From f52e40f12ba143f651f2e423569e086373b21782 Mon Sep 17 00:00:00 2001 From: Sulstice Date: Mon, 4 Apr 2022 00:02:10 -0400 Subject: [PATCH] ss-main: added depedency --- .../GlobalChemExtensions_Demonstration.ipynb | 1284 +++++++++++++++++ 1 file changed, 1284 insertions(+) create mode 100644 example_notebooks/GlobalChemExtensions_Demonstration.ipynb diff --git a/example_notebooks/GlobalChemExtensions_Demonstration.ipynb b/example_notebooks/GlobalChemExtensions_Demonstration.ipynb new file mode 100644 index 00000000..d7f4022d --- /dev/null +++ b/example_notebooks/GlobalChemExtensions_Demonstration.ipynb @@ -0,0 +1,1284 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "GlobalChemExtensions_Demonstration.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PY-f8bB8jpqi", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7e53952a-60e6-4af4-e016-0207865fa09a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (3.1.0)\n", + "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from h5py) (1.18.3)\n", + "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py) (1.5.2)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (3.10.0.2)\n", + "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (0.37.1)\n" + ] + } + ], + "source": [ + "# Environment Setup\n", + "!pip install h5py\n", + "!pip install typing-extensions\n", + "!pip install wheel\n", + "!rm -rf global-chem\n", + "!git clone -q https://github.com/Sulstice/global-chem\n", + "\n", + "!pip install -q global-chem-extensions --upgrade" + ] + }, + { + "cell_type": "code", + "source": [ + "# Header\n", + "\n", + "from global_chem import GlobalChem\n", + "from global_chem_extensions import GlobalChemExtensions\n", + "\n", + "gc = GlobalChem()\n", + "gce = GlobalChemExtensions()" + ], + "metadata": { + "id": "NoYEtzDf1jYF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# GlobalChem Molecule\n", + "\n", + "global_chem_molecule = gce.initialize_globalchem_molecule('C1CCCC1')\n", + "attributes = global_chem_molecule.get_attributes()\n", + "print (attributes)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rI182ix3lVT5", + "outputId": "2ed3e8e7-859e-46ca-bf95-74bdbc8f0899" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'molecular_weight': 70.07825032, 'logp': 1.9505000000000001, 'h_bond_donor': 0, 'h_bond_acceptors': 0, 'rotatable_bonds': 0, 'number_of_atoms': 5, 'molar_refractivity': 23.084999999999994, 'topological_surface_area_mapping': 0.0, 'formal_charge': 0, 'heavy_atoms': 5, 'num_of_rings': 1}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# CGenFF Functions\n", + "\n", + "# Read in CGenFF Molecule\n", + "\n", + "cgenff_molecule = gce.initialize_cgenff_molecule(\n", + " 'global-chem/example_data/forcefield_parameterization/perfluorobutanoic_acid.str'\n", + ")\n", + "\n", + "cgenff_molecule.update_bonded_dataframe(\n", + " 'CG302-CG312-CG312',\n", + " 200,\n", + " force_constant=True,\n", + " harmonic_value=False,\n", + " multiplicity=False\n", + ")\n", + "\n", + "# print (cgenff_molecule)\n", + "# print (cgenff_molecule.convert_to_smiles())\n", + "# print (cgenff_molecule.bond_parameters)\n", + "# print (cgenff_molecule.angle_parameters)\n", + "# print (cgenff_molecule.dihedral_parameters)\n", + "\n", + "cgenff_molecule.write_stream_file('new.str')\n", + "\n", + "!cat new.str" + ], + "metadata": { + "id": "umfnRBGNCHau" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# GAFF2 Functions\n", + "\n", + "# Read in GAFF2 Molecule\n", + "\n", + "gaff2_molecule = gce.initialize_gaff2_molecule(\n", + " 'global-chem/example_data/forcefield_parameterization/amber_gws.frcmod'\n", + ")\n", + "\n", + "gaff2_molecule.update_bonded_dataframe(\n", + " 'ca-ca-ns-hn',\n", + " 200,\n", + " force_constant=True,\n", + " harmonic_value=False,\n", + " multiplicity=False,\n", + " fluctuation=False,\n", + ")\n", + "\n", + "# print (gaff2_molecule)\n", + "# print (gaff2_molecule.dihedral_parameters)\n", + "\n", + "gaff2_molecule.write_frcmod_file('new.frcmod')\n", + "\n", + "!cat new.frcmod" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6TlLEyZRtC7h", + "outputId": "5ac5e8a3-161d-4c1b-9595-5f9947c474be" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Remark line goes here\n", + "MASS\n", + "BONDS\n", + "ca-ns\t1.412\t315.20\n", + "c-ns\t1.379\t356.20\n", + "hn-ns\t1.013\t527.30\n", + "\n", + "ANGLE\n", + "c-ns-ca\t123.710\t65.700\n", + "ca-ns-hn\t116.000\t48.000\n", + "ns-c-o\t123.050\t113.800\n", + "c-ns-hn\t117.550\t48.700\n", + "ca-ca-ns\t120.190\t85.600\n", + "c3-c-ns\t115.180\t84.300\n", + "\n", + "DIHE\n", + "o-c-ns-ca\t4\t10.000\t180.000\t2.000\n", + "c3-c-ns-ca\t1\t0.750\t180.000\t-2.000\n", + "c3-c-ns-ca\t1\t0.500\t0.000\t3.000\n", + "o-c-ns-hn\t1\t2.500\t180.000\t-2.000\n", + "o-c-ns-hn\t1\t2.000\t0.000\t1.000\n", + "ca-ca-ns-c\t1\t0.950\t180.000\t2.000\n", + "ns-c-c3-c3\t1\t0.000\t180.000\t-4.000\n", + "ns-c-c3-c3\t1\t0.710\t180.000\t2.000\n", + "ca-ca-ns-hn\t4\t1.800\t180.000\t2.000\n", + "c3-c-ns-hn\t4\t10.000\t180.000\t2.000\n", + "\n", + "IMPROPER\n", + "ca-ca-ca-ns-1.1\t180.0\t2.0 \n", + "ca-ca-ca-ha-1.1\t180.0\t2.0 \n", + "c3-ns-c-o-10.5\t180.0\t2.0 \n", + "c-ca-ns-hn-1.1\t180.0\t2.0 \n", + "ca-h4-ca-nb-1.1\t180.0\t2.0 \n", + "\n", + "\n", + "NONBON\n", + "ca-h4\t1.1\t180.0\n", + "ca-h4\t1.1\t180.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Compute CGenFF Dissimmilarity Score Between Two Molecule CGenFF Stream File\n", + "\n", + "dissimilar_score = gce.compute_cgenff_dissimilar_score(\n", + " 'global-chem/example_data/forcefield_parameterization/perfluorobutanoic_acid.str',\n", + " 'global-chem/example_data/forcefield_parameterization/perfluorohexanoic_acid.str',\n", + ")\n", + "\n", + "print (\"CGenFF Dissimilar Score: %s\" % dissimilar_score)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "i4xnE5Xe2874", + "outputId": "637c13b5-97be-45ca-84ce-8b7650c80383" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CGenFF Dissimilar Score: 26\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Check the Validity of the SMILES\n", + "\n", + "smiles_list = gc.get_all_smiles()\n", + "\n", + "sucesses, failures = gce.verify_smiles(\n", + " smiles_list,\n", + " rdkit=False, \n", + " partial_smiles=False,\n", + " return_failures=True,\n", + " pysmiles=False,\n", + " molvs=False,\n", + " deepsmiles=True\n", + ")\n", + "\n", + "total = len(sucesses) + len(failures)\n", + "print (\"Percantage of Accepted SMILES: %s\" % ((len(sucesses) / total) * 100))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IMm6kQvpSIQr", + "outputId": "2ef893ac-93ee-40ba-cfd3-d142f8ba7f06" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Percantage of Accepted SMILES: 99.25666812418015\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Sunbursting\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "smiles_list = list(gc.get_node_smiles('emerging_perfluoroalkyls').values())\n", + "GlobalChemExtensions().sunburst_chemical_list(smiles_list, save_file=False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "UqTUcQIzjx5x", + "outputId": "a630c710-33ae-411e-e445-5c8ac75a1c85" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# PCA Analysis\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "node = gc.get_node_smiles('pihkal')\n", + "smiles_list = list(node.values())\n", + "names_list = list(node.keys())\n", + "\n", + "for k, v in node.items():\n", + " print (k, v)\n", + "GlobalChemExtensions().node_pca_analysis(smiles_list, save_file=False)\n" + ], + "metadata": { + "id": "jSEjREZPycAE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "bb077794-b3a5-4460-8df9-0c1a16daba85" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "alpha-ethylmescaline CCC(N)CC1=CC(=C(OC)C(=C1)OC)OC\n", + "4-allyloxy-3,5-dimethoxyphenethylamine COC1=CC(=CC(=C1OCC=C)OC)CCN\n", + "2,5-dimethoxy-4-methylthioamphetamine COC1=CC(=C(OC)C=C1CC(C)N)SC\n", + "2,5-dimethoxy-4-ethylthioamphetamine CCSC1=C(OC)C=C(CC(C)N)C(=C1)OC\n", + "2,5-dimethoxy-4-(i)-propylthioamphetamine COC1=CC(=C(OC)C=C1CC(C)N)SC(C)C\n", + "2,5-dimethoxy-4-phenylthioamphetamine COC1=CC(=C(OC)C=C1CC(C)N)SC2=CC=CC=C2\n", + "2,5-dimethoxy-4-(n)-propylthioamphetamine CCCSC1=C(OC)C=C(CC(C)N)C(=C1)OC\n", + "dimoxamine CCC(N)CC1=CC(=C(C)C=C1OC)OC\n", + "asymbescaline CCOC1=CC(=CC(=C1OCC)OC)CCN\n", + "buscaline CCCCOC1=C(OC)C=C(CCN)C=C1OC\n", + "2,5-dimethoxy-4,n-dimethylamphetamine CNC(C)CC1=CC(=C(C)C=C1OC)OC\n", + "4-methyl-2,5-bis-(methylthio)amphetamine CSC1=C(C)C=C(SC)C(=C1)CC(C)N\n", + "4-bromo-2,5-beta-trimethoxyphenethylamine COC(CN)C1=CC(=C(Br)C=C1OC)OC\n", + "4-methyl-2,5,beta-trimethoxyphenethylamine COC(CN)C1=CC(=C(C)C=C1OC)OC\n", + "beta-methoxy-3,4-methylenedioxyphenethylamine COC(CN)C1=CC(=C(C)C=C1OC)OC\n", + "2,5-dimethoxy-beta-hydroxy-4-methylphenethylamine CC1=C(O)C=C(C(O)CN)C(=C1)Br\n", + "beta-methoxymescaline COC(CN)C1=CC(=C(OC)C(=C1)OC)OC\n", + "3,5-dimethoxy-4-bromoamphetamine COC1=C(Br)C(=CC(=C1)CC(C)N)OC\n", + "2-bromo-4,5-methylenedioxyamphetamine CC(N)CC1=C(Br)C=C2OCOC2=C1\n", + "4-bromo-2,5-dimethoxyphenethylamine [CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[Br:12])[CH2:8][CH2:9][NH2:10]\n", + "4-benzyloxy-3,5-dimethoxyamphetamine COC1=C(OCC2=CC=CC=C2)C(=CC(=C1)CC(C)N)OC\n", + "2,5-dimethoxy-4-chlorophenethylamine COC1=CC(=C(OC)C=C1Cl)CCN\n", + "2,5-dimethoxy-4-methylphenethylamine [CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[CH3:12])[CH2:8][CH2:9][NH2:10]\n", + "2,5-dimethoxy-4-ethylphenethylamine CCC1=CC(=C(CCN)C=C1OC)OC\n", + "3,5-dimethoxy-4-ethoxyamphetamine CCOC1=C(OC)C=C(CC(C)N)C=C1OC\n", + "2,5-dimethoxy-4-fluorophenethylamine COC1=CC(=C(OC)C=C1F)CCN\n", + "2,5-dimethoxy-3,4-dimethylphenethylamine COC1=CC(=C(OC)C(=C1C)C)CCN\n", + "2,5-dimethoxy-3,4-(trimethylene)phenethylamine COC1=CC(=C(OC)C2=C1CCC2)CCN\n", + "2,5-dimethoxy-3,4-(tetramethylene)phenethylamine COC1=CC(=C(OC)C2=C1CCCC2)CCN\n", + "3,6-dimethoxy-4-(2-aminoethyl)benzonorbornane COC1=CC(=C(OC)C2=C1C3CCC2C3)CCN\n", + "1,4-dimethoxynaphthyl-2-ethylamine COC1=CC(=C(OC)C2=C1C=CC=C2)CCN\n", + "2,5-dimethoxyphenethylamine COC1=CC(=C(OC)C=C1)CCN\n", + "2,5-dimethoxy-4-iodophenethylamine [CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[I:12])[CH2:8][CH2:9][NH2:10]\n", + "2,5-dimethoxy-4-nitrophenethylamine COC1=C(CCN)C=C(OC)C(=C1)[N](=O)=O\n", + "2,5-dimethoxy-4-(i)-propoxyphenethylamine COC1=C(CCN)C=C(OC)C(=C1)OC(C)C\n", + "2,5-dimethoxy-4-(n)-propylphenethylamine CCCOC1=CC(=C(CCN)C=C1OC)OC\n", + "cyclopropylmescaline COC1=C(CCN)C=C(OC)C(=C1)OCC2CC2\n", + "2,5-dimethoxy-4-methylseleneophenethylamine COC1=C(CCN)C=C(OC)C(=C1)[Se]C\n", + "2,5-dimethoxy-4-methylthiophenethylamine COC1=C(CCN)C=C(OC)C(=C1)SC\n", + "2,5-dimethoxy-4-ethylthiophenethylamine [CH3:30][CH2:29][S:12][C:1]1=[CH:2][C:3](=[C:4]([CH2:8][CH2:9][NH2:10])[CH:5]=[C:6]1[O:13][CH3:20])[O:16][CH3:11]\n", + "2,5-dimethoxy-4-(i)-propylthiophenethylamine COC1=C(CCN)C=C(OC)C(=C1)SC(C)C\n", + "2,6-dimethoxy-4-(i)-propylthiophenethylamine) COC1=C(CCN)C(=CC(=C1)SC(C)C)OC\n", + "2,5-dimethoxy-4-(n)-propylthiophenethylamine [CH3:33][CH2:30][CH2:29][S:12][C:1]1=[CH:2][C:3](=[C:4]([CH2:8][CH2:9][NH2:10])[CH:5]=[C:6]1[O:13][CH3:20])[O:16][CH3:11]\n", + "2,5-dimethoxy-4-cyclopropylmethylthiophenethylamine COC1=C(CCN)C=C(OC)C(=C1)SCC2CC2\n", + "2,5-dimethoxy-4-(t)-butylthiophenethylamine COC1=C(CCN)C=C(OC)C(=C1)SC(C)(C)C\n", + "2,5-dimethoxy-4-(2-methoxyethylthio)phenethylamine COCCSC1=CC(=C(CCN)C=C1OC)OC\n", + "2,5-dimethoxy-4-cyclopropylthiophenethylamine COC1=C(CCN)C=C(OC)C(=C1)SC2CC2\n", + "2,5-dimethoxy-4-(s)-butylthiophenethylamine CCC(C)SC1=CC(=C(CCN)C=C1OC)OC\n", + "2,5-dimethoxy-4-(2-fluoroethylthio)phenethylamine COC1=C(CCN)C=C(OC)C(=C1)SCCF\n", + "3,5-dimethoxy-4-trideuteromethoxy-phenethylamine COC1=CC(=CC(=C1OC)OC)CCN\n", + "3,4,5-trimethoxy-beta,beta-dideuterophenethylamine COC1=CC(=CC(=C1OC)OC)CCN\n", + "3,5-dimethoxy-4-methylphenethylamine COC1=CC(=CC(=C1C)OC)CCN\n", + "2,4-dimethoxyamphetamine COC1=CC=C(CC(C)N)C(=C1)OC\n", + "2,5-dimethoxyamphetamine COC1=CC=C(OC)C(=C1)CC(C)N\n", + "3,4-dimethoxyamphetamine COC1=CC=C(CC(C)N)C=C1OC\n", + "2-(2,5-dimethoxy-4-methylphenyl)cyclopropylamine COC1=CC(=C(OC)C=C1C)C2CC2N\n", + "3,4-dimethoxy-beta-hydroxyphenethylamine COC1=CC=C(C=C1OC)C(O)CN\n", + "2,5-dimethoxy-3,4-methylenedioxyamphetamine COC1=C2OCOC2=C(OC)C(=C1)CC(C)N\n", + "2,3-dimethoxy-4,5-methylenedioxyamphetamine COC1=C(CC(C)N)C=C2OCOC2=C1OC\n", + "3,4-dimethoxyphenethylamine COC1=CC=C(CCN)C=C1OC\n", + "2,5-dimethoxy-4-(n)-amylamphetamine CCCCCC1=CC(=C(CC(C)N)C=C1OC)OC\n", + "2,5-dimethoxy-4-bromoamphetamine COC1=CC(=C(OC)C=C1Br)CC(C)N\n", + "2,5-dimethoxy-4-(n)-butylamphetamine CCCCC1=CC(=C(CC(C)N)C=C1OC)OC\n", + "2,5-dimethoxy-4-chloroamphetamine COC1=CC(=C(OC)C=C1Cl)CC(C)N\n", + "2,5-dimethoxy-4-(2-fluoroethyl)-amphetamine COC1=CC(=C(OC)C=C1CCF)CC(C)N\n", + "2,5-dimethoxy-4-ethylamphetamine CCC1=CC(=C(CC(C)N)C=C1OC)OC\n", + "2,5-dimethoxy-4-iodoamphetamine COC1=CC(=C(OC)C=C1I)CC(C)N\n", + "2,5-dimethoxy-4-methylamphetamine COC1=CC(=C(OC)C=C1C)CC(C)N\n", + "2,6-dimethoxy-4-methylamphetamine COC1=C(CC(C)N)C(=CC(=C1)C)OC\n", + "2,5-dimethoxy-4-nitroamphetamine COC1=C(CC(C)N)C=C(OC)C(=C1)[N](=O)=O\n", + "2,5-dimethoxy-4-(n)-propylamphetamine CCCC1=CC(=C(CC(C)N)C=C1OC)OC\n", + "3,5-dimethoxy-4-ethoxyphenethylamine CCOC1=C(OC)C=C(CCN)C=C1OC\n", + "2,4,5-triethoxyamphetamine CCOC1=C(CC(C)N)C=C(OCC)C(=C1)OCC\n", + "2,4-diethoxy-5-methoxyamphetamine CCOC1=C(CCN)C=C(OC)C(=C1)OCC\n", + "2,5-diethoxy-4-methoxyamphetamine CCOC1=C(CCN)C=C(OCC)C(=C1)OC\n", + "4,5-dimethoxy-2-ethoxyamphetamine CCOC1=C(CCN)C=C(OC)C(=C1)OC\n", + "2-ethylamino-1-(3,4-methylenedioxyphenyl)butane CCC(N)CC1=CC=C2OCOC2=C1\n", + "2-ethylamino-1-(3,4-methylenedioxyphenyl)pentane CCCC(CC1=CC=C2OCOC2=C1)NCC\n", + "6-(2-aminopropyl)-5-methoxy-2-methyl-2,3-dihydrobenzofuran COC1=C(CC(C)N)C=C2OC(C)CC2=C1\n", + "6-(2-aminopropyl)-2,2-dimethyl-5-methoxy-2,3-dihydrobenzofuran COC1=C(CC(C)N)C=C2OC(C)(C)CC2=C1\n", + "n-hydroxy-n-methyl-3,4-methylenedioxyamphetamine CC(CC1=CC=C2OCOC2=C1)N(C)O\n", + "2,5-dimethoxy-3,4-(trimethylene)amphetamine COC1=C2CCCC2=C(OC)C(=C1)CC(C)N\n", + "2,5-dimethoxy-3,4-(tetramethylene)amphetamine COC1=C2CCCCC2=C(OC)C(=C1)CC(C)N\n", + "3,6-dimethoxy-4-(2-aminopropyl)benzonorbornane COC1=C2C3CCC(C3)C2=C(OC)C(=C1)CC(C)N\n", + "2,5-dimethoxy-3,4-dimethylamphetamine COC1=CC(=C(OC)C(=C1C)C)CC(C)N\n", + "1,4-dimethoxynaphthyl-2-isopropylamine COC1=CC(=C(OC)C2=C1C=CC=C2)CC(C)N\n", + "2,5-dimethoxy-4-ethylthio-n-hydroxyphenethylamine \n", + "2,5-dimethoxy-n-hydroxy-4-(n)-propylthiophenethylamine CCCSC1=CC(=C(CCNO)C=C1OC)OC\n", + "2,5-dimethoxy-4-(s)-butylthio-n-hydroxyphenethylamine CCC(C)SC1=CC(=C(CCNO)C=C1OC)OC\n", + "2,5-dimethoxy-n,n-dimethyl-4-iodoamphetamine COC1=CC(=C(OC)C=C1I)CC(C)N(C)C\n", + "isomescaline COC1=C(OC)C(=C(CCN)C=C1)OC\n", + "isoproscaline COC1=CC(=CC(=C1OC(C)C)OC)CCN\n", + "5-ethoxy-2-methoxy-4-methylamphetamine CCOC1=CC(=C(OC)C=C1C)CC(C)N\n", + "2-amino-1-(3,4-methylenedioxyphenyl)butane CCC(N)CC1=CC=C2OCOC2=C1\n", + "3-methoxy-4,5-methylenedioxyphenethylamine COC1=C2OCOC2=CC(=C1)CCN\n", + "mescaline C[O:7][C:2]1=[CH:1][C:6](=[CH:5][C:4](=[C:3]1[O:9]C)[O:20]C)[CH2:10][CH2:11][NH2:12]\n", + "4-methoxyamphetamine CC(CC1=CC=C(C=C1)OC)N\n", + "2,n-dimethyl-4,5-methylenedioxyamphetamine CNC(C)CC1=C(C)C=C2OCOC2=C1\n", + "methallylescaline COC1=CC(=CC(=C1OCC(C)=C)OC)CCN\n", + "3,4-methylenedioxyamphetamine CC(N)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n- allylamphetamine CC(CC1=CC=C2OCOC2=C1)NCC=C\n", + "3,4-methylenedioxy-n-butylamphetamine CCCCNC(C)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n-benzylamphetamine CC(CC1=CC=C2OCOC2=C1)NCC3=CC=CC=C3\n", + "3,4-methylenedioxy-ncyclopropylmethylamphetamine CC(CC1=CC=C2OCOC2=C1)NCC3CC3\n", + "3,4-methylenedioxy-n,n-dimethylamphetamine CC(CC1=CC=C2OCOC2=C1)N(C)C\n", + "3,4-methylenedioxy-n-ethylamphetamine CCNC(C)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n-(2-hydroxyethyl)amphetamine CC(CC1=CC=C2OCOC2=C1)NCCO\n", + "(3,4-methylenedioxy-n-isopropylamphetamine) CC(C)NC(C)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n-methylamphetamine CNC(C)CC1=CC=C2OCOC2=C1\n", + "3,4-ethylenedioxy-n-methylamphetamine CNC(C)CC1=CC=C2OCCOC2=C1\n", + "3,4-methylenedioxy-n-methyoxyamphetamine CONC(C)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n-(2-methoxyethyl)amphetamine COCCNC(C)CC1=CC=C2OCOC2=C1\n", + "a,a,n-trimethyl-3,4-methylenedioxy-phenethylamine CNC(C)(C)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-n-hydroxyamphetamine CC(CC1=CC=C2OCOC2=C1)NO\n", + "3,4-methylenedioxyphenethylamine NCCC1=CC=C2OCOC2=C1\n", + "a,a-dimethyl-3,4-methylenedioxy-phenethylamine CC(C)(N)CC1=CC=C2OCOC2=C1\n", + "3,4-methylenedioxy-npropargylamphetamine) CC(CC1=CC=C2OCOC2=C1)NCC#C\n", + "3,4-methylenedioxy-n-propylamphetamine CCCNC(C)CC1=CC=C2OCOC2=C1\n", + "metaescaline CCOC1=CC(=CC(=C1OC)OC)CCN\n", + "3-methoxy-4,5-ethylenedioxyamphetamine COC1=C2OCCOC2=CC(=C1)CCN\n", + "4,5-diethoxy-2-methoxyamphetamine CCOC1=CC(=C(OC)C=C1OCC)CC(C)N\n", + "2,5-dimethoxy-4-ethoxyamphetamine CCOC1=CC(=C(CC(C)N)C=C1OC)OC\n", + "3-methoxy-4-ethoxyphenethylamine CCOC1=CC=C(CCN)C=C1OC\n", + "5-bromo-2,4-dimethoxyamphetamine COC1=CC(=C(CC(C)N)C=C1Br)OC\n", + "2,4-dimethoxy-5-methylthioamphetamine COC1=C(CC(C)N)C=C(SC)C(=C1)OC\n", + "2,5-dimethoxy-n-methylamphetamine CNC(C)CC1=C(OC)C=CC(=C1)OC\n", + "4-bromo-2,5-dimethoxy-n-methylamphetamine CNC(C)CC1=C(OC)C=C(Br)C(=C1)OC\n", + "2-methylamino-1-(3,4-methylenedioxyphenyl)butane CCC(CC1=CC=C2OCOC2=C1)NC\n", + "2-methylamino-1-(3,4-methylenedioxyphenyl)pentane CCCC(CC1=CC=C2OCOC2=C1)NC\n", + "4-methoxy-n-methylamphetamine CNC(C)CC1=CC=C(OC)C=C1\n", + "2-methoxy-n-methyl-4,5-methylenedioxyamphetamine CNC(C)CC1=C(OC)C=C2OCOC2=C1\n", + "3-methoxy-4,5-methylenedioxyamphetamine COC1=C2OCOC2=CC(=C1)CC(C)N\n", + "2-methoxy-4,5-methylenedioxyamphetamine COC1=C(CC(C)N)C=C2OCOC2=C1\n", + "2-methoxy-3,4-methylenedioxyamphetamine COC1=C2OCOC2=CC=C1CC(C)N\n", + "4-methoxy-2,3-methylenedioxyamphetamine COC1=CC=C(CC(C)N)C2=C1OCO2\n", + "2,4-dimethoxy-5-ethoxyamphetamine CCOC1=CC(=C(OC)C=C1OC)CC(C)N\n", + "metaproscaline CCCOC1=CC(=CC(=C1OC)OC)CCN\n", + "2,5-dimethoxy-4-(n)-propoxyamphetamine COc1cc(OC)c(cc1OCCC)CC(C)N\n", + "4,5-dimethoxy-2-methylthioamphetamine COC1=CC(=C(SC)C=C1OC)CC(C)N\n", + "proscaline CCCOC1=C(OC)C=C(CCN)C=C1OC\n", + "phenescaline COC1=CC(=CC(=C1OCCCC2=CC=CC=C2)OC)CCN\n", + "phenethylamine NCCC1=CC=CC=C1\n", + "3,5-dimethoxy-4-(2-propynyloxy)phenethylamine COC1=CC(=CC(=C1OCC=C)OC)CCN\n", + "symbescaline CCOC1=CC(=CC(=C1OC)OCC)CCN\n", + "2,3,4,5-tetramethoxyamphetamine COC1=C(OC)C(=C(OC)C(=C1)CC(C)N)OC\n", + "3-thioasymbescaline [CH3:16][CH2:15][O:14][C:8]1=[C:1]([S:10][CH2:11][CH3:17])[CH:2]=[C:3]([CH2:5][CH2:6][NH2:7])[CH:4]=[C:9]1[O:12][CH3:13]\n", + "4-thioasymbescaline CCOC1=CC(=CC(=C1SCC)OC)CCN\n", + "5-thioasymbescaline CCOC1=CC(=CC(=C1OCC)SC)CCN\n", + "4-thiobuscaline CCCCSC1=C(OC)C=C(CCN)C=C1OC\n", + "3-thioescaline CCOC1=C(OC)C=C(CCN)C=C1SC\n", + "4-thioescaline [CH3:16][CH2:11][S:10][C:8]1=[C:1]([O:12][CH3:13])[CH:2]=[C:3]([CH2:5][CH2:6][NH2:7])[CH:4]=[C:9]1[O:14][CH3:15]\n", + "2-thioisomescaline COC1=C(OC)C(=C(CCN)C=C1)SC\n", + "3-thiomescaline COC1=C(SC)C(=C(CCN)C=C1)OC\n", + "4-thioisomescaline COC1=C(CCN)C=CC(=C1OC)SC\n", + "2-thiomescaline COC1=CC(=CC(=C1OC)SC)CCN\n", + "4-thiomescaline COC1=CC(=CC(=C1SC)OC)CCN\n", + "3,4,5-trimethoxyamphetamine COC1=CC(=CC(=C1OC)OC)CC(C)N\n", + "2,4,5-trimethoxyamphetamine COC1=C(CC(C)N)C=C(OC)C(=C1)OC\n", + "2,3,4-trimethoxyamphetamine COC1=C(OC)C(=C(CC(C)N)C=C1)OC\n", + "2,3,5-trimethoxyamphetamine COC1=CC(=C(OC)C(=C1)OC)CC(C)N\n", + "2,3,6-trimethoxyamphetamine COC1=CC=C(OC)C(=C1CC(C)N)OC\n", + "2,4,6-trimethoxyamphetamine COC1=CC(=C(CC(C)N)C(=C1)OC)OC\n", + "3-thiometaescaline CCSC1=CC(=CC(=C1OC)OC)CCN\n", + "4-thiometaescaline CCOC1=CC(=CC(=C1SC)OC)CCN\n", + "5-thiometaescaline CCOC1=CC(=CC(=C1OC)SC)CCN\n", + "3,4-methylenedioxy-2-methylthioamphetamine CSC1=C2OCOC2=CC=C1CC(C)N\n", + "6-(2-aminopropyl)-5-methoxy-1,3-benzoxathiol COC1=C(CC(C)N)C=C2OCSC2=C1\n", + "2,4,5-trimethoxyphenethylamine COC1=C(CCN)C=C(OC)C(=C1)OC\n", + "4-ethyl-5-methoxy-2-methylthioamphetamine CCC1=CC(=C(CC(C)N)C=C1OC)SC\n", + "4-ethyl-2-methoxy-5-methylthioamphetamine CCC1=CC(=C(CC(C)N)C=C1SC)OC\n", + "5-methoxy-4-methyl-2-methylthioamphetamine COC1=CC(=C(SC)C=C1C)CC(C)N\n", + "2-methoxy-4-methyl-5-methylthioamphetamine COC1=C(CC(C)N)C=C(SC)C(=C1)C\n", + "2-methoxy-4-methyl-5-methylsulfinylamphetamine COC1=C(CC(C)N)C=C(C(=C1)C)[S](C)=O\n", + "thioproscaline CCCSC1=C(OC)C=C(CCN)C=C1OC\n", + "trescaline CCOC1=CC(=CC(=C1OCC)OCC)CCN\n", + "3-thiosymbescaline CCOC1=C(OC)C(=CC(=C1)CCN)SCC\n", + "4-thiosymbescaline CCOC1=CC(=CC(=C1SC)OCC)CCN\n", + "3-thiotrescaline CCOC1=C(OCC)C(=CC(=C1)CCN)SCC\n", + "4-thiotrescaline CCOC1=CC(=CC(=C1SCC)OCC)CCN\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " \n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", + " var css_urls = [];\n", + " \n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " function(Bokeh) {\n", + " \n", + " \n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\\nMolID: @ids
\\n@img{safe}\\n
\\n\"},\"id\":\"1386\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1380\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1366\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1371\",\"type\":\"BasicTicker\"},{\"attributes\":{\"source\":{\"id\":\"1358\",\"type\":\"ColumnDataSource\"}},\"id\":\"1399\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1406\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1381\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1364\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1383\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1362\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1385\",\"type\":\"HelpTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1407\",\"type\":\"BoxAnnotation\"}},\"id\":\"1382\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1380\",\"type\":\"PanTool\"},{\"id\":\"1381\",\"type\":\"WheelZoomTool\"},{\"id\":\"1382\",\"type\":\"BoxZoomTool\"},{\"id\":\"1383\",\"type\":\"SaveTool\"},{\"id\":\"1384\",\"type\":\"ResetTool\"},{\"id\":\"1385\",\"type\":\"HelpTool\"},{\"id\":\"1386\",\"type\":\"HoverTool\"}]},\"id\":\"1387\",\"type\":\"Toolbar\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1376\",\"type\":\"BasicTicker\"}},\"id\":\"1379\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1405\",\"type\":\"Selection\"},{\"attributes\":{\"children\":[[{\"id\":\"1359\",\"subtype\":\"Figure\",\"type\":\"Plot\"},0,0]]},\"id\":\"1409\",\"type\":\"GridBox\"},{\"attributes\":{\"text\":\"Compounds\"},\"id\":\"1360\",\"type\":\"Title\"},{\"attributes\":{\"formatter\":{\"id\":\"1401\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1376\",\"type\":\"BasicTicker\"}},\"id\":\"1375\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1412\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n", + " var render_items = [{\"docid\":\"2cd42450-cc0d-48d1-b498-8fcd10212960\",\"roots\":{\"1412\":\"92bce9ea-c717-4edf-9b06-113a06508d60\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1412" + } + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Deep Layering\n", + "\n", + "gc.initiate_deep_layer_network()\n", + "gc.add_deep_layer(\n", + " [\n", + " 'emerging_perfluoroalkyls',\n", + " 'montmorillonite_adsorption',\n", + " 'common_monomer_repeating_units',\n", + " 'vitamins',\n", + " 'schedule_two',\n", + " ]\n", + ")\n", + "gc.add_deep_layer(\n", + " [\n", + " 'common_warheads_covalent_inhibitors',\n", + " 'privileged_scaffolds',\n", + " 'iupac_blue_book',\n", + " 'schedule_four',\n", + " 'schedule_one',\n", + " ]\n", + ")\n", + "\n", + "gc.print_deep_network()\n", + "deep_layer_network = gc.deep_layer_network\n", + "print (deep_layer_network)" + ], + "metadata": { + "id": "vr8iNR3-NZuh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9241212e-d196-4da6-8e5d-f6a2b21b889d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " ┌common_warheads_covalent_inhibitors\n", + " ├privileged_scaffolds\n", + " ┌emerging_perfluoroalkyls─├iupac_blue_book\n", + " │ ├schedule_four\n", + " │ └schedule_one\n", + " │ ┌common_warheads_covalent_inhibitors\n", + " │ ├privileged_scaffolds\n", + " ├montmorillonite_adsorption─├iupac_blue_book\n", + " │ ├schedule_four\n", + " │ └schedule_one\n", + " │ ┌common_warheads_covalent_inhibitors\n", + " │ ├privileged_scaffolds\n", + "global_chem─├common_monomer_repeating_units─├iupac_blue_book\n", + " │ ├schedule_four\n", + " │ └schedule_one\n", + " │ ┌common_warheads_covalent_inhibitors\n", + " │ ├privileged_scaffolds\n", + " ├vitamins─├iupac_blue_book\n", + " │ ├schedule_four\n", + " │ └schedule_one\n", + " │ ┌common_warheads_covalent_inhibitors\n", + " │ ├privileged_scaffolds\n", + " └schedule_two─├iupac_blue_book\n", + " ├schedule_four\n", + " └schedule_one\n", + "None\n", + "{'global_chem': {'node_value': , 'children': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'parents': [], 'name': 'global_chem', 'layer': '1'}, 'emerging_perfluoroalkyls': {'node_value': , 'children': ['common_warheads_covalent_inhibitors', 'privileged_scaffolds', 'iupac_blue_book', 'schedule_four', 'schedule_one'], 'parents': ['global_chem'], 'name': 'emerging_perfluoroalkyls', 'layer': 2}, 'montmorillonite_adsorption': {'node_value': , 'children': ['common_warheads_covalent_inhibitors', 'privileged_scaffolds', 'iupac_blue_book', 'schedule_four', 'schedule_one'], 'parents': ['global_chem'], 'name': 'montmorillonite_adsorption', 'layer': 2}, 'common_monomer_repeating_units': {'node_value': , 'children': ['common_warheads_covalent_inhibitors', 'privileged_scaffolds', 'iupac_blue_book', 'schedule_four', 'schedule_one'], 'parents': ['global_chem'], 'name': 'common_monomer_repeating_units', 'layer': 2}, 'vitamins': {'node_value': , 'children': ['common_warheads_covalent_inhibitors', 'privileged_scaffolds', 'iupac_blue_book', 'schedule_four', 'schedule_one'], 'parents': ['global_chem'], 'name': 'vitamins', 'layer': 2}, 'schedule_two': {'node_value': , 'children': ['common_warheads_covalent_inhibitors', 'privileged_scaffolds', 'iupac_blue_book', 'schedule_four', 'schedule_one'], 'parents': ['global_chem'], 'name': 'schedule_two', 'layer': 2}, 'common_warheads_covalent_inhibitors': {'node_value': , 'children': [], 'parents': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'name': 'common_warheads_covalent_inhibitors', 'layer': 3}, 'privileged_scaffolds': {'node_value': , 'children': [], 'parents': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'name': 'privileged_scaffolds', 'layer': 3}, 'iupac_blue_book': {'node_value': , 'children': [], 'parents': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'name': 'iupac_blue_book', 'layer': 3}, 'schedule_four': {'node_value': , 'children': [], 'parents': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'name': 'schedule_four', 'layer': 3}, 'schedule_one': {'node_value': , 'children': [], 'parents': ['emerging_perfluoroalkyls', 'montmorillonite_adsorption', 'common_monomer_repeating_units', 'vitamins', 'schedule_two'], 'name': 'schedule_one', 'layer': 3}}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Amino Acids\n", + "\n", + "amino_acid_test = ['RSTEFGHIKLADPQ']\n", + "smiles = gce.amino_acids_to_smiles(amino_acid_test)\n", + "print (smiles)\n", + "\n", + "amino_acid = gce.smiles_to_amino_acids(smiles)\n", + "print (amino_acid)" + ], + "metadata": { + "id": "o6q50QS4DEq_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d06d1fb9-4545-4728-97e7-800c3a8fec62" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['NC(CCCCNC(N)=N)C(NC(CO)C(NC(C(C)([H])O)C(NC(CCC(O)=O)C(NC(CC1=CC=CC=C1)C(NC([H])C(NC(CC1=CNC=N1)C(NC(C(CC)([H])C)C(NC(CCCCN)C(NC(CC(C)C)C(NC(C)C(NC(CC(O)=O)C(NC(C2CCCN2)C(NC(CCC(N)=O)C(NCC(O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O']\n", + "['RSTEFGHIKLADPQ', None]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Database Monitoring\n", + "\n", + "GlobalChemExtensions.check_status_on_open_source_databases()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UWRGdx8zStAL", + "outputId": "f916b146-4f1d-4dc1-86fc-d1121719d965" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Zinc 15\n", + " Zinc 20\n", + " PubChem\n", + " NIST Chemistry Webhook\n", + " Chem Exper\n", + " NMR Shift Database\n", + " Drug Bank\n", + " Binding Database\n", + " Spectral Database for Organic Compounds\n", + " Sider\n", + " ChemSpider\n", + " Stitch\n", + " CardPred\n", + " Comparative Toxicogenomics Database\n", + " AMED Cardiotoxicity Database\n", + " Tox21\n", + " Drug Safety Analysis System\n", + " OpenFDA\n", + " Metabolites Biological Role\n", + " MetaboAnalyst\n", + " Adverse Drug Reaction Classification System\n", + " Metabolism and Transport Database\n", + " Ecology Toxicity \n", + " Human and Environment Risk Assessment \n", + " International Toxicity Information for Risk Assesments\n", + " Japan Exisiting Database\n", + " National Pesticide Center\n", + " Pesticide Info\n", + " Kyoto Encyclopedia of Genes and Genomes\n", + " Hetereocycles\n", + " Chemical Resolver\n", + " LookChem\n", + " Lipid Maps\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Drug Filtering\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "smiles_list = list(gc.get_node_smiles('emerging_perfluoroalkyls').values())\n", + "\n", + "filtered_smiles = GlobalChemExtensions.filter_smiles_by_criteria(\n", + " smiles_list,\n", + " lipinski_rule_of_5=True,\n", + " ghose=False,\n", + " veber=False,\n", + " rule_of_3=False,\n", + " reos=False,\n", + " drug_like=False,\n", + " pass_all_filters=False\n", + ")\n", + "\n", + "print (filtered_smiles)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bvpiip2dSyeq", + "outputId": "733553c8-f5b3-4017-a8c3-486436c0fc2c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 'FC(F)(F)C1(F)OC1(F)F', 'O=C(O)C(F)(F)C(F)(F)C(F)(F)F', 'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# One Hot Encoding your SMILES\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "smiles_list = list(gc.get_node_smiles('pihkal').values())\n", + "\n", + "encoded_smiles = GlobalChemExtensions.encode_smiles(smiles_list, max_length=200)\n", + "\n", + "print ('Encoded SMILES: %s' % encoded_smiles[0])\n", + "\n", + "decoded_smiles = GlobalChemExtensions.decode_smiles(encoded_smiles)\n", + "\n", + "print ('Decoded SMILES: %s' % decoded_smiles[0])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XoFHQLdov7r7", + "outputId": "13832bd5-d24c-4260-9ed1-0663c8227f38" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Encoded SMILES: [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n", + "Decoded SMILES: CCC(N)CC1=CC(=C(OC)C(=C1)OC)OC \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Convert into Networkx\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "network = gc.network\n", + "networkx_graph = GlobalChemExtensions.convert_to_networkx(network)\n", + "print (networkx_graph.nodes.data())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Pi9wPCO5TEPm", + "outputId": "c3c47907-4679-4abd-8356-0d4d3620461e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[('global_chem', {}), ('', {}), ('organic_synthesis', {}), ('solvents', {}), ('common_organic_solvents', {}), ('protecting_groups', {}), ('amino_acid_protecting_groups', {}), ('interstellar_space', {}), ('environment', {}), ('emerging_perfluoroalkyls', {}), ('miscellaneous', {}), ('amino_acids', {}), ('open_smiles', {}), ('vitamins', {}), ('regex_patterns', {}), ('materials', {}), ('clay', {}), ('montmorillonite_adsorption', {}), ('polymers', {}), ('common_monomer_repeating_units', {}), ('narcotics', {}), ('schedule_two', {}), ('pihkal', {}), ('schedule_one', {}), ('schedule_three', {}), ('schedule_four', {}), ('schedule_five', {}), ('medicinal_chemistry', {}), ('cannabinoids', {}), ('rings', {}), ('rings_in_drugs', {}), ('iupac_blue_book_rings', {}), ('phase_2_hetereocyclic_rings', {}), ('warheads', {}), ('electrophillic_warheads_for_kinases', {}), ('common_warheads_covalent_inhibitors', {}), ('scaffolds', {}), ('privileged_scaffolds', {}), ('common_r_group_replacements', {}), ('iupac_blue_book', {}), ('proteins', {}), ('kinases', {}), ('braf', {}), ('braf_inhibitors', {}), ('privileged_kinase_inhibitors', {})]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Protonation over a range of Ph\n", + "\n", + "gce.find_protonation_states(\n", + " ['CC(=O)O'],\n", + " min_ph=4.0,\n", + " max_ph=8.1,\n", + " pka_precision=1.0,\n", + " max_variants=128,\n", + " label_states=False,\n", + " )" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dWUjiDFnTHpO", + "outputId": "b1669b25-f83e-49e0-fa61-8a175e7e131b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'CC(=O)O': ['CC(=O)O', 'CC(=O)[O-]']}" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# PDF Generation & Parsing\n", + "\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "smiles_list = list(gc.get_node_smiles('pihkal').values())\n", + "\n", + "gce.smiles_to_pdf(\n", + " smiles=smiles_list,\n", + " labels = [],\n", + " file_name = 'molecules.pdf',\n", + " include_failed_smiles = True,\n", + " title = 'MY MOLECULES',\n", + ")\n", + "\n", + "molecules = gce.pdf_to_smiles(\n", + " 'molecules.pdf',\n", + ")\n", + "\n", + "print (molecules)\n" + ], + "metadata": { + "id": "XWGLAGE8TLyH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "33435a75-cc5d-455d-ebd2-706910030c43" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Method: 'generate' Time: 0.98 seconds\n", + "[b'CCC(N)CC1=CC(=C(OC)C(=C1)OC)OC', b'COC1=CC(=CC(=C1OCC=C)OC)CCN', b'COC1=CC(=C(OC)C=C1CC(C)N)SC', b'CCSC1=C(OC)C=C(CC(C)N)C(=C1)OC', b'COC1=CC(=C(OC)C=C1CC(C)N)SC(C)C', b'COC1=CC(=C(OC)C=C1CC(C)N)SC2=CC=CC=C2', b'CCCSC1=C(OC)C=C(CC(C)N)C(=C1)OC', b'CCC(N)CC1=CC(=C(C)C=C1OC)OC', b'CCOC1=CC(=CC(=C1OCC)OC)CCN', b'CCCCOC1=C(OC)C=C(CCN)C=C1OC', b'CNC(C)CC1=CC(=C(C)C=C1OC)OC', b'CSC1=C(C)C=C(SC)C(=C1)CC(C)N', b'COC(CN)C1=CC(=C(Br)C=C1OC)OC', b'COC(CN)C1=CC(=C(C)C=C1OC)OC', b'COC(CN)C1=CC(=C(C)C=C1OC)OC', b'CC1=C(O)C=C(C(O)CN)C(=C1)Br', b'COC(CN)C1=CC(=C(OC)C(=C1)OC)OC', b'COC1=C(Br)C(=CC(=C1)CC(C)N)OC', b'CC(N)CC1=C(Br)C=C2OCOC2=C1', b'[CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[Br:12])[CH2:8][CH2:9][NH2:10]', b'COC1=C(OCC2=CC=CC=C2)C(=CC(=C1)CC(C)N)OC', b'COC1=CC(=C(OC)C=C1Cl)CCN', b'[CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[CH3:12])[CH2:8][CH2:9][NH2:10]', b'CCC1=CC(=C(CCN)C=C1OC)OC', b'CCOC1=C(OC)C=C(CC(C)N)C=C1OC', b'COC1=CC(=C(OC)C=C1F)CCN', b'COC1=CC(=C(OC)C(=C1C)C)CCN', b'COC1=CC(=C(OC)C2=C1CCC2)CCN', b'COC1=CC(=C(OC)C2=C1CCCC2)CCN', b'COC1=CC(=C(OC)C2=C1C3CCC2C3)CCN', b'COC1=CC(=C(OC)C2=C1C=CC=C2)CCN', b'COC1=CC(=C(OC)C=C1)CCN', b'[CH3:20][O:13][C:6]1=[CH:5][C:4](=[C:3]([O:16][CH3:11])[CH:2]=[C:1]1[I:12])[CH2:8][CH2:9][NH2:10]', b'COC1=C(CCN)C=C(OC)C(=C1)[N](=O)=O', b'COC1=C(CCN)C=C(OC)C(=C1)OC(C)C', b'CCCOC1=CC(=C(CCN)C=C1OC)OC', b'COC1=C(CCN)C=C(OC)C(=C1)OCC2CC2', b'COC1=C(CCN)C=C(OC)C(=C1)[Se]C', b'COC1=C(CCN)C=C(OC)C(=C1)SC', b'[CH3:30][CH2:29][S:12][C:1]1=[CH:2][C:3](=[C:4]([CH2:8][CH2:9][NH2:10])[CH:5]=[C:6]1[O:13][CH3:20])[O:16][CH3:11]', b'COC1=C(CCN)C=C(OC)C(=C1)SC(C)C', b'COC1=C(CCN)C(=CC(=C1)SC(C)C)OC', b'[CH3:33][CH2:30][CH2:29][S:12][C:1]1=[CH:2][C:3](=[C:4]([CH2:8][CH2:9][NH2:10])[CH:5]=[C:6]1[O:13][CH3:20])[O:16][CH3:11]', b'COC1=C(CCN)C=C(OC)C(=C1)SCC2CC2', b'COC1=C(CCN)C=C(OC)C(=C1)SC(C)(C)C', b'COCCSC1=CC(=C(CCN)C=C1OC)OC', b'COC1=C(CCN)C=C(OC)C(=C1)SC2CC2', b'CCC(C)SC1=CC(=C(CCN)C=C1OC)OC', b'COC1=C(CCN)C=C(OC)C(=C1)SCCF', b'COC1=CC(=CC(=C1OC)OC)CCN', b'COC1=CC(=CC(=C1OC)OC)CCN', b'COC1=CC(=CC(=C1C)OC)CCN', b'COC1=CC=C(CC(C)N)C(=C1)OC', b'COC1=CC=C(OC)C(=C1)CC(C)N', b'COC1=CC=C(CC(C)N)C=C1OC', b'COC1=CC(=C(OC)C=C1C)C2CC2N', b'COC1=CC=C(C=C1OC)C(O)CN', b'COC1=C2OCOC2=C(OC)C(=C1)CC(C)N', b'COC1=C(CC(C)N)C=C2OCOC2=C1OC', b'COC1=CC=C(CCN)C=C1OC', b'CCCCCC1=CC(=C(CC(C)N)C=C1OC)OC', b'COC1=CC(=C(OC)C=C1Br)CC(C)N', b'CCCCC1=CC(=C(CC(C)N)C=C1OC)OC', b'COC1=CC(=C(OC)C=C1Cl)CC(C)N', b'COC1=CC(=C(OC)C=C1CCF)CC(C)N', b'CCC1=CC(=C(CC(C)N)C=C1OC)OC', b'COC1=CC(=C(OC)C=C1I)CC(C)N', b'COC1=CC(=C(OC)C=C1C)CC(C)N', b'COC1=C(CC(C)N)C(=CC(=C1)C)OC', b'COC1=C(CC(C)N)C=C(OC)C(=C1)[N](=O)=O', b'CCCC1=CC(=C(CC(C)N)C=C1OC)OC', b'CCOC1=C(OC)C=C(CCN)C=C1OC', b'CCOC1=C(CC(C)N)C=C(OCC)C(=C1)OCC', b'CCOC1=C(CCN)C=C(OC)C(=C1)OCC', b'CCOC1=C(CCN)C=C(OCC)C(=C1)OC', b'CCOC1=C(CCN)C=C(OC)C(=C1)OC', b'CCC(N)CC1=CC=C2OCOC2=C1', b'CCCC(CC1=CC=C2OCOC2=C1)NCC', b'COC1=C(CC(C)N)C=C2OC(C)CC2=C1', b'COC1=C(CC(C)N)C=C2OC(C)(C)CC2=C1', b'CC(CC1=CC=C2OCOC2=C1)N(C)O', b'COC1=C2CCCC2=C(OC)C(=C1)CC(C)N', b'COC1=C2CCCCC2=C(OC)C(=C1)CC(C)N', b'COC1=C2C3CCC(C3)C2=C(OC)C(=C1)CC(C)N', b'COC1=CC(=C(OC)C(=C1C)C)CC(C)N', b'COC1=CC(=C(OC)C2=C1C=CC=C2)CC(C)N', b'', b'CCCSC1=CC(=C(CCNO)C=C1OC)OC', b'CCC(C)SC1=CC(=C(CCNO)C=C1OC)OC', b'COC1=CC(=C(OC)C=C1I)CC(C)N(C)C', b'COC1=C(OC)C(=C(CCN)C=C1)OC', b'COC1=CC(=CC(=C1OC(C)C)OC)CCN', b'CCOC1=CC(=C(OC)C=C1C)CC(C)N', b'CCC(N)CC1=CC=C2OCOC2=C1', b'COC1=C2OCOC2=CC(=C1)CCN', b'C[O:7][C:2]1=[CH:1][C:6](=[CH:5][C:4](=[C:3]1[O:9]C)[O:20]C)[CH2:10][CH2:11][NH2:12]', b'CC(CC1=CC=C(C=C1)OC)N', b'CNC(C)CC1=C(C)C=C2OCOC2=C1', b'COC1=CC(=CC(=C1OCC(C)=C)OC)CCN', b'CC(N)CC1=CC=C2OCOC2=C1', b'CC(CC1=CC=C2OCOC2=C1)NCC=C', b'CCCCNC(C)CC1=CC=C2OCOC2=C1', b'CC(CC1=CC=C2OCOC2=C1)NCC3=CC=CC=C3', b'CC(CC1=CC=C2OCOC2=C1)NCC3CC3', b'CC(CC1=CC=C2OCOC2=C1)N(C)C', b'CCNC(C)CC1=CC=C2OCOC2=C1', b'CC(CC1=CC=C2OCOC2=C1)NCCO', b'CC(C)NC(C)CC1=CC=C2OCOC2=C1', b'CNC(C)CC1=CC=C2OCOC2=C1', b'CNC(C)CC1=CC=C2OCCOC2=C1', b'CONC(C)CC1=CC=C2OCOC2=C1', b'COCCNC(C)CC1=CC=C2OCOC2=C1', b'CNC(C)(C)CC1=CC=C2OCOC2=C1', b'CC(CC1=CC=C2OCOC2=C1)NO', b'NCCC1=CC=C2OCOC2=C1', b'CC(C)(N)CC1=CC=C2OCOC2=C1', b'CC(CC1=CC=C2OCOC2=C1)NCC#C', b'CCCNC(C)CC1=CC=C2OCOC2=C1', b'CCOC1=CC(=CC(=C1OC)OC)CCN', b'COC1=C2OCCOC2=CC(=C1)CCN', b'CCOC1=CC(=C(OC)C=C1OCC)CC(C)N', b'CCOC1=CC(=C(CC(C)N)C=C1OC)OC', b'CCOC1=CC=C(CCN)C=C1OC', b'COC1=CC(=C(CC(C)N)C=C1Br)OC', b'COC1=C(CC(C)N)C=C(SC)C(=C1)OC', b'CNC(C)CC1=C(OC)C=CC(=C1)OC', b'CNC(C)CC1=C(OC)C=C(Br)C(=C1)OC', b'CCC(CC1=CC=C2OCOC2=C1)NC', b'CCCC(CC1=CC=C2OCOC2=C1)NC', b'CNC(C)CC1=CC=C(OC)C=C1', b'CNC(C)CC1=C(OC)C=C2OCOC2=C1', b'COC1=C2OCOC2=CC(=C1)CC(C)N', b'COC1=C(CC(C)N)C=C2OCOC2=C1', b'COC1=C2OCOC2=CC=C1CC(C)N', b'COC1=CC=C(CC(C)N)C2=C1OCO2', b'CCOC1=CC(=C(OC)C=C1OC)CC(C)N', b'CCCOC1=CC(=CC(=C1OC)OC)CCN', b'COc1cc(OC)c(cc1OCCC)CC(C)N', b'COC1=CC(=C(SC)C=C1OC)CC(C)N', b'CCCOC1=C(OC)C=C(CCN)C=C1OC', b'COC1=CC(=CC(=C1OCCCC2=CC=CC=C2)OC)CCN', b'NCCC1=CC=CC=C1', b'COC1=CC(=CC(=C1OCC=C)OC)CCN', b'CCOC1=CC(=CC(=C1OC)OCC)CCN', b'COC1=C(OC)C(=C(OC)C(=C1)CC(C)N)OC', b'[CH3:16][CH2:15][O:14][C:8]1=[C:1]([S:10][CH2:11][CH3:17])[CH:2]=[C:3]([CH2:5][CH2:6][NH2:7])[CH:4]=[C:9]1[O:12][CH3:13]', b'CCOC1=CC(=CC(=C1SCC)OC)CCN', b'CCOC1=CC(=CC(=C1OCC)SC)CCN', b'CCCCSC1=C(OC)C=C(CCN)C=C1OC', b'CCOC1=C(OC)C=C(CCN)C=C1SC', b'[CH3:16][CH2:11][S:10][C:8]1=[C:1]([O:12][CH3:13])[CH:2]=[C:3]([CH2:5][CH2:6][NH2:7])[CH:4]=[C:9]1[O:14][CH3:15]', b'COC1=C(OC)C(=C(CCN)C=C1)SC', b'COC1=C(SC)C(=C(CCN)C=C1)OC', b'COC1=C(CCN)C=CC(=C1OC)SC', b'COC1=CC(=CC(=C1OC)SC)CCN', b'COC1=CC(=CC(=C1SC)OC)CCN', b'COC1=CC(=CC(=C1OC)OC)CC(C)N', b'COC1=C(CC(C)N)C=C(OC)C(=C1)OC', b'COC1=C(OC)C(=C(CC(C)N)C=C1)OC', b'COC1=CC(=C(OC)C(=C1)OC)CC(C)N', b'COC1=CC=C(OC)C(=C1CC(C)N)OC', b'COC1=CC(=C(CC(C)N)C(=C1)OC)OC', b'CCSC1=CC(=CC(=C1OC)OC)CCN', b'CCOC1=CC(=CC(=C1SC)OC)CCN', b'CCOC1=CC(=CC(=C1OC)SC)CCN', b'CSC1=C2OCOC2=CC=C1CC(C)N', b'COC1=C(CC(C)N)C=C2OCSC2=C1', b'COC1=C(CCN)C=C(OC)C(=C1)OC', b'CCC1=CC(=C(CC(C)N)C=C1OC)SC', b'CCC1=CC(=C(CC(C)N)C=C1SC)OC', b'COC1=CC(=C(SC)C=C1C)CC(C)N', b'COC1=C(CC(C)N)C=C(SC)C(=C1)C', b'COC1=C(CC(C)N)C=C(C(=C1)C)[S](C)=O', b'CCCSC1=C(OC)C=C(CCN)C=C1OC', b'CCOC1=CC(=CC(=C1OCC)OCC)CCN', b'CCOC1=C(OC)C(=CC(=C1)CCN)SCC', b'CCOC1=CC(=CC(=C1SC)OCC)CCN', b'CCOC1=C(OCC)C(=CC(=C1)CCN)SCC', b'CCOC1=CC(=CC(=C1SCC)OCC)CCN']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Visualize SMARTS \n", + "\n", + "gc = GlobalChem()\n", + "gc.build_global_chem_network(print_output=False, debugger=False)\n", + "smarts_list = list(gc.get_node_smarts('pihkal').values())\n", + "\n", + "molecule = gce.visualize_smarts(smarts_list[0])\n", + "molecule" + ], + "metadata": { + "id": "0baIE9U_YpML", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "1d3e6e9a-4d1c-410d-ba8b-577616b50e71" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "image/png": 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+ }, + "metadata": {}, + "execution_count": 7 + } + ] + } + ] +} \ No newline at end of file