDZIydLAQCwMw35XitJPjdJimG+MGD2iMlSAABwbR6R5HYLzr62u784ZRgAAJY2pJz+zyZLsYDuvjTJmQNWvnOqLAAAAAAALEY5PQAAbKGqOirJHyZ5xpJHXJqNUvs7dfd/2/9kDQAA2KyzBszecLIUAAAAAMAqumjArN9hAwAMM7QE9PxJUgzzdwNmldMDAMzjRwfM/t5kKQAAWFpVHZ3klgNW3j1VlgGGvDbl9pOlAAAAAABgIbvmDgAAAOuiqo5I8idJHrTkEX+U5F9198fGSwUAwJiq6vAs/gTZL3X3kBftT+kTA2aPmSoEAAAAALCSLh8we+FkKQAAdqbzsnEzoMMXnL94wiyLGvJ77ismSwEAwDWqqpsledyC419O8oYJ4wAAsLwbDpz/+CQphhmS4UaTpQAAAAAAYCHK6QEAYAtU1WFJXpfliuk/n+Rfdverxk0FAMAE7pXkHQvOvjDJz02YZYjzBsxef7IUAAAAAMAqOmLA7PmTpQAA2IG6u6vqY0nuseDKIVPmWdCQ7w/dvAgAYOs9M4t/3/jy7r5syjAAACztmIHzn58ixECfGTA75OeMAAAAAABM4KC5AwAAwJr4X0keusTeaUnuopgeAGDbuGjA7C0nSzHcBQNmvz1ZCgAAAABgFd1swOznJksBALBzfWTA7JGTpVjcjQbMunkRAMDW+6cDZv/3ZCkAANis6w+cH/K6kKkM+XmgziMAAAAAgJntmjsAAADsdFX100meMXDtm0l+tLtPnyASAADTGfJk3mOnCrGESwbMrsITlgEAAACArXPsgNlPTxUCAGAHG1JOvwo3QR9STu/mRQAAW6iq7p3kuAXHP9bd750yDwAAm3LpwPkrJkkxzMUDZr02BQAAAABgZsrpAQBgQlV1jyQvGLj20SRP6O4hLzgDAGA1fGHA7B0nSzHckQNmvzFZCgAAAABgFd1zwOwnpgoBALCDvWfA7B0mS7G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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "{'seen': UMAP1 UMAP2 yield priority\n", " CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 4.313092 -5.602553 1.0 -2.0\n", " CCCCI.P(CC)(CC)CC 6.415388 -7.337059 90.0 -2.0\n", " ClCCCI.P(C)(C)C 8.321555 -6.296204 10.0 -2.0,\n", " 'neutral': UMAP1 UMAP2 yield \\\n", " BrCCCI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 5.104922 -3.412623 PENDING \n", " BrCCCI.P(C(C)C)(C(C)C)C(C)C 6.146019 -3.608788 PENDING \n", " BrCCCI.P(C)(C)C 7.978132 -5.469464 PENDING \n", " BrCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 3.984681 -3.658060 PENDING \n", " BrCCCI.P(CC)(CC)CC 7.261620 -4.502103 PENDING \n", " ... ... ... ... \n", " OCCI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 3.432869 -7.998339 PENDING \n", " OCCI.P(C(C)C)(C(C)C)C(C)C 4.254867 -8.385830 PENDING \n", " OCCI.P(C)(C)C 8.279959 -7.553286 PENDING \n", " OCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 2.317646 -4.158529 PENDING \n", " OCCI.P(CCC)(CCC)CCC 5.169470 -8.636270 PENDING \n", " \n", " priority \n", " BrCCCI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.0 \n", " BrCCCI.P(C(C)C)(C(C)C)C(C)C 0.0 \n", " BrCCCI.P(C)(C)C 0.0 \n", " BrCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.0 \n", " BrCCCI.P(CC)(CC)CC 0.0 \n", " ... ... \n", " OCCI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.0 \n", " OCCI.P(C(C)C)(C(C)C)C(C)C 0.0 \n", " OCCI.P(C)(C)C 0.0 \n", " OCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.0 \n", " OCCI.P(CCC)(CCC)CCC 0.0 \n", " \n", " [67 rows x 4 columns],\n", " 'cut': UMAP1 UMAP2 yield priority\n", " C1CCCCC1I.P(CC)(CC)CC 6.047512 -6.659769 PENDING -1.0\n", " CC(C)(C)I.P(CC)(CC)CC 6.019232 -5.728792 PENDING -1.0\n", " CC(C)C(C)I.P(CC)(CC)CC 6.347685 -6.545941 PENDING -1.0\n", " CC(C)I.P(CC)(CC)CC 6.088325 -7.641491 PENDING -1.0\n", " CCC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 4.703033 -5.146311 PENDING -1.0\n", " CCCCI.P(C)(C)C 7.632472 -7.268377 PENDING -1.0\n", " CCCCI.P(CCC)(CCC)CCC 5.654973 -6.974147 PENDING -1.0\n", " CCCI.P(CC)(CC)CC 6.392099 -8.121288 PENDING -1.0\n", " CCCI.P(CCC)(CCC)CCC 5.424211 -7.795016 PENDING -1.0\n", " CCI.P(CC)(CC)CC 6.048952 -8.723996 PENDING -1.0\n", " ClCCCI.P(CC)(CC)CC 6.785428 -5.416522 PENDING -1.0\n", " OCCI.P(CC)(CC)CC 6.645766 -8.327135 PENDING -1.0}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# call the function with the latest search space file\n", "ScopeBO.visualize(filename=\"reaction_space_round1.csv\",return_dfs=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding results again" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CI.P(CCC)(CCC)CCC186.642634-0.408318-0.231409-2.2804071.4514840.23267421.62050518.480703-0.0471980.029576...271.575185-0.365362-0.1527707.8259071.82280628.380926-0.078216-0.126537101.0
CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3274.839894-0.402234-0.230929-1.9971491.6206440.4497725.19476424.5332720.0019530.072709...335.813928-0.366304-0.2386597.2948681.56203732.9585480.003254-0.11039001.0
CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3186.642634-0.408318-0.231409-2.2804071.4514840.23267421.62050518.480703-0.0471980.029576...335.813928-0.366304-0.2386597.2948681.56203732.9585480.003254-0.110390501.0
BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3252.281108-0.424785-0.265952-0.4410510.1300450.4562354.27092426.4430560.0005880.009899...335.813928-0.366304-0.2386597.2948681.56203732.9585480.003254-0.110390PENDING0.9
CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C186.642634-0.408318-0.231409-2.2804071.4514840.23267421.62050518.480703-0.0471980.029576...322.301921-0.344248-0.1539427.1431670.0000000.000000-0.034990-0.138842PENDING0.8
..................................................................
C1CCCCC1I.P(CC)(CC)CC293.073124-0.398283-0.231873-1.7548791.7492830.6119720.07695333.0737600.0436060.102410...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588PENDING-1.0
CC(C)I.P(CC)(CC)CC242.902322-0.400897-0.235196-1.7966491.6529880.5172500.24000627.9319480.0490400.106570...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.106588PENDING-1.0
CCCCI.P(CC)(CC)CC274.839894-0.402234-0.230929-1.9971491.6206440.4497725.19476424.5332720.0019530.072709...199.428774-0.365679-0.1529987.9222813.56428823.715433-0.079000-0.10658890-2.0
ClCCCI.P(C)(C)C268.323472-0.410241-0.240026-1.5267621.4101440.4817525.40316025.106049-0.0017580.045916...129.102449-0.369419-0.1467978.23020215.75536819.594742-0.107096-0.09488510-2.0
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90 rows × 24 columns

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" ], "text/plain": [ " AlkI_SASA AlkI_HOMO \\\n", "CI.P(CCC)(CCC)CCC 186.642634 -0.408318 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 274.839894 -0.402234 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 186.642634 -0.408318 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 252.281108 -0.424785 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 186.642634 -0.408318 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 293.073124 -0.398283 \n", "CC(C)I.P(CC)(CC)CC 242.902322 -0.400897 \n", "CCCCI.P(CC)(CC)CC 274.839894 -0.402234 \n", "ClCCCI.P(C)(C)C 268.323472 -0.410241 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 262.917309 -0.397556 \n", "\n", " AlkI_LUMO AlkI_EA \\\n", "CI.P(CCC)(CCC)CCC -0.231409 -2.280407 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.230929 -1.997149 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.231409 -2.280407 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.265952 -0.441051 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.231409 -2.280407 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.231873 -1.754879 \n", "CC(C)I.P(CC)(CC)CC -0.235196 -1.796649 \n", "CCCCI.P(CC)(CC)CC -0.230929 -1.997149 \n", "ClCCCI.P(C)(C)C -0.240026 -1.526762 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.237575 -1.573800 \n", "\n", " AlkI_Dipole AlkI_C_1_BV \\\n", "CI.P(CCC)(CCC)CCC 1.451484 0.232674 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.620644 0.449772 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.451484 0.232674 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.130045 0.456235 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 1.451484 0.232674 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 1.749283 0.611972 \n", "CC(C)I.P(CC)(CC)CC 1.652988 0.517250 \n", "CCCCI.P(CC)(CC)CC 1.620644 0.449772 \n", "ClCCCI.P(C)(C)C 1.410144 0.481752 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 1.724610 0.626968 \n", "\n", " AlkI_C_1_SASA AlkI_C_1_P_int \\\n", "CI.P(CCC)(CCC)CCC 21.620505 18.480703 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 5.194764 24.533272 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 21.620505 18.480703 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 4.270924 26.443056 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 21.620505 18.480703 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.076953 33.073760 \n", "CC(C)I.P(CC)(CC)CC 0.240006 27.931948 \n", "CCCCI.P(CC)(CC)CC 5.194764 24.533272 \n", "ClCCCI.P(C)(C)C 5.403160 25.106049 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.000000 0.000000 \n", "\n", " AlkI_C_1_charge \\\n", "CI.P(CCC)(CCC)CCC -0.047198 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.001953 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.047198 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.000588 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.047198 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.043606 \n", "CC(C)I.P(CC)(CC)CC 0.049040 \n", "CCCCI.P(CC)(CC)CC 0.001953 \n", "ClCCCI.P(C)(C)C -0.001758 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.084700 \n", "\n", " AlkI_C_1_electrophilicity ... \\\n", "CI.P(CCC)(CCC)CCC 0.029576 ... \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.072709 ... \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.029576 ... \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.009899 ... \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.029576 ... \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 0.102410 ... \n", "CC(C)I.P(CC)(CC)CC 0.106570 ... \n", "CCCCI.P(CC)(CC)CC 0.072709 ... \n", "ClCCCI.P(C)(C)C 0.045916 ... \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.129850 ... \n", "\n", " Phos_Volume Phos_HOMO \\\n", "CI.P(CCC)(CCC)CCC 271.575185 -0.365362 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 335.813928 -0.366304 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 322.301921 -0.344248 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 199.428774 -0.365679 \n", "CC(C)I.P(CC)(CC)CC 199.428774 -0.365679 \n", "CCCCI.P(CC)(CC)CC 199.428774 -0.365679 \n", "ClCCCI.P(C)(C)C 129.102449 -0.369419 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 263.550839 -0.355563 \n", "\n", " Phos_LUMO Phos_IP \\\n", "CI.P(CCC)(CCC)CCC -0.152770 7.825907 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.238659 7.294868 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.153942 7.143167 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.152998 7.922281 \n", "CC(C)I.P(CC)(CC)CC -0.152998 7.922281 \n", "CCCCI.P(CC)(CC)CC -0.152998 7.922281 \n", "ClCCCI.P(C)(C)C -0.146797 8.230202 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.153386 7.520335 \n", "\n", " Phos_C_1_SASA Phos_C_1_P_int \\\n", "CI.P(CCC)(CCC)CCC 1.822806 28.380926 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.562037 32.958548 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.000000 0.000000 \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC 3.564288 23.715433 \n", "CC(C)I.P(CC)(CC)CC 3.564288 23.715433 \n", "CCCCI.P(CC)(CC)CC 3.564288 23.715433 \n", "ClCCCI.P(C)(C)C 15.755368 19.594742 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C 0.072002 22.730716 \n", "\n", " Phos_C_1_electrophilicity \\\n", "CI.P(CCC)(CCC)CCC -0.078216 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.003254 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.034990 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.079000 \n", "CC(C)I.P(CC)(CC)CC -0.079000 \n", "CCCCI.P(CC)(CC)CC -0.079000 \n", "ClCCCI.P(C)(C)C -0.107096 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.056090 \n", "\n", " Phos_P_1_charge yield \\\n", "CI.P(CCC)(CCC)CCC -0.126537 10 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 0 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 50 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 -0.110390 PENDING \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C -0.138842 PENDING \n", "... ... ... \n", "C1CCCCC1I.P(CC)(CC)CC -0.106588 PENDING \n", "CC(C)I.P(CC)(CC)CC -0.106588 PENDING \n", "CCCCI.P(CC)(CC)CC -0.106588 90 \n", "ClCCCI.P(C)(C)C -0.094885 10 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -0.136051 1 \n", "\n", " priority \n", "CI.P(CCC)(CCC)CCC 1.0 \n", "CCCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.0 \n", "CI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 1.0 \n", "BrCCI.P(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 0.9 \n", "CI.CC(C)(C)P(C(C)(C)C)C(C)(C)C 0.8 \n", "... ... \n", "C1CCCCC1I.P(CC)(CC)CC -1.0 \n", "CC(C)I.P(CC)(CC)CC -1.0 \n", "CCCCI.P(CC)(CC)CC -2.0 \n", "ClCCCI.P(C)(C)C -2.0 \n", "CC(C)(C)I.P(C(C)C)(C(C)C)C(C)C -2.0 \n", "\n", "[90 rows x 24 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('./reaction_space_round1.csv',index_col=0,header=0)\n", "df['yield'].iat[0] = 10\n", "df['yield'].iat[1] = 0\n", "df['yield'].iat[2] = 50\n", "\n", "df.to_csv('./reaction_space_round2.csv')\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look at the feature analysis" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# call the function using the latest searchspace file\n", "shap_values, mean_abs_shap_values = ScopeBO.feature_analysis(filename=\"reaction_space_round2.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These feature values provide the relative importance of the features for the BO surrogate model predictions. They however do not provide the sign. By extending the function input, we can also generate a beeswarm plot for further insights. This gives the impact on the model output for each sample and feature so that the can also see the sign of the SHAP values (red = high feature value; blue = low feature value)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap_values, mean_abs_shap_values = ScopeBO.feature_analysis(filename=\"reaction_space_round2.csv\",plot_type=['beeswarm'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predict the performance for unseen samples" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are now 27 entries with results in the reaction scope.\n" ] } ], "source": [ "# currently, we only have 6 scope entries which is not sufficient to run the prediction algorithm\n", "\n", "# generate some more scope entries by adding results for other search space examples\n", "for i in range(6,27,1):\n", " df['yield'].iat[i] = np.random.randint(0,101)\n", "df.to_csv(\"reaction_space_round9.csv\")\n", "print(f\"There are now {len(df[df['yield'] != 'PENDING'])} entries with results in the reaction scope.\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This might take a moment. Please wait!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Model optimization progress: 100%|██████████| 1000/1000 [00:26<00:00, 37.44it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model training completed.\n", "\n", "Here are the model parameters using scaled features:\n", "AlkI_I_1_nucleophilicity: -3.5195383925926667\n", "Phos_SASA: 6.171632035016945\n", "Phos_HOMO: -2.705908055571715\n", "Intercept: 44.33333333333333\n", "\n", "Model statistics (on outer CV splits):\n", "Mean RMSE: 34.80141947425061\n", "Mean R2 value: 0.045116425650183985\n", "\n", "Predictions have been saved in the file 'mlr_predictions.csv' in the folder '.'.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# run the prediction function again\n", "model_parameters = ScopeBO.predict_performance(filename=\"reaction_space_round9.csv\", # search space file\n", " objective=\"yield\", # objective that is predicted\n", " further_objectives=None, # list of further objectives (None in this case)\n", " show_pred_for_cut=True # also show predictions for cut samples\n", " ) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also predict the performance using a Gaussian process (same model as in the substrate suggestions of ScopeBO). In this case, the uncertainty of the predictions is also shown with smaller predicted points having a higher uncertainty." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions saved as gp_predictions.csv.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# run the prediction function again\n", "model_parameters = ScopeBO.expected_improvement(filename=\"reaction_space_round9.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE: The yield values were chosen randomly in this example case and are not chemically meaningful. This also influence the quality of the shown predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the Vendi score" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ScopeBO().get_vendi_score(filename=\"reaction_space_round2.csv\",objectives=['yield'])" ] } ], "metadata": { "kernelspec": { "display_name": "scope_bo", "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": "3.9.22" } }, "nbformat": 4, "nbformat_minor": 2 }