diff --git a/.github/workflows/run-notebooks.yml b/.github/workflows/run-notebooks.yml new file mode 100644 index 0000000..0cf7a17 --- /dev/null +++ b/.github/workflows/run-notebooks.yml @@ -0,0 +1,91 @@ +name: Run Jupyter Notebooks + +on: + push: + branches: + - "**" + pull_request: + types: [opened, synchronize, reopened] + +jobs: + run-notebooks: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.13" + + - name: Install dependencies + run: | + pip install --upgrade pip + pip install chemaxon jupyter nbconvert ipykernel + python -m ipykernel install --user --name python3 + + - name: Run 01_basic_functions.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/01_basic_functions_executed.ipynb \ + jupyter/01_basic_functions.ipynb + + - name: Run 02_calculators.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/02_calculators_executed.ipynb \ + jupyter/02_calculators.ipynb + + - name: Run 03_molecular_similarity.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/03_molecular_similarity_executed.ipynb \ + jupyter/03_molecular_similarity.ipynb + + - name: Run 04_standardizer.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/04_standardizer_executed.ipynb \ + jupyter/04_standardizer.ipynb + + - name: Run 05_structure_checker.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/05_structure_checker_executed.ipynb \ + jupyter/05_structure_checker.ipynb + + - name: Run 06_molecule_search.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/06_molecule_search_executed.ipynb \ + jupyter/06_molecule_search.ipynb + + - name: Run 07_pandas_integration_examples.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/07_pandas_integration_examples_executed.ipynb \ + jupyter/07_pandas_integration_examples.ipynb + + - name: Run 08_fingerprint_calculations.ipynb + run: | + jupyter nbconvert --to notebook --execute \ + --ExecutePreprocessor.timeout=600 \ + --output /tmp/08_fingerprint_calculations_executed.ipynb \ + jupyter/08_fingerprint_calculations.ipynb + + - name: Upload executed notebooks as artifacts + if: always() + uses: actions/upload-artifact@v4 + with: + name: executed-notebooks + path: /tmp/*_executed.ipynb + retention-days: 7 \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..e3c9ae0 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,209 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, +special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by +reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS \ No newline at end of file diff --git a/README.md b/README.md index 187541f..258eb88 100644 --- a/README.md +++ b/README.md @@ -10,5 +10,5 @@ This repository contains usage examples for [Chemaxon Python API](https://docs.c ## Note -If you have any question, suggestion please feel free to contact us at -[`chemlib@chemaxon.com`](mailto:chemlib@chemaxon.com) +If you have any question, suggestion please feel free to contact us via +[FreshDesk support](https://chemaxon.freshdesk.com/a/tickets/new) diff --git a/jupyter/01_basic_functions.ipynb b/jupyter/01_basic_functions.ipynb index 3de9c46..c9660dc 100644 --- a/jupyter/01_basic_functions.ipynb +++ b/jupyter/01_basic_functions.ipynb @@ -12,27 +12,15 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "0fade33e-d909-44f6-a37b-ac210794ff72", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting chemaxon\n", - " Using cached chemaxon-25.1.0b5-cp313-cp313-macosx_14_0_arm64.whl.metadata (1.9 kB)\n", - "Using cached chemaxon-25.1.0b5-cp313-cp313-macosx_14_0_arm64.whl (57.6 MB)\n", - "Installing collected packages: chemaxon\n", - "Successfully installed chemaxon-25.1.0b5\n" - ] - } - ], "source": [ - "import sys\n", + " import sys\n", "\n", - "!{sys.executable} -m pip install chemaxon" - ] + " !{sys.executable} -m pip install chemaxon" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -44,27 +32,17 @@ }, { "cell_type": "code", - "execution_count": 2, "id": "76ca42b3-30a1-4d4f-971c-e37d3af1c1b7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Version: 25.1.0b5\n", - "CCL version: 25.1.3\n", - "CCL build date: 2025-04-24\n" - ] - } - ], "source": [ "import chemaxon\n", "\n", "print('Version: ', chemaxon.__version__)\n", "print('CCL version: ', chemaxon.ccl_version())\n", "print('CCL build date:', chemaxon.ccl_build_date())" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -76,27 +54,13 @@ }, { "cell_type": "code", - "execution_count": 3, "id": "8d9f4205-d0a9-467c-97e9-054d78bdde60", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'product': 'Python API',\n", - " 'expiration': '2025-06-30',\n", - " 'licensee': 'Chemaxon',\n", - " 'state': 'VALID'}]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "[lic for lic in chemaxon.licenses() if lic['product'] == 'Python API']" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -659,12 +623,219 @@ ] }, { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Molecule property handling**", + "id": "6588cf0d348eed22" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "You can store custom properties in the __Molecule__ objects using the __set_property__ and __get_property__ methods. A dictionary of the __Molecule__ object's properties can also be reteived using the __get_properties_dict__ method.\n", + "\n", + "_**Note:** Modifying that dictionary will not change the properties of the __Molecule__ object. For that, you should use __set_property__ method._" + ], + "id": "d6d63c6344fc92ac" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Supported property value types are: __str__, __int__, __float__, __bool__, __list__ (of int, float). Considering the list of scalar values, the api is aligned to the behavior of Chemaxon Java API. So, double values can only be used with dots, not with commas.", + "id": "c5c0afd0a3dd4cdd" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "#### Reading properties:", + "id": "4df4fca46fb9372d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-03T08:09:55.633222159Z", + "start_time": "2026-02-03T08:09:55.544785396Z" + } + }, "cell_type": "code", - "execution_count": null, - "id": "02aab71e-3d23-4713-98d9-ba75d47511af", + "source": [ + "from chemaxon import open_for_import\n", + "\n", + "with open_for_import('mol_with_properties.mrv') as mol_iterator:\n", + " mols = list(mol_iterator)\n", + "\n", + "mols[0]" + ], + "id": "36b4c96ad9ab34b8", + "outputs": [ + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\nTRUEasd424.2asd1 2 3 4 542TRUE1.5 2.7 3.3 4.567\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)), Bond(type=2, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=2, atoms=(0, 5)), Bond(type=2, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(9, 10)), Bond(type=1, atoms=(10, 11)), Bond(type=1, atoms=(6, 7)), Bond(type=2, atoms=(6, 11)), Bond(type=1, atoms=(4, 6)), Bond(type=2, atoms=(13, 14)), Bond(type=1, atoms=(14, 15)), Bond(type=2, atoms=(15, 16)), Bond(type=1, atoms=(16, 17)), Bond(type=1, atoms=(12, 13)), Bond(type=2, atoms=(12, 17)), Bond(type=1, atoms=(9, 12))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(0, 5)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(2, 3)), Bond(type=2, atoms=(3, 4)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(4, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=2, atoms=(0, 5)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=2, atoms=(6, 11)), Bond(type=1, atoms=(4, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(7, 8)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(9, 10)), Bond(type=1, atoms=(9, 12)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(9, 10)), Bond(type=1, atoms=(10, 11)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(10, 11)), Bond(type=2, atoms=(6, 11)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(12, 13)), Bond(type=2, atoms=(12, 17)), Bond(type=1, atoms=(9, 12)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(13, 14)), Bond(type=1, atoms=(12, 13)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(13, 14)), Bond(type=1, atoms=(14, 15)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(14, 15)), Bond(type=2, atoms=(15, 16)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(15, 16)), Bond(type=1, atoms=(16, 17)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(16, 17)), Bond(type=2, atoms=(12, 17))))), native_handle=34, _to_str=, properties=[, , , , , ])" + ], + "image/svg+xml": "asd1 2 3 4 542TRUE1.5 2.7 3.3 4.567\n" + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 31 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-03T09:24:48.581391621Z", + "start_time": "2026-02-03T09:24:48.531897571Z" + } + }, + "cell_type": "code", + "source": [ + "from chemaxon import open_for_import\n", + "\n", + "with open_for_import('mol_with_properties.mrv') as mol_iterator:\n", + " mols = list(mol_iterator)\n", + "\n", + "mol = mols[0]\n", + "\n", + "print(\"Number of properties: \" + str(len(mol.properties)))\n", + "print(\"\\ttest_str_property -> \" + mol.get_property(\"test_str_property\"))\n", + "print(\"\\ttest_double_property -> \" + str(mol.get_property(\"test_double_property\")))\n", + "print(\"\\ttest_int_property -> \" + str(mol.get_property(\"test_int_property\")))\n", + "print(\"\\ttest_boolean_property -> \" + str(mol.get_property(\"test_boolean_property\")))\n", + "print(\"\\ttest_int_array_property -> \" + str(mol.get_property(\"test_int_array_property\")))\n", + "print(\"\\ttest_double_array_property -> \" + str(mol.get_property(\"test_double_array_property\")))\n", + "\n", + "print()\n", + "print(\"All properties as dict: \\n\" + str(mol.get_property_dict()))" + ], + "id": "3174830bcf4c2edd", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of properties: 6\n", + "\ttest_str_property -> asd\n", + "\ttest_double_property -> 4.2\n", + "\ttest_int_property -> 42\n", + "\ttest_boolean_property -> True\n", + "\ttest_int_array_property -> [1, 2, 3, 4, 5]\n", + "\ttest_double_array_property -> [1.2, 3.4, 5.22, 6.7889]\n", + "\n", + "All properties as dict: \n", + "{'test_boolean_property': True, 'test_str_property': 'asd', 'test_int_property': 42, 'test_double_property': 4.2, 'test_int_array_property': [1, 2, 3, 4, 5], 'test_double_array_property': [1.2, 3.4, 5.22, 6.7889]}\n" + ] + } + ], + "execution_count": 36 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "#### Adding properties to the molecule object:\n", + "\n", + "Supported types are: __bool__, __int__, __float__, __str__, __list__ of int, __list__ of float__. Note that the list of scalar values should be provided as a list of the corresponding type, not as a string. So, double values can only be used with dots, not with commas." + ], + "id": "ddc34a664bc27eed" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-06T14:26:44.259402624Z", + "start_time": "2026-02-06T14:26:44.197003574Z" + } + }, + "cell_type": "code", + "source": [ + "from chemaxon import import_mol\n", + "\n", + "mol = import_mol('CC(=O)NC1=CC=C(O)C=C1')\n", + "\n", + "mol.set_property('test_str_property', 'test string')\n", + "mol.set_property('test_double_property', 3.14)\n", + "mol.set_property('test_int_property', 42)\n", + "mol.set_property('test_boolean_property', True)\n", + "mol.set_property('test_int_array_property', [1, 2, 3])\n", + "mol.set_property('test_double_array_property', [1.1, 2.2, 3.3])\n", + "\n", + "print(\"Number of properties: \" + str(len(mol.properties)))\n", + "print(\"\\ttest_str_property -> \" + mol.get_property(\"test_str_property\"))\n", + "\n", + "\n", + "# Properties are persisted on export of course:\n", + "from chemaxon import export_mol\n", + "\n", + "print(export_mol(mol, 'sdf'))" + ], + "id": "cfec6f58a612938e", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of properties: 6\n", + "\ttest_str_property -> test string\n", + "\n", + " Mrv2503 02062615262D \n", + "\n", + " 11 11 0 0 0 0 999 V2000\n", + " 0.0000 3.3000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.0000 2.4750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " -0.7145 2.0625 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.7145 2.0625 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.7145 1.2375 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1.4289 0.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1.4289 -0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.7145 -0.4125 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.7145 -1.2375 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.0000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0.0000 0.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 1 2 1 0 0 0 0\n", + " 2 3 2 0 0 0 0\n", + " 2 4 1 0 0 0 0\n", + " 4 5 1 0 0 0 0\n", + " 5 6 2 0 0 0 0\n", + " 6 7 1 0 0 0 0\n", + " 7 8 2 0 0 0 0\n", + " 8 9 1 0 0 0 0\n", + " 8 10 1 0 0 0 0\n", + " 10 11 2 0 0 0 0\n", + " 5 11 1 0 0 0 0\n", + "M END\n", + "> \n", + "test string\n", + "\n", + "> \n", + "3.14\n", + "\n", + "> \n", + "42\n", + "\n", + "> \n", + "True\n", + "\n", + "> \n", + "[1, 2, 3]\n", + "\n", + "> \n", + "[1.1, 2.2, 3.3]\n", + "\n", + "$$$$\n", + "\n" + ] + } + ], + "execution_count": 2 + }, + { "metadata": {}, + "cell_type": "code", "outputs": [], - "source": [] + "execution_count": null, + "source": "", + "id": "1f169e93d6c6c99d" } ], "metadata": { diff --git a/jupyter/02_calculators.ipynb b/jupyter/02_calculators.ipynb index d430751..4f53094 100644 --- a/jupyter/02_calculators.ipynb +++ b/jupyter/02_calculators.ipynb @@ -12,177 +12,27 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "4651ecec-55a8-48d2-a76e-a5c8487ef569", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "logP: 0.91\n", - "logD[pH 9.0]: 0.78\n", - "pKa: [atom: 8, pka: 9.46 (ACIDIC)], [atom: 2, pka: -4.36 (BASIC)], [atom: 8, pka: -5.94 (BASIC)]\n", - "hlb: 14.14\n" - ] - } - ], "source": [ "from chemaxon import import_mol, logp, logd, pka, hlb\n", + "from numpy.random.mtrand import normal\n", + "from rdkit.Chem.rdchem import Conformer\n", "\n", "mol = import_mol('CC(=O)NC1=CC=C(O)C=C1') # paracetamol\n", "\n", "print('logP: ', logp(mol))\n", - "print('logD[pH 9.0]:', logd(mol, pH=9.0))\n", + "print('logD[pH 9.0]:', logd(mol, ph=9.0))\n", "print('pKa: ', pka(mol))\n", "print('hlb: ', hlb(mol))" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 2, "id": "a23b02ed-fe3b-419e-95e2-a2fee78abb3f", "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "CH3O-7.14 OOH3.41 O\n" - ], - "text/plain": [ - "Molecule(_mrv='\\nTCC\\n', _mrv_to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 3)), Bond(type=1, atoms=(3, 4)), Bond(type=2, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)), Bond(type=2, atoms=(8, 9)), Bond(type=1, atoms=(4, 9)), Bond(type=1, atoms=(9, 10)), Bond(type=1, atoms=(10, 11)), Bond(type=2, atoms=(10, 12))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 3)))), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=1, atoms=(1, 3)), Bond(type=1, atoms=(3, 4)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=2, atoms=(4, 5)), Bond(type=1, atoms=(4, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(7, 8)), Bond(type=2, atoms=(8, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=2, atoms=(8, 9)), Bond(type=1, atoms=(4, 9)), Bond(type=1, atoms=(9, 10)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(9, 10)), Bond(type=1, atoms=(10, 11)), Bond(type=2, atoms=(10, 12)))), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=1, atoms=(10, 11)),)), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=2, atoms=(10, 12)),))))" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "from chemaxon import import_mol, pka\n", "\n", @@ -190,7 +40,38 @@ "\n", "pka_result = pka(mol)\n", "pka_result.structure" - ] + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "657e9aeb-8a20-47cd-8443-3df87c474e07", + "metadata": {}, + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install matplotlib" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "51086e75-7995-4010-96ef-b96444770342", + "metadata": {}, + "source": [ + "from chemaxon import logd_ph_range, PhRange\n", + "import matplotlib.pyplot as pyplt\n", + "\n", + "res = logd_ph_range(import_mol('aspirin'), PhRange(0, 14, 0.5))\n", + "pyplt.plot([r.pH for r in res],[r.logd for r in res])\n", + "pyplt.title(\"logD by pH\")\n", + "pyplt.xlabel(\"pH\")\n", + "pyplt.ylabel(\"logD\")" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -204,23 +85,8 @@ }, { "cell_type": "code", - "execution_count": 3, "id": "bc97b4ee-ecc0-41da-b0c1-de15e3dde46b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Formula: C9H8O4\n", - "Atom count: 21\n", - "Ring count: 1\n", - "Fsp3: 0.1111111111111111\n", - "logP: 1.238089698333333\n", - "logS: -1.8489350401969602\n" - ] - } - ], "source": [ "from chemaxon import evaluate\n", "\n", @@ -230,7 +96,9 @@ "print('Fsp3: ', evaluate(mol, \"fsp3()\"))\n", "print('logP: ', evaluate(mol, \"logP()\"))\n", "print('logS: ', evaluate(mol, \"logS()\"))" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -242,167 +110,14 @@ }, { "cell_type": "code", - "execution_count": 4, "id": "edca4a1e-dda0-41e4-9a3c-1062765dcb24", "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "OOOO\n" - ], - "text/plain": [ - "Molecule(_mrv='\\n\\n', _mrv_to_svg=, bonds=(Bond(type=0, atoms=(0, 1)), Bond(type=0, atoms=(1, 2)), Bond(type=0, atoms=(1, 3)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=0, atoms=(5, 6)), Bond(type=0, atoms=(6, 7)), Bond(type=1, atoms=(6, 8)), Bond(type=1, atoms=(8, 9)), Bond(type=4, atoms=(9, 10)), Bond(type=4, atoms=(10, 11)), Bond(type=4, atoms=(11, 12)), Bond(type=4, atoms=(12, 13)), Bond(type=4, atoms=(13, 14)), Bond(type=4, atoms=(9, 14)), Bond(type=1, atoms=(14, 15)), Bond(type=0, atoms=(5, 15)), Bond(type=0, atoms=(15, 16)), Bond(type=1, atoms=(4, 17)), Bond(type=4, atoms=(17, 18)), Bond(type=4, atoms=(18, 19)), Bond(type=4, atoms=(19, 20)), Bond(type=4, atoms=(20, 21)), Bond(type=4, atoms=(21, 22)), Bond(type=4, atoms=(17, 22))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=0, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=0, atoms=(0, 1)), Bond(type=0, atoms=(1, 2)), Bond(type=0, atoms=(1, 3)))), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=0, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=0, atoms=(1, 3)), Bond(type=1, atoms=(3, 4)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(4, 17)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=0, atoms=(5, 6)), Bond(type=0, atoms=(5, 15)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=0, atoms=(5, 6)), Bond(type=0, atoms=(6, 7)), Bond(type=1, atoms=(6, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=0, atoms=(6, 7)),)), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=1, atoms=(6, 8)), Bond(type=1, atoms=(8, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(8, 9)), Bond(type=4, atoms=(9, 10)), Bond(type=4, atoms=(9, 14)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(9, 10)), Bond(type=4, atoms=(10, 11)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(10, 11)), Bond(type=4, atoms=(11, 12)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(11, 12)), Bond(type=4, atoms=(12, 13)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(12, 13)), Bond(type=4, atoms=(13, 14)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(13, 14)), Bond(type=4, atoms=(9, 14)), Bond(type=1, atoms=(14, 15)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(14, 15)), Bond(type=0, atoms=(5, 15)), Bond(type=0, atoms=(15, 16)))), Atom(symbol='O', atom_number=8, mass_number=0, bonds=(Bond(type=0, atoms=(15, 16)),)), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=1, atoms=(4, 17)), Bond(type=4, atoms=(17, 18)), Bond(type=4, atoms=(17, 22)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(17, 18)), Bond(type=4, atoms=(18, 19)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(18, 19)), Bond(type=4, atoms=(19, 20)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(19, 20)), Bond(type=4, atoms=(20, 21)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(20, 21)), Bond(type=4, atoms=(21, 22)))), Atom(symbol='C', atom_number=6, mass_number=0, bonds=(Bond(type=4, atoms=(21, 22)), Bond(type=4, atoms=(17, 22))))))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "warfarin = import_mol(\"warfarin\")\n", "import_mol(evaluate(warfarin, \"molFormat(genericTautomer(), 'smarts')\"))" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -414,372 +129,340 @@ }, { "cell_type": "code", - "execution_count": 5, "id": "abf54870-2e45-4bb6-a317-95cb3006ce92", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting pandas\n", - " Using cached pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (89 kB)\n", - "Collecting matplotlib\n", - " Using cached matplotlib-3.10.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (11 kB)\n", - "Requirement already satisfied: numpy>=1.26.0 in /opt/homebrew/Cellar/jupyterlab/4.4.1/libexec/lib/python3.13/site-packages (from pandas) (2.2.5)\n", - "...\n", - "Using cached pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl (11.3 MB)\n", - "Using cached matplotlib-3.10.3-cp313-cp313-macosx_11_0_arm64.whl (8.1 MB)\n", - "Installing collected packages: pandas, matplotlib\n", - "Successfully installed matplotlib-3.10.3 pandas-2.2.3\n" - ] - } - ], "source": [ "import sys\n", "\n", "!{sys.executable} -m pip install pandas matplotlib" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 6, "id": "a72c8cd9-154c-450e-80a4-58ad8a44d92c", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1000 rows × 6 columns

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" - ], - "text/plain": [ - " SMILES NCI_ID LOGP LOGD[3.0] \\\n", - "0 CC1=CC(=O)C=CC1=O 1 1.42 1.42 \n", - "1 S(SC1=NC2=CC=CC=C2S1)C3=NC4=C(S3)C=CC=C4 2 6.22 6.22 \n", - "2 OC1=C(Cl)C=C(C=C1[N+]([O-])=O)[N+]([O-])=O 3 2.15 1.96 \n", - "3 [O-][N+](=O)C1=CNC(=N)S1 4 -0.22 -1.69 \n", - "4 NC1=CC2=C(C=C1)C(=O)C3=C(C=CC=C3)C2=O 5 2.09 2.04 \n", - ".. ... ... ... ... \n", - "995 OC(=O)CN(CC(O)=O)CC1=CC=CC=C1 1003 -2.08 -2.08 \n", - "996 CC(C)N(CC(O)=O)CC(O)=O 1004 -3.19 -3.96 \n", - "997 C1=CC=C(C=C1)C=NC(N=CC2=CC=CC=C2)C3=CC=CC=C3 1005 5.72 5.72 \n", - "998 CC(C)(C)C[C]1(C)NC(=O)NC1=O 1006 1.11 1.10 \n", - "999 CCOC(=O)C(C(C)C)C(=O)OCC 1007 1.95 1.95 \n", - "\n", - " LOGD[7.4] LOGD[11.0] \n", - "0 1.42 1.42 \n", - "1 6.22 6.22 \n", - "2 0.25 0.25 \n", - "3 -0.27 -0.33 \n", - "4 2.09 2.09 \n", - ".. ... ... \n", - "995 -5.27 -6.17 \n", - "996 -6.34 -6.35 \n", - "997 5.72 5.72 \n", - "998 1.10 -0.05 \n", - "999 1.95 1.95 \n", - "\n", - "[1000 rows x 6 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mols['LOGP'] = mols.apply(lambda row: round(logp(import_mol(row['SMILES'])), 2), axis = 'columns')\n", - "mols['LOGD[3.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), pH=3.0), axis = 'columns')\n", - "mols['LOGD[7.4]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), pH=7.4), axis = 'columns')\n", - "mols['LOGD[11.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), pH=11.0), axis = 'columns')\n", + "mols['LOGD[3.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=3.0), axis = 'columns')\n", + "mols['LOGD[7.4]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=7.4), axis = 'columns')\n", + "mols['LOGD[11.0]'] = mols.apply(lambda row: logd(import_mol(row['SMILES']), ph=11.0), axis = 'columns')\n", "mols" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 8, "id": "1eb642b1-4087-4f7e-90f6-6d0cc6891abe", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[,\n", - " ],\n", - " [,\n", - " ]], dtype=object)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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AACehBAUAADgOAQoA12jbtq3ExcWVmcaPH2/W9+nTp8y6sWPHxvqwAVQDVTwAXGP37t1y+fLlwOu8vDzp37+/3HvvvYFlo0ePllmzZgVeJycn1/hxArh6BCgAXKNp06Yhr2fPni3XXXed3HHHHSEBSVpaWgyODkAkEaAAcKWLFy/K0qVLZcqUKaYqx7Zs2TKzXIOUzMxMyc7OrrQUpaioyEy2M2fOmHlxcbGZIs3eZzT27RR22pLirajuP5b8dB2LI5jGcPZFgALAldasWSMFBQUyatSowLIRI0ZImzZtpGXLlrJ//3557LHHJD8/X1atWlXhfnJzcyUnJ6fM8o0bN0a1emjTpk3idT/rVhKV/a5fv16cwg/XcVME01hYWFjlbQlQALjSCy+8IAMHDjTBiG3MmDGBvzt27CgtWrSQvn37yuHDh01VUHmysrJMKUxwCUqrVq0kIyNDUlJSIn7cegepGb62nUlMTBQvstOYvSdeikq+LN2KlLyZAyTW/HQd+0cwjXYJZVUQoABwnY8//lg2b95cacmISk9PN/NDhw5VGKAkJSWZqTTNkKP5H0+09+8EGpwUXY58gOKk8+aH65gYwTSGsx8CFBd1pgbg/yxevFiaNWsmgwdX3sHgvn37zFxLUgC4CwEKAFcpKSkxAcrIkSMlIeHLLEyrcZYvXy6DBg2Sxo0bmzYokydPlt69e0unTp1ieswAwkeAAsBVtGrnyJEj8sADD4Qsr127tlk3f/58OX/+vGlHMnz4cJk+fXrMjhVA9RGgAHAVbbxqWWUfX9WAZNu2bTE5JgCRR1f3AADAcQhQAACA4xCgAAAAxyFAAQAAjkOAAgAAHIcABQAAOA4BCgAAcBwCFAAA4Dh01AYAcPU4ZR/NrnxMJrgTJSgAAMBxCFAAAIDjUMUDAIhotUtSLUvmdI/p4cADKEEBAACOQwkKDBqdAQCchBIUAADgOAQoAADAcajicVg1i924rMPMN0QkLtaHBABATFCCAgAAHIcABQAAOA4BCgAAcBwCFAAA4DgEKABcY+bMmRIXFxcy3XTTTYH1Fy5ckPHjx0vjxo2lfv36Mnz4cDl58mRMjxlA9RCgAHCVW265RY4fPx6Y3nrrrcC6yZMny+uvvy4rV66Ubdu2ybFjx2TYsGExPV4ADglQuMMBEE0JCQmSlpYWmJo0aWKWnz59Wl544QWZN2+efOtb35KuXbvK4sWL5e2335adO3fG+rABOKEfFL3D2bx585cfkpAQcoezbt06c4eTmpoqEyZMMHc4f/7zn6NxKAA85uDBg9KyZUupU6eO9OjRQ3Jzc6V169ayd+9eKS4uln79+gW21ZsjXbdjxw657bbbyt1fUVGRmWxnzpwxc92XTpFm7zMa+44l7cMp8He8FTKPtlicS69ex2inMZx9JUTzDqc0+w5n+fLl5g5H6R1O+/btzR1ORRkIAKj09HRZsmSJ3HjjjaZ6JycnR26//XbJy8uTEydOSO3ataVhw4Yh72nevLlZVxENcHQ/pW3cuFGSk5MlWjZt2iReUt7oxT/rVlIjn71+/XqJFa9dx2insbCwMLYBitvvcGJ5B1LTdx4VcdJ59cOdipPOiZPP88CBAwN/d+rUyQQsbdq0kVdffVXq1q1brX1mZWXJlClTQvKXVq1aSUZGhqSkpEg0zq9m+P3795fExETxiv/r/VoC+ZcGJ9l74qWoJPo9YufNHCA1zavXMdpptP//jkmA4qU7nFjegdTUnYcT70j8fKfihHMSzh1OrGlecsMNN8ihQ4dMJnrx4kUpKCgIyWO0jVt5Jbq2pKQkM5WmGXI0/+OJ9v5rWtHlsoGIBiflLY+0WJ5Hr13HaKcxnP1EPEDxwh1OLO9AavrOw0l3JH6+U3HSOQnnDifWzp07J4cPH5Yf/ehHplGsnostW7aYxvcqPz9fjhw5YkpyAbhL1AcLdPMdTk0LvtOoqTuPijjxvHrtejv1nDj5HE+dOlUyMzPNTY8+QjxjxgypVauW3HfffabR/YMPPmhuZho1amRuXiZOnGiCE9q3Ae4TX1N3OC1atAi5w7FxhwOgqv7xj3+YYESrkL/73e+a7gq0gX3Tpk3N+l/+8pdy9913mxKU3r17mxufVatWxfqwATihBIU7HADRsmLFikrXa8P8hQsXmgmAuyVE6w7ns88+M3c1vXr1KnOHEx8fb+5w9MmcAQMGyHPPPRfpwwAAAC4W8QCFOxwAAOD4RrIAAERT22nrQl5/NHtwzI4FkcNggQAAwHEIUAAAgOMQoAAAAMchQAEAAI5DgAIAAByHAAUAADgOAQoAAHAcAhQAAOA4BCgAAMBxCFAAAIDj0NU9qoSupAEANYkSFAAA4DiUoKBKJSYAANQkApQYIggAgMijStobqOIBAACOQ4ACAAAchwAFAAA4DgEKAABwHAIUAK6Rm5srt956qzRo0ECaNWsmQ4YMkfz8/JBt+vTpI3FxcSHT2LFjY3bMXm6IGjwBkUaAAsA1tm3bJuPHj5edO3fKpk2bpLi4WDIyMuT8+fMh240ePVqOHz8emObMmROzYwZQPTxmDMA1NmzYEPJ6yZIlpiRl79690rt378Dy5ORkSUtLi8ERAogUAhQArnX69Gkzb9SoUcjyZcuWydKlS02QkpmZKdnZ2SZoKU9RUZGZbGfOnDFzLZ3RKdLsfUZj39HUYeYbIa+TalW8bVK8FTKPNa6jc9IYzr4IUAC4UklJiUyaNEl69uwpHTp0CCwfMWKEtGnTRlq2bCn79++Xxx57zLRTWbVqVYXtWnJycsos37hxY4VBTSRoFZWbzOke/nt+1q1EnGD9+vVR27fbrmOs01hYWFjlbQlQALiStkXJy8uTt956K2T5mDFjAn937NhRWrRoIX379pXDhw/LddddV2Y/WVlZMmXKlJASlFatWpm2LSkpKRE/br2D1Ay/f//+kpiYKG4tQamMlpxocJK9J16KSuIk1vJmDoj4Pt16HWOdRruEsioIUAC4zoQJE2Tt2rWyfft2ufbaayvdNj093cwPHTpUboCSlJRkptI0Q47mfzzR3n+kFV0OP9DQ4KQ674s0rqNz0hjOfghQALiGZVkyceJEWb16tbz55pvSrl27K75n3759Zq4lKQDcgwAFgKuqdZYvXy6vvfaa6QvlxIkTZnlqaqrUrVvXVOPo+kGDBknjxo1NG5TJkyebJ3w6deoU68MHEAYCFACusWjRokBnbMEWL14so0aNktq1a8vmzZtl/vz5pm8UbUsyfPhwmT59eoyO2L3ofA2xRoACwFVVPJXRgEQ7cwPgfvQkCwAAHIcABQAAOA4BCgAAcBwCFAAA4DgEKAAAwHF4igdReSTxo9mDY3YsAAD3I0ABAHgaN1DuRIASRV7+UdCJEwAgmmiDAgAAHIcSlAiiVAEAgMggQAEAcIMFx6GKBwAAOA4BCgAAcBwCFAAA4Di0QQEA+JqXu4RwMwKUGuSnRmj84AE4lZ/yYjcjQAEAH+I/aTgdAcpV4AdedZSoAADCQSNZAADgOJSgAIAPUOILtyFAKYWqCABuQX4VHZxXZ6CKBwAAOE5MS1AWLlwoc+fOlRMnTkjnzp1lwYIF0r17d3EyikkjgzsURJsb8xf4D3mhAwOUV155RaZMmSLPP/+8pKeny/z582XAgAGSn58vzZo1E6cgIHGm4OsS7g863AyBDMR93JK/OOl3gfDOc1ItS+Z0F+kw8w3J//ndUf2sYH66rjELUObNmyejR4+W+++/37zWjGTdunXy4osvyrRp00K2LSoqMpPt9OnTZv75559LcXFxpZ+Tnrsl5PWurL6Vro91o5yEEksKC0skoTheLpfEiV98beqrFa5Lirdk+tdLpMvjq6To/89J8HX67LPPKt33la5xZZ9d3vZX+rxwBR9f6e9nRfR7X1hYaI4lMTGx3H2Vpyr7P3v2rJlbliVu5ZT8pbzty/s+V/T+K+0/4dL5Cj+r9Pe6JvM2r+Vj5Z3L4DRWloeE+3/Ola7bZ1eZ/4TznS2dz9R4/mLFQFFRkVWrVi1r9erVIct//OMfW9/+9rfLbD9jxgxNCRMTUw1OR48etdyI/IWJSTyRv8SkwODTTz+Vy5cvS/PmzUOW6+sPPvigzPZZWVmmuNZWUlJi7m4aN24scXHuj86DnTlzRlq1aiVHjx6VlJSUWB+OI3BOavac6J2N3uW0bNlS3Mjp+Ysfvs+k0RvORCGN4eQvsa7RqJKkpCQzBWvYsKF4mX4ZvPqlry7OSc2dk9TUVPGLWOUvfvg+k0ZvSIlwGquav8TkMeMmTZpIrVq15OTJkyHL9XVaWlosDgmAR5C/AN4QkwCldu3a0rVrV9myZUtIsaq+7tGjRywOCYBHkL8A3hCzKh6t8x05cqR069bN9E2gjwGeP38+0Orer7SoecaMGWWKnP2Mc1IW58S9+Ysfrh1p9IakGKcxTlvKxuSTReRXv/pVoCOlLl26yLPPPmv6LACAq0X+ArhbTAMUAACA8jAWDwAAcBwCFAAA4DgEKAAAwHEIUAAAgOMQoDhsePi2bdtKnTp1zNMGf/nLX8Qvtm/fLpmZmab7Y+1efM2aNSHrtS33E088IS1atJC6detKv3795ODBg+Jlubm5cuutt0qDBg3MCLxDhgwxo/EGu3DhgowfP950y16/fn0ZPnx4mQ7K4Bw///nP5Zvf/KYkJydX2FvtkSNHZPDgwWYbve6PPPKIXLp0SdzES3mZ1/OmXAfnMwQoDhseXp85f+edd6Rz585mePhTp06JH2gfFZpmzdjKM2fOHPOYqI5Ku2vXLqlXr545P/rD8apt27aZTGHnzp2yadMmM7JoRkaGOVe2yZMny+uvvy4rV6402x87dkyGDRsW0+NGxS5evCj33nuvjBs3rtz1OoaQBie63dtvvy0vvfSSLFmyxPwH6BZey8u8njdtc3I+E8FBRHEVunfvbo0fPz7w+vLly1bLli2t3Nxcy2/0axk8Em1JSYmVlpZmzZ07N7CsoKDASkpKsn73u99ZfnHq1ClzbrZt2xY4B4mJidbKlSsD2xw4cMBss2PHjhgeKa5k8eLFVmpqapnl69evt+Lj460TJ04Eli1atMhKSUkxozS7gZfzMj/kTacclM9QguIAere0d+9eUzRoi4+PN6937Nghfvfhhx+azraCz48ONqVFx346P6dPnzbzRo0ambl+Z/RuJ/i83HTTTdK6dWtfnRcv0evWsWPHkJGY9W5cR5V97733xOn8lpd5MW867aB8hgDF4cPD65ff7+xz4Ofzo2PJTJo0SXr27CkdOnQwyzTtOu5M6bYMfjovXqPXrbzvub3O6fyWl3ktbypxWD5DgAK4gNYR5+XlyYoVK2J9KChl2rRppvFkZdMHH3wQ68MEXJfPxGywQHyJ4eErZ58DPR/aUt6mr3WMFa+bMGGCrF271jxNcO2114acFy1SLygoCLm74XtTsx5++GEZNWpUpdt89atfrdK+9LqVfuLFzhfccE39lpd5KW+a4MB8hhIUB2B4+Mq1a9fO/BCCz4/WyWuLeS+fH22Tp5nG6tWrZevWreY8BNPvTGJiYsh50ccD9TFVL58Xp2natKmpk69s0t94Veh1++tf/xryxIs+WZGSkiI333yzOJ3f8jIv5E2Wk/OZqDbBRZWtWLHCtPxesmSJ9f7771tjxoyxGjZsGNKa38vOnj1rvfvuu2bSr+W8efPM3x9//LFZP3v2bHM+XnvtNWv//v3WPffcY7Vr18764osvLK8aN26cedLjzTfftI4fPx6YCgsLA9uMHTvWat26tbV161Zrz549Vo8ePcwEZ9Lvs36vc3JyrPr16we+8/r9V5cuXbI6dOhgZWRkWPv27bM2bNhgNW3a1MrKyrLcwmt5mdfzpnEOzmcIUBxkwYIF5ktQu3Zt86jezp07Lb/44x//aH78paeRI0cGHufLzs62mjdvbjK/vn37Wvn5+ZaXlXc+dNJHVG2aCT700EPWNddcYyUnJ1tDhw41mQucSb/P5V1T/f7bPvroI2vgwIFW3bp1rSZNmlgPP/ywVVxcbLmJl/Iyr+dN4uB8Ju7/DxAAAMAxaIMCAAAchwAFAAA4DgEKAABwHAIUAADgOAQoiIk333wzpKfNPXv21Pgx6LDpsT4GAO7PS8KhXcnbx1q/fv1YH46jEaC4nA7FfqUfpT6o9dvf/lZ69+5tegJMTk42A5LNmjUrZEjt0nR47czMTDPmgnbApINH6T5+8YtfmM6IgrVt2zbwo9PBwfRz9DPGjBljOi2qyE9/+lNzbME9bfbp06fCLsO1w6Bw6CBX2sGVvveZZ54JWdetWzfz2XqMgN95MS85fvy4GYrgzjvvlAYNGph9akBTno0bN8qDDz5oxqDR3nD1OML1hz/8Qb7xjW9InTp1zGB6M2bMkEuXLoVs86Mf/cgc5+233x72/v2Gru49TgfuGjFihLz66qvmBzFz5kyTqfzpT3+SnJwcWblypWzevDlksCvt+VF/qJphacbw0EMPSatWreTs2bNm9Mrp06fL+vXrQ3oWVNq1s3b7rXTbAwcOmP3/5je/kcmTJ8u8efPKHF///v1NQBLs8ccfl3/7t38LWaaZ39ixYyUjIyOs9C9YsMD0eFge7c75hz/8oclA/vM//zOs/QJ+48a8RHs8ffrpp+X66683n1/Z6LvLly+XV155xQQYLVu2DPv8/Pd//7cMGTLEHIPmO9oj8JNPPml6BV60aFFIz6w66bl65513wv4cX4l6TyuIKu1MRy/j7t27y13/1FNPmfVTp04ts+4Pf/iDFR8fb911110hy3Nzc817Jk+ebDohKu3YsWOm98Rgbdq0sQYPHlxmW+2NcMiQIWZ/zz33XJnOj4I7qKrMb3/7W7P9smXLrKo6efKk6SFx1qxZ5r1z586t1jkE/MCLecmZM2eszz77zPy9cuXKSvOcTz75xLp48aL5Wz9fjyMcN998s9W5c+eQTvUef/xxKy4uzjpw4ECZ7bWjt3r16oX1GX5DgOLhTEV/0Nrz3w033FBhT5T333+/ef+OHTvM6/Pnz5tum2+55RbT7XZVVZSp2F1FN2rUyPrKV74SyKTCDVC0Z039MZ87d67Kx6Rp014s//73vxOgAD7PS64UoAQLN0B57733zL4XLlxYJujR5T/72c/KvIcA5cpog+Jhb731lvzrX/8yxbIJCeXX5v34xz82cx3F0n6Pjlp53333mXrYSNCGYEOHDpVPPvlE3n///bDf/89//tMMmKbFp/Xq1avSe3RE2Jdeeknmz59v6p0BVJ9X8pJoeffddwPt2oJpVZFWJdvrER4CFA+zf8CdO3eucBt7ndbxqg8++MDMtaFY6frnTz/9NGQKZ5QEe3+HDx8OOx1aL6ztRH7wgx9UaXs9rokTJ8r3vvc914woCjiZV/KSaNHGuKpFixZl1umyY8eOxeCo3I9Gsh6mjcuUtl6viL3Obklvz0s//qYNvr7+9a+XKdlo0qRJlY7F3p99TOHQxms6pL02gqsKbZCnx/v73/8+7M8C4N28JFq++OILM09KSiqzTp/oKf2kEqqGEhQPszOMyn7IpTMee37u3LmQ7b72ta+Zahad9DG5cNn7qyyDK8/f//530/JeS0MqKloOphlBVlaWPPLII+ZpAQBXzwt5STTVrVvXzIuKisqsu3DhQmA9wkOA4mHt27c38/3791e4jb1O+wpRN910k5nn5eWVuWvp16+fmYL7Gagqe3+aOYVbeqKqWr2jfZ1cvHjRBDQfffSRmf7xj3+YdVqHrq91PQB/5SXRZFft2FU9wXRZdR5bBgGKp/Xq1ct0cqT/yWu9b3lefvllM7/77rvNXPs3SE1NlRUrVpg+DCJB73hWr15tSjTsjK6q9Nivu+46ue2226q0vfZ5ooHILbfcIu3atTOT3SHSU089ZV47qXEd4AZeyEuiSfttUaU7udO2J3qDZK9HeAhQPEw7UZo6darprEg7Pytt3bp1pr3GgAEDAgGAvufRRx81dynaA2N5jdfCadCmdbNajPv555+bYwjniRpt+a4N7vTJgYpoQ7ngxnL//u//bjKw4OnXv/61WTdq1CjzWoMUAP7JSyJJe6fWBsDBpSV6Q6QlRtrhY3AApx206XF+5zvficmxuh2NZD3ixRdflA0bNpRZrk+z6H/02puituUYPny4qQ/VRwCXLl1q7kL0cdxgmploYDB37lzT/bO+Rx+V05IJ7flQe3Rs1qyZafwVTB/9033adzpaUqHbnjhxwvQK+ZOf/CSsNC1btuyK1Tt9+/Y1c626UdoLpE7B7HWaieijygD8k5dob67qvffeM3PtZl6PWWlPtsFVVNpVvTp06JCcPn068F59Qkm76rePTdM6cuRIE5TZNI3f/va3TW/X3//+901g9qtf/cr0iu2k0h5XqUJfKXBB50oVTUePHrUuX75stuvZs6eVkpJi1alTx3SelJOTU2nHZ6tXr7YGDRpkNW3a1EpISDCdLvXq1ct0eFZQUBCyrXZqZH+m9pyon6OfMXr0aGvXrl1l9n2lzpX0mLUzpm984xuVpl8/90odKn344Yd01Ab4NC+pLE1VTb92qlY6PwleFpzOLl26WElJSda1115rTZ8+PdA7bWl01HZlcfpPrIMk+I8O2KUDeOmIwj179jT121V5SieStLGsPvWjdeR6d7h79+4yHS0BcDYn5CXh0HHFtLpK8xwdRLH0U074Em1QEFNa5aJ9nOzbt6/GP1sHKdPP1owCgLvFMi8Jh7af0ePUGyNUjhIUxITWQe/duzfwOj09vcb7NdDOof7nf/4npscAwP15STj+9re/BUZY15Ke0iMw40sEKAAAwHGo4gEAAI5DgAIAAByHAAUAADiOc5/FqoR2m6xdCGtDqFj1Jgh4lTZL04HfdPyQ+Hj/3cOQvwAOyV8sF9IOgyrrfIeJienqJ/2d1bRt27ZZd999t9WiRQtzDNrxVbCSkhIrOzvbSktLM52E9e3b1/rb3/4Wss1nn31mjRgxwmrQoIGVmppqPfDAA9bZs2erfAzkL0xM4oj8xZUlKPYjZEePHpWUlJSQMRK0O2XtajgxMVH8wq/pVqQ98mnXzut0MLZYPKqpnVhpt+IPPPCADBs2rMz6OXPmyLPPPmu6VNcxlbKzs834L9oVut1dug6NoOOkbNq0yZyj+++/X8aMGRMYGbu6+cuV+O27SHq9rzgKaQ4nf3FlgGIXu2rmUTpA0QGqdJlfvkB+Trci7dFLeyyqNwYOHGimioqG58+fb8ZPueeeewIj6DZv3tz0Iqrjn+i4LzqOTHCvwAsWLJBBgwbJM888U6Vh7yvKX67Eb99F0ut9xVFMc1XyF1cGKAD858MPPzSDxfXr1y+wLDU11XTMpYPXaYCic+3qPHjIAt1e67p37dolQ4cOLbPfoqIiMwXf4dmZs05VZW8bznvcjPR6X3EU0hzOvghQALiCBidKS0yC6Wt7nc51dNxg2ltno0aNAtuUlpubKzk5OWWWa9G23j2GS6uW/IT0et+mCKa5sLCwytsSoADwtaysLJkyZUqZOnKtdw+3ikcz8v79+/uiCoD0el9xFNJsl1BWBQEKAFdIS0sz85MnT0qLFi0Cy/V1ly5dAtucOnUq5H2XLl2Szz//PPD+0pKSksxUmmbI1cmUq/s+tyK93pcYwTSHsx8CFB9pO21d4O+PZg+O6bEA4dKndjTI2LJlSyAg0bsxbVsybtw487pHjx5SUFBgBo/r2rWrWbZ161bTt4m2VUFs8htFnoNwEaAAcIxz587JoUOHQhrG7tu3z7Qhad26tUyaNEmefPJJuf766wOPGeuTOUOGDDHbt2/fXu666y4ZPXq0PP/886aIesKECaYBbVWe4AHgHAQoABxjz549cueddwZe221DRo4cKUuWLJFHH33U9JWi/ZpoSUmvXr3MY8V2Hyhq2bJlJijp27eveXpn+PDhpu8UAO5CgALAMfr06WP6O6ms74RZs2aZqSJa2lLVTtkQO1QB4Ur8N9AGAABwPAIUAADgOAQoAADAcQhQAACA49BIFgBQ441igSuhBAUAADgOAQoAAHAcAhQAAOA4BCgAAMBxCFAAAID7A5Tt27dLZmamGXhLu51es2ZNyHrtpvqJJ54ww6HXrVtX+vXrJwcPHgzZRoc+/8EPfiApKSnSsGFDefDBB80gYQAAANUKUHSgrs6dO8vChQvLXT9nzhwzMJeOJKrDoNerV08GDBggFy5cCGyjwcl7770nmzZtkrVr15qgRwf/AgAAqFY/KAMHDjRTebT0ZP78+TJ9+nS55557zLKXX35ZmjdvbkpadMjzAwcOmNFHd+/eLd26dTPbLFiwQAYNGiTPPPMMQ6LXEAbqAgD4pqO2Dz/8UE6cOGGqdWypqamSnp4uO3bsMAGKzrVaxw5OlG6vw6JricvQoUPL7LeoqMhMtjNnzph5cXGxmWz238HL/KCq6U6qVfEosW49Z3695tFMux/PJQCPByganCgtMQmmr+11Om/WrFnoQSQkmCHS7W1Ky83NlZycnDLLN27cKMnJyWWWa9WRH10p3XO6V7xu/fr14mZ+vebRSHthYWFE9wcAnu3qPisrS6ZMmRJSgtKqVSvJyMgwDW2D7/w0s+7fv78kJiaKX1Q13R1mvlHhuryZA8SN/HrNo5l2u4QSADwToKSlpZn5yZMnzVM8Nn3dpUuXwDanTp0Ked+lS5fMkz32+0tLSkoyU2maKZeXMVe03OuulO6iy3GVvtfN/HrNo5F2v55HAB7uB6Vdu3YmyNiyZUvI3Zi2LenRo4d5rfOCggLZu3dvYJutW7dKSUmJaasCAAAQdgmK9ldy6NChkIax+/btM21IWrduLZMmTZInn3xSrr/+ehOwZGdnmydzhgwZYrZv37693HXXXTJ69GjzKLIWU0+YMME0oOUJHgAAUK0AZc+ePXLnnXcGXtttQ0aOHClLliyRRx991PSVov2aaElJr169zGPFderUCbxn2bJlJijp27eveXpn+PDhpu8UAACAagUoffr0Mf2dVER7l501a5aZKqKlLcuXL+cKAAAA9z7Fg+ij4zYAsUQehNIYLBAAADgOAQoAAHAcAhQAAOA4BCgAAMBxaCSLctFgDUBldOgMHd9L59pLdaTziOA8iPzHnyhBAQAAjkOAAsA12rZta/paKj2NHz8+0E9T6XVjx46N9WEDqAaqeAC4xu7du+Xy5cuB13l5eWY053vvvTewTIfRCO4oMjk5ucaPE8DVI0AB4BpNmzYNeT179my57rrr5I477ggJSCoaGR2AexCgAHClixcvytKlS814YFqVEzzWly7XICUzM9MMWFpZKUpRUZGZgkdgVzqQqU5VZW8bznvcLCneCpmXTndSrYqHRAmXE86p365vtNIczr4IUAC40po1a8yApKNGjQosGzFihLRp08aMjL5//3557LHHJD8/X1atWlXhfnJzcyUnJ6fM8o0bN1aremjTpk3iBz/rZs9LzHz9+vUh6/UJn0gpve9Y8sv1jVaaCwsLq7wtAQoAV3rhhRdk4MCBJhix6Sjqto4dO0qLFi3MqOmHDx82VUHlycrKCozKbpegtGrVSjIyMiQlJSWsO0PNyLVNTGJionhd11kbTHCSvSdeikriJG/mgJD1+vhxpJTedyz47fpGK812CWVVEKAAcJ2PP/5YNm/eXGnJiEpPTzfzQ4cOVRigJCUlmak0zZCrkylX931uo0GJPdd+UEqnWZdFipPOp1+ub7TSHM5+eMwYgOssXrxYmjVrJoMHV96B1759+8xcS1IAuAslKABcpaSkxAQoI0eOlISEL7MwrcZZvny5DBo0SBo3bmzaoEyePFl69+4tnTp1iukxAwgfAQoAV9GqnSNHjsgDDzwQsrx27dpm3fz58+X8+fOmHcnw4cNl+vTpMTtWPw+PAVwtAhQArqKNVy2r7COsGpBs27YtJscEIPJogwIAAByHEhQAQNhVOEm1YnYo8AkCFFQrc2L4cwBANFHFAwAAHIcABQAAeD9Aadu2rRm4q/Q0fvx4s75Pnz5l1o0dOzbShwEAAFws4m1Qdu/eLZcvXw68zsvLM/3433vvvYFlo0ePllmzZgVeV2dALgAA4F0RD1CaNm0a8nr27NlmDIw77rgjJCDRodARWTo4V/D4FzRkBQC4VVSf4rl48aIsXbrUjBSqVTm2ZcuWmeUapGRmZkp2dnalpShFRUVmKj0aoo60qJPN/jt4mR/Y6U2KD+28qvR5SKplRfwzY82v1zyaaffjuQTgswBlzZo1UlBQIKNGjQosGzFihLRp08YMka5jZTz22GOSn59f6aikubm5kpOTU2b5xo0byw1sdHhoP9Khz4OtX78+5PWc7pH7rNL7jjW/XvNopL2wsDCi+wMAxwUoL7zwggwcONAEI7YxY8YE/u7YsaMZZbRv375moK+KhkPPysoypTDBJSjarbV2eZ2SkhJy56eZtbZ58dNw2Ha6s/fEB4ZAV3kzB5SpAoqU0vuOFb9e82im3S6hBABPBigff/yxGbirspIRlZ6ebuaHDh2qMEBJSkoyU2maKZeXMVe03Os0OAlug3J99sZSW3y57mo57fz69ZpHI+1+PY8AfNIPig6H3qxZMxk8uPKGmvv27TNzLUkBAACIWglKSUmJCVBGjhwpCQlffoRW4yxfvlwGDRokjRs3Nm1QJk+eLL1795ZOnTpxRQAAQPQCFK3aOXLkiDzwwAMhy2vXrm3WzZ8/X86fP2/akQwfPlymT58ejcMAAAAuFZUARRuvWlbZR1o1INm2bVs0PhIAAHgIY/EAAADHIUABAAD+6gcFAICr1XbaupDXDOPhD5SgAAAAxyFAAQAAjkOAAgAAHIcABQAAOA4BCgAAcBwCFAAA4DgEKAAAwHEIUAAAgOMQoAAAAMchQAHgGjNnzpS4uLiQ6aabbgqsv3DhgowfP14aN24s9evXN6Olnzx5MqbHDKB6CFAAuMott9wix48fD0xvvfVWYN3kyZPl9ddfl5UrV5qR048dOybDhg2L6fECqB7G4gHgKgkJCZKWllZm+enTp+WFF16Q5cuXy7e+9S2zbPHixdK+fXvZuXOn3HbbbTE4WgDVRYACwFUOHjwoLVu2lDp16kiPHj0kNzdXWrduLXv37pXi4mLp169fYFut/tF1O3bsqDBAKSoqMpPtzJkzZq770qmq7G3DeY+bJNWyQl/HWyHzmhSLc+z161tTaQ5nXwQoAFwjPT1dlixZIjfeeKOp3snJyZHbb79d8vLy5MSJE1K7dm1p2LBhyHuaN29u1lVEAxzdT2kbN26U5OTksI9x06ZN4kVzupe//GfdSmr6UGT9+vUSK169vjWV5sLCwipvS4ACwDUGDhwY+LtTp04mYGnTpo28+uqrUrdu3WrtMysrS6ZMmRJSgtKqVSvJyMiQlJSUsO4MNSPv37+/JCYmitd0mPlGyGstOdHgJHtPvBSVxNXoseTNHCA1zevXt6bSbJdQVgUBCgDX0tKSG264QQ4dOmQy0YsXL0pBQUFIKYo+xVNemxVbUlKSmUrTDLk6mXJ13+d0RZfLD0I0OKloXbTE8vx69frWVJrD2Q9P8QBwrXPnzsnhw4elRYsW0rVrV5P5bdmyJbA+Pz9fjhw5YtqqAHAXSlAAuMbUqVMlMzPTVOvoI8QzZsyQWrVqyX333Sepqany4IMPmuqaRo0ameqZiRMnmuCEJ3gA9yFAAeAa//jHP0ww8tlnn0nTpk2lV69e5hFi/Vv98pe/lPj4eNNBmz6ZM2DAAHnuuedifdgAqoEABYBrrFixotL1+ujxwoULzQTA3SLeBoWuqAEAgCNLULQr6s2bN3/5IQkJIV1Rr1u3znRFrXXGEyZMMF1R//nPf47GoXha22nrAh0oVdRHAQAAbhSVAIWuqP0THNk+mj04ZscCAPCeBC91Re23rojtrqdj2eW0LVbn3G/XvCbS7sdzCW464IMAxQldUfulK+LS1Tqx6HLaCV1P++ma10Taw+mKGgBcE6DEsitqv3VFbHc9Hcsup2PZ9bQfr3lNpD2crqgBwLWPGceiK2q/dEVcunvpWHQ5bYv1+fbLNa+JtPv1PAJwlqh3dU9X1AAAIOYlKHRFDQAAHBeg0BU1AABwXIBCV9QAAMDxbVAAAADCRYACAAAchwAFAAA4DgEKAABwHAIUAADgv55kEb3BvAAA8CpKUAAAgONQggIAKIMSW8QaJSgAAMBxCFAAAIDjEKAAAADHIUABAACOQyNZB6ORGgDAryhBAQAAjkOAAgAAHIcqHgCAq6u/P5o9OGbHguihBAUAADgOAQoA18jNzZVbb71VGjRoIM2aNZMhQ4ZIfn5+yDZ9+vSRuLi4kGns2LExO2YA1UMVDwDX2LZtm4wfP94EKZcuXZKf/vSnkpGRIe+//77Uq1cvsN3o0aNl1qxZgdfJyckxOmLn4ilBOB0BCgDX2LBhQ8jrJUuWmJKUvXv3Su/evUMCkrS0tBgcIYBIIUAB4FqnT58280aNGoUsX7ZsmSxdutQEKZmZmZKdnV1hKUpRUZGZbGfOnDHz4uJiM1WVvW0474mlpFrW1b0/3gqZx1JNnHO3XV+npjmcfRGgAHClkpISmTRpkvTs2VM6dOgQWD5ixAhp06aNtGzZUvbv3y+PPfaYaaeyatWqCtu15OTklFm+cePGalUNbdq0SdxgTvfI7Odn3Uok1tavX19jn+WW6+vUNBcWFlZ5WwIUB6FOGKg6bYuSl5cnb731VsjyMWPGBP7u2LGjtGjRQvr27SuHDx+W6667rsx+srKyZMqUKSElKK1atTJtW1JSUsK6M9SMvH///pKYmChO12HmG1f1fi050eAke0+8FJXESSzlzRwQ9c9w2/V1aprtEsqYBCh6N6J3Kh988IHUrVtXvvnNb8rTTz8tN954Y0gre23sFuwnP/mJPP/885E+HAAeNGHCBFm7dq1s375drr322kq3TU9PN/NDhw6VG6AkJSWZqTTNkKuTKVf3fTWt6HJkggoNTiK1r+q6PntjjfWL4pbr69Q0h7OfiAcotLL3JzpOQk2wLEsmTpwoq1evljfffFPatWt3xffs27fPzLUkBYB7JLihlX1VG7G5vRFTdRutOamxmq2mroHbr7kT0+7kc6k3P8uXL5fXXnvN9IVy4sQJszw1NdWU2Go1jq4fNGiQNG7c2LRBmTx5ssl7OnXqFOvDBxCGBDe0sg+3EZtbGzFdbaM1JzRWi0WjNTdfcyemPZxGbDVt0aJFgWriYIsXL5ZRo0ZJ7dq1ZfPmzTJ//nw5f/68aUsyfPhwmT59eoyOGIAjA5RItbKvaiM2tzdiqm6jNSc1VqvJRmteuOZOTHs4jdhiUcVTGc0XSrdvA+BOCW5oZR9uIza3NmK62oZmTmisZqvp8+/Wa+7EtPv1PALwSYASyVb2XsVjxQAA1FCAQit7AADguACFVvYAAMBxAQqt7KHoFwUA4LgqnsrQyh4AAFxJ/BW3AAAAqGEMFggA8BSqmL2BEhQAAOA4lKAAgAfRzxLcjhIUAADgOAQoAADAcQhQAACA4xCgAAAAxyFAAQAAjkOAAgAAHIfHjGuQnx/7o+MkAEA4KEEBAACOQwkKADgUJY/wMwKUKPJzlQ4AAFeDACWCCEiqjztFANFC/uJOBCgA4BHcJFUNAYs70EgWAAA4DiUoAOBSlJjAywhQEBNkrED4+N3AT6jiAQAAjkMJyhXQmApATaGExPv4P6XqCFDgSPyIAcDfYhqgLFy4UObOnSsnTpyQzp07y4IFC6R79+7iZNzhAO7gxvwFgAMClFdeeUWmTJkizz//vKSnp8v8+fNlwIABkp+fL82aNYvVYQHwALfkL9zwuKPEVtcn1bJkTneRDjPfkPyf313DR+hPMQtQ5s2bJ6NHj5b777/fvNaMZN26dfLiiy/KtGnTYnVYcFmGYmca5a2rbvVQVTKrq9k/oo/8BXC/mAQoFy9elL1790pWVlZgWXx8vPTr10927NhRZvuioiIz2U6fPm3mn3/+uRQXFweW69+FhYXy2WefSWJiolmWnrslZF+7svqGvC693o2NdBJKLCksLJGE4ni5XBInXvS1qa+We13stAdf84RL50O21XVXc80r+uyK9h+u4OMp/f2sTFW+76VVZf9nz541c8uyxI2ilb+UJ/h8J8VbMv3rJdLl8VVS9P+/wyud79LfVTfxcr5T3m8+OL2l15cWfN1L/yavlH9E4jcc7Er/B4aTx9R4/mLFwCeffKJHZr399tshyx955BGre/fuZbafMWOG2Z6JianmpqNHj1puRP7CxCSeyF/cUEBg7oS0PtlWUlJi7m4aN24scXFfRu5nzpyRVq1aydGjRyUlJUX8wq/pVqQ98mnXOxu9y2nZsqX4QVXzlyvx23eR9HrfmSikOZz8JSYBSpMmTaRWrVpy8uTJkOX6Oi0trcz2SUlJZgrWsGHDCvevJ9IvX6Bgfk23Iu2RTXtqaqq4VbTzlyvx23eR9HpfSoTTXNX8JSY9ydauXVu6du0qW7ZsCblr0dc9evSIxSEB8AjyF8AbYlbFo0WqI0eOlG7dupm+CfQxwPPnzwda3QNAdZG/AO4XswDle9/7nvzzn/+UJ554wnSk1KVLF9mwYYM0b9682vvUYtoZM2aUKa71Or+mW5F2f6Y9FvnLlfjtepBe70uKcZrjtKVsTD4ZAACgAoxmDAAAHIcABQAAOA4BCgAAcBwCFAAA4DgEKAAAwHE8E6D8/Oc/l29+85uSnJxcYS+QR44ckcGDB5ttdMj1Rx55RC5duiRut3DhQmnbtq3UqVPHDC3/l7/8Rbxm+/btkpmZabpH1u7H16xZE7JeH0bTR0pbtGghdevWNQPDHTx4UNwuNzdXbr31VmnQoIH5zg4ZMkTy8/NDtrlw4YKMHz/edM1ev359GT58eJleVBFdfsx/vJrv+C2vyXVwHuOZAEVHML333ntl3Lhx5a6/fPmyyRx0u7ffflteeuklWbJkifmiudkrr7xiOqXSZ9Xfeecd6dy5swwYMEBOnTolXqKdbGnaNFMsz5w5c+TZZ5+V559/Xnbt2iX16tUz50F/WG62bds2kzHs3LlTNm3aZEYXzcjIMOfDNnnyZHn99ddl5cqVZvtjx47JsGHDYnrcfuO3/MfL+Y7f8pptTs5jLI9ZvHixlZqaWmb5+vXrrfj4eOvEiROBZYsWLbJSUlKsoqIiy610dNbx48cHXl++fNlq2bKllZubG9Pjiib92q5evTrwuqSkxEpLS7Pmzp0bWFZQUGAlJSVZv/vd7ywvOXXqlEn/tm3bAulMTEy0Vq5cGdjmwIEDZpsdO3bE8Ej9yS/5j1/yHT/mNacclMd4pgTlSnbs2CEdO3YM6UlSo14drfG9994TN9K7sb1795oiRlt8fLx5ren1iw8//ND0Fhp8HnQwKi129tp5OH36tJk3atTIzPX66x1PcNpvuukmad26tefS7mZeyn/8nO/4Ia857aA8xjcBin6pSndzbb/WdW706aefmqLj8tLl1jRVh51Wr58HHfBu0qRJ0rNnT+nQoYNZpunTwfFKt3vwWtrdzkv5j5/zHa/nNSUOy2McHaBMmzbNNFKqbPrggw9ifZhAjdB64ry8PFmxYkWsD8UXyH/gN+MdlsfEbLDAqnj44Ydl1KhRlW7z1a9+tUr7SktLK9PK3G6FrOvcqEmTJlKrVq0yran1tVvTVB12WjXd2rLepq91kDgvmDBhgqxdu9Y8YXDttdeGpF2L3AsKCkLucPz2HYgG8p/y+Tnf8XJeM8GBeYyjS1CaNm1q6roqm7ToqSp69Oghf/3rX0NamWuL5ZSUFLn55pvFjTTtXbt2lS1btoQU0elrTa9ftGvXzvxQgs+D1u1rC3u3nwdtp6cZx+rVq2Xr1q0mrcH0+icmJoakXR8R1Eda3Z72WCP/KZ+f8x0v5jWWk/MYyyM+/vhj691337VycnKs+vXrm791Onv2rFl/6dIlq0OHDlZGRoa1b98+a8OGDVbTpk2trKwsy81WrFhhWpAvWbLEev/9960xY8ZYDRs2DHlawAv0OtrXVL+28+bNM3/rdVezZ8826X7ttdes/fv3W/fcc4/Vrl0764svvrDcbNy4ceapkDfffNM6fvx4YCosLAxsM3bsWKt169bW1q1brT179lg9evQwE2qO3/IfL+c7fstrxjk4j/FMgDJy5EjzZSo9/fGPfwxs89FHH1kDBw606tatazVp0sR6+OGHreLiYsvtFixYYL48tWvXNo//7dy50/IavY7lXV+97vbjf9nZ2Vbz5s1Nxtm3b18rPz/fcrvy0qyTPs5q04zxoYcesq655horOTnZGjp0qMlgUHP8mP94Nd/xW14jDs5j4v7/AAEAABzD0W1QAACAPxGgAAAAxyFAAQAAjkOAAgAAHIcABQAAOA4BCgAAcBwCFAAA4DgEKAAAwHEIUAAAgOMQoAAAAMchQAEAAOI0/wtqsidlOe+MDQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "mols.hist(['LOGP', 'LOGD[3.0]', 'LOGD[7.4]', 'LOGD[11.0]'], bins=50)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "3342b6123213a517", + "metadata": {}, + "source": [ + "### **ADMET predictors**" + ] + }, + { + "cell_type": "markdown", + "id": "b55ece180fc11860", + "metadata": {}, + "source": [ + "There are four endpoints available in the Chemaxon Python API falling into the ADMET predictions category: _herg_classifiaction_, _herg_activity_, _bbb_ (Blood-Brain Barrier) and _cns_mpo_ (CNS Multiparameter Optimisation)." + ] + }, + { + "cell_type": "code", + "id": "283cae5aef6dbd02", + "metadata": {}, + "source": [ + "from chemaxon import herg_classification, herg_activity, bbb, cns_mpo\n", + "\n", + "print(str(mol), ' (aspirin)')\n", + "print()\n", + "print('HERG classification:', \"SAFE\" if herg_classification(mol).classification == 0 else \"TOXIC\")\n", + "print('HERG activity: ', herg_activity(mol).value)\n", + "print('BBB score: ', bbb(mol).score)\n", + "print('CNS MPO score: ', cns_mpo(mol).score)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "b919537734868299", + "metadata": {}, + "source": [ + "mols_admet = pandas.read_table('nci50.smiles', names=['SMILES', 'NCI_ID'])\n", + "\n", + "mols_admet['HERG classification'] = mols_admet.apply(lambda row: \"SAFE\" if herg_classification(import_mol(row['SMILES'])).classification == 0 else \"TOXIC\", axis = 'columns')\n", + "mols_admet['HERG activity'] = mols_admet.apply(lambda row: herg_activity(import_mol(row['SMILES'])).value, axis = 'columns')\n", + "mols_admet['BBB score'] = mols_admet.apply(lambda row: bbb(import_mol(row['SMILES'])).score, axis = 'columns')\n", + "mols_admet['CNS MPO score'] = mols_admet.apply(lambda row: cns_mpo(import_mol(row['SMILES'])).score, axis = 'columns')\n", + "\n", + "mols_admet" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "e38e8bb09a41c99c", + "metadata": {}, + "source": [ + "Filter out the TOXIC compounds based on their hERG classification." ] + }, + { + "cell_type": "code", + "id": "e832650c7a89f47f", + "metadata": {}, + "source": [ + "mols_admet.filter(items=['SMILES', 'NCI_ID', 'HERG classification', 'HERG activity']).query('`HERG classification` == \"TOXIC\"')" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "460ca8eb85abedbe", + "metadata": {}, + "source": [ + "### **Isoelectric point calculation**" + ] + }, + { + "cell_type": "code", + "id": "4d623f46db883daf", + "metadata": {}, + "source": [ + "from chemaxon import isoelectric_point\n", + "\n", + "glycine_mol = import_mol('C(C(=O)O)N')\n", + "\n", + "res = isoelectric_point(glycine_mol)\n", + "\n", + "print('Isoelectric point of glycine: ', res.isoelectric_point)\n", + "print('Charge distributions: \\n', res.charge_distributions)\n", + "print()" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "efa7a82fc57b8f03", + "metadata": {}, + "source": [ + "from chemaxon import PhRange\n", + "\n", + "res = isoelectric_point(glycine_mol, ph_range=PhRange(0, 14, 0.5))\n", + "\n", + "rows = []\n", + "for charge_res in res.charge_distributions:\n", + " rows.append({'pH': charge_res.pH, 'Charge distribution': charge_res.charge})\n", + "\n", + "df = pandas.DataFrame(rows)\n", + "df" + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Conformer calculation**", + "id": "93caaa3fa88c81a2" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "#helper function to show the result of the conformer and tautomer calculation\n", + "def display_result(result: list):\n", + " for res in result:\n", + " display(res[0])\n", + " print(res[1])\n", + " \n", + "from chemaxon import import_mol, conformers\n", + "\n", + "mol = import_mol(\"OC1CC(O)CCC1\")\n", + "result = conformers(mol)\n", + "display_result(result)" + ], + "id": "153fe02e877229e7", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from chemaxon import ConformerOptions, ConformerForceField, ConformerEnergyUnit, ConformerOptimization\n", + "\n", + "options = ConformerOptions()\n", + "options.max_number_of_conformers = 3\n", + "options.energy_unit = ConformerEnergyUnit.KJ_PER_MOL\n", + "options.force_field = ConformerForceField.MMFF94\n", + "options.optimization_limit = ConformerOptimization.VERY_STRICT\n", + "result = conformers(mol, options=options)\n", + "display_result(result)" + ], + "id": "52a2f4bedcb9ef20", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Solubility calculation**", + "id": "22c3ba6d23fe3dfe" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from chemaxon import import_mol, solubility\n", + "\n", + "mol_str = \"CC(=O)OC1=CC=CC=C1C(O)=O\"\n", + "mol = import_mol(mol_str)\n", + "solubility(mol)" + ], + "id": "e1eef8678004a2da", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from chemaxon import solubility_ph_range, PhRange\n", + "from chemaxon.calculations.solubility import SolubilityUnit\n", + "\n", + "result = solubility_ph_range(mol, PhRange(0.0, 14.0, 1.0), unit = SolubilityUnit.MOL_PER_L)\n", + "rows = []\n", + "for res in result:\n", + " rows.append({'pH': res.ph, 'Solubility': res.solubility})\n", + "\n", + "df = pandas.DataFrame(rows)\n", + "df" + ], + "id": "4e923e7caab4bc93", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Tautomer calculation**", + "id": "b5175c4fd4d84274" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from chemaxon import import_mol, all_tautomers, dominant_tautomer_distribution, canonical_tautomer, major_tautomer, TautomerAdvancedOptions\n", + "\n", + "mol = import_mol(\"OC1=NC=CC2=CC=NC=C12\")\n", + "result = all_tautomers(mol)\n", + "for res in result:\n", + " display(res)" + ], + "id": "f3af005d1832bb69", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "mol = import_mol(\"CC1=CNCC(O)=C1\")\n", + "result = dominant_tautomer_distribution(mol)\n", + "display_result(result)" + ], + "id": "b5c1b85e30fa50a8", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "mol = import_mol(\"OC1N(S)CC=CC1=O\")\n", + "canonical_tautomer(mol, normal=True)" + ], + "id": "e973360c5ab8dba0", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "mol = import_mol(\"OC1=NC=CC2=CC=NC=C12\")\n", + "major_tautomer(mol, ph=14.0)" + ], + "id": "29d55dab2d287549", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "mol = import_mol(\"CC(=O)\\C=C(/O)CC1=CC=CC=C1\")\n", + "advanced_options = TautomerAdvancedOptions()\n", + "advanced_options.protect_double_bond_stereo = True\n", + "major_tautomer(mol, options=advanced_options)" + ], + "id": "e6001e311478c464", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "f9dbdbb6dd0578db", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "#!{sys.executable} -m pip install matplotlib\n", + "\n", + "import matplotlib\n", + "\n", + "df.plot(x='pH', y='Charge distribution', kind='line', marker='o', title='Charge Distribution vs pH for Glycine', grid=True)" + ], + "id": "191f6fbd86484972", + "outputs": [], + "execution_count": null } ], "metadata": { @@ -798,7 +481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.3" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/jupyter/04_standardizer.ipynb b/jupyter/04_standardizer.ipynb index 46dae10..7cc81ed 100644 --- a/jupyter/04_standardizer.ipynb +++ b/jupyter/04_standardizer.ipynb @@ -2478,6 +2478,28 @@ "source": [ "Standardizer('ungroupsgroups..aromatize').standardize(mol_w_sgroups)" ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Using XML configuration for standardizer:", + "id": "507a202d9414b975" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from chemaxon import import_mol, Standardizer\n", + "\n", + "configFile = \"path/to/my_config.xml\"\n", + "with open(configFile) as config:\n", + " standardizer = Standardizer(config)\n", + " mol = import_mol(\"[H].[H]C1=C([H])C([H])=C([H])C([H])=C1[H]\")\n", + " standard_mol = standardizer.standardize(mol)" + ], + "id": "44372e0f4eee37a6" } ], "metadata": { diff --git a/jupyter/05_structure_checker.ipynb b/jupyter/05_structure_checker.ipynb index 715d187..53cef01 100644 --- a/jupyter/05_structure_checker.ipynb +++ b/jupyter/05_structure_checker.ipynb @@ -1,5 +1,17 @@ { "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Structure Checker**", + "id": "a516f35da6f8f94a" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "For the concept of Structure Checker in general see [Structure Checker User's Guide](https://docs.chemaxon.com/latest/structure-checker_user-guide.html).", + "id": "cd0228716795281f" + }, { "cell_type": "code", "execution_count": 1, diff --git a/jupyter/06_molecule_search.ipynb b/jupyter/06_molecule_search.ipynb index 8d35147..0560b34 100644 --- a/jupyter/06_molecule_search.ipynb +++ b/jupyter/06_molecule_search.ipynb @@ -91,9 +91,7 @@ "cell_type": "markdown", "id": "696bd8fc-05a7-4b74-b700-b095fec68733", "metadata": {}, - "source": [ - "Hit indexes" - ] + "source": "### **Hit indexes**" }, { "cell_type": "code", @@ -111,9 +109,7 @@ "cell_type": "markdown", "id": "75c7ad5b-0e83-4525-a9fb-d9d65f472434", "metadata": {}, - "source": [ - "Duplicate search, setting search options" - ] + "source": "### **Duplicate search, setting search options**" }, { "cell_type": "code", @@ -131,9 +127,7 @@ "cell_type": "markdown", "id": "bca9acc0-dfe1-4a34-be7e-5892ff677905", "metadata": {}, - "source": [ - "Hit coloring" - ] + "source": "### **Hit coloring**" }, { "cell_type": "code", @@ -151,9 +145,7 @@ "cell_type": "markdown", "id": "5ef2d767-c818-4577-8322-98cc6680f375", "metadata": {}, - "source": [ - "Setting standardizer" - ] + "source": "### **Setting standardizer**" }, { "cell_type": "code", @@ -182,9 +174,7 @@ "cell_type": "markdown", "id": "a6fd2f42-6a18-4bf8-af30-a8126c92109a", "metadata": {}, - "source": [ - "No standardizer" - ] + "source": "### **No standardizer**" }, { "cell_type": "code", @@ -212,9 +202,7 @@ "cell_type": "markdown", "id": "a366b219-1232-4b65-bd10-73589713a051", "metadata": {}, - "source": [ - "Bulk Search" - ] + "source": "### **Bulk Search**" }, { "cell_type": "code", @@ -342,9 +330,7 @@ "cell_type": "markdown", "id": "06c1cba7-2407-41f4-ab96-adcbaa3f590f", "metadata": {}, - "source": [ - "Bulk search speed" - ] + "source": "### **Bulk search speed**" }, { "cell_type": "code", diff --git a/jupyter/07_pandas_integration_examples.ipynb b/jupyter/07_pandas_integration_examples.ipynb index f724308..ee86f38 100644 --- a/jupyter/07_pandas_integration_examples.ipynb +++ b/jupyter/07_pandas_integration_examples.ipynb @@ -1,31 +1,40 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# __Pandas examples with ChemAxon molecules__", - "id": "b70058148a1f011b" + "id": "b70058148a1f011b", + "metadata": {}, + "source": [ + "# __Pandas examples with ChemAxon molecules__" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "You can see simpler examples of using pandas.DataGrame.read_table with ChemAxon molecules in the [Calculators](02_calculators.ipynb) and [Molecular similiarity notebook](03_molecular_similarity.ipynb). But here are some additional examples showing how to work with ChemAxon molecules in pandas DataFrames.", - "id": "74aa29c7fecce4b7" + "id": "74aa29c7fecce4b7", + "metadata": {}, + "source": [ + "You can see simpler examples of using pandas.DataGrame.read_table with ChemAxon molecules in the [Calculators](02_calculators.ipynb) and [Molecular similiarity notebook](03_molecular_similarity.ipynb). But here are some additional examples showing how to work with ChemAxon molecules in pandas DataFrames." + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "By default, molecules are printed in the _cxsmiles_ format. In case of issues with that, it is written in the _cxsmarts_ format. If none of them are possible for some reason, representation falls back to use the original format, which was recognized during the import process.", - "id": "b1ec732d397843a3" + "id": "b1ec732d397843a3", + "metadata": {}, + "source": [ + "By default, molecules are printed in the _cxsmiles_ format. In case of issues with that, it is written in the _cxsmarts_ format. If none of them are possible for some reason, representation falls back to use the original format, which was recognized during the import process." + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "7f80b7d19aaee0a3", "metadata": { "ExecuteTime": { - "end_time": "2026-02-02T15:19:21.211892562Z", - "start_time": "2026-02-02T15:19:21.143339417Z" + "end_time": "2026-06-03T15:02:52.828061395Z", + "start_time": "2026-06-03T15:02:52.271133087Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import pandas as pd\n", "from chemaxon import import_mol, open_for_import\n", @@ -41,171 +50,163 @@ "df_mols = pd.DataFrame(data=d)\n", "\n", "df_mols" - ], - "id": "7f80b7d19aaee0a3", - "outputs": [ - { - "data": { - "text/plain": [ - " molecule\n", - "0 CC(=O)NC1=CC=C(O)C=C1 |c:9,t:4,6|\n", - "1 CC(C)CC1=CC=CC=C1 |c:6,8,t:4|" - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 4 + ] }, { + "cell_type": "markdown", + "id": "37b627c193000a57", "metadata": {}, + "source": [ + "We also provide a utility functions that return a dictionary object that already contains the __Molecule__ objects, so you can easily create a __DataFrame__ from it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d00e290012fecb07", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-03T15:10:13.216041514Z", + "start_time": "2026-06-03T15:10:13.086516041Z" + } + }, + "outputs": [], + "source": [ + "from chemaxon import load_molecules_for_pandas, prepare_molecules_for_pandas\n", + "\n", + "df_mols_2 = pd.DataFrame(data=load_molecules_for_pandas('mol_w_sgroups.mol'))\n", + "df_mols_2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1f2a58a8c2d64e3", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-03T15:10:15.012968238Z", + "start_time": "2026-06-03T15:10:14.895569083Z" + } + }, + "outputs": [], + "source": [ + "df_mol_with_custom_columns = pd.DataFrame(data=load_molecules_for_pandas(file_name='mol_w_sgroups.mol', molecule_column=\"mols\", molecule_str_column=\"cxsmiles\"))\n", + "df_mol_with_custom_columns.to_html('web_view.html', escape=False, formatters=dict(mols=mol_to_svg_formatter))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a432fcf935327495", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-03T15:10:16.886155677Z", + "start_time": "2026-06-03T15:10:16.749779096Z" + } + }, + "outputs": [], + "source": [ + "from chemaxon import prepare_molecules_for_pandas\n", + "\n", + "mol_lst_with_properties = []\n", + "with open_for_import('mol_with_properties.mrv') as mol_importer:\n", + " for m in mol_importer:\n", + " mol_lst_with_properties.append(m)\n", + "\n", + "df_mols_with_properties = pd.DataFrame(data=prepare_molecules_for_pandas(mol_lst_with_properties, read_properties_to_columns=True))\n", + "df_mols_with_properties" + ] + }, + { "cell_type": "markdown", - "source": "In case of creating HTML output from pandas.DataFrame, you can use the helper function _mol_to_svg_formatter_ to visualize the molecules as SVG images.", - "id": "7d771f74cf01d2f6" + "id": "7d771f74cf01d2f6", + "metadata": {}, + "source": [ + "In case of creating HTML output from pandas.DataFrame, you can use the helper function _mol_to_svg_formatter_ to visualize the molecules as SVG images." + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "27bf252df19e4e0f", "metadata": { "ExecuteTime": { "end_time": "2025-12-01T14:44:23.122745Z", "start_time": "2025-12-01T14:44:22.978179Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "from chemaxon import mol_to_svg_formatter\n", "\n", "df_mols.to_html('web_view.html', escape=False, formatters=dict(molecule=mol_to_svg_formatter))" - ], - "id": "27bf252df19e4e0f", - "outputs": [], - "execution_count": 3 + ] }, { + "cell_type": "markdown", + "id": "384199f4-c35c-4610-9194-8aa5a6d6ebb5", + "metadata": {}, + "source": [ + "HTML output combined with molecule file loading. Loading is parametrized in order to see the exported molecule in the cxsmiles column:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e538e04-5b88-40c9-be4c-fca67963bd51", "metadata": {}, + "outputs": [], + "source": [ + "from chemaxon import mol_to_svg_formatter\n", + "df_mol_with_custom_columns = pd.DataFrame(data=load_molecules_for_pandas(file_name='mol_w_sgroups.mol', molecule_column=\"mols\", molecule_str_column=\"cxsmiles\"))\n", + "df_mol_with_custom_columns.to_html('web_view_cxsmiles_column.html', escape=False, formatters=dict(mols=mol_to_svg_formatter))" + ] + }, + { "cell_type": "markdown", - "source": "Since the __Molecule__ objects are being stored in the __DataFrame__, not just their representation, you can easily calculate properties for them and store the results in new columns.", - "id": "5cc167e89a64f2e6" + "id": "5cc167e89a64f2e6", + "metadata": {}, + "source": [ + "Since the __Molecule__ objects are being stored in the __DataFrame__, not just their representation, you can easily calculate properties for them and store the results in new columns." + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "d4877596d4d17c61", "metadata": { "ExecuteTime": { "end_time": "2025-12-01T14:44:25.428052Z", "start_time": "2025-12-01T14:44:25.414389Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "from chemaxon import logp\n", "\n", "df_mols['LogP'] = df_mols['molecule'].apply(lambda m: logp(m))\n", "df_mols" - ], - "id": "d4877596d4d17c61", - "outputs": [ - { - "data": { - "text/plain": [ - " molecule LogP\n", - "0 CC(=O)NC1=CC=C(O)C=C1 |c:9,t:4,6| 0.92\n", - "1 [Li]C1=C(I)C(Br)=C(F)C2=C1C(Cl)=C(N)C(O)=C2S |... 4.06" - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 4 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "You can also easily create new molecule columns based on existing columns, that contain molecules in any supported format.", - "id": "754df0780b6944f8" + "id": "754df0780b6944f8", + "metadata": {}, + "source": [ + "You can also easily create new molecule columns based on existing columns, that contain molecules in any supported format." + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "7cf77defedadb058", "metadata": { "ExecuteTime": { "end_time": "2025-12-02T12:54:58.077678Z", "start_time": "2025-12-02T12:54:58.057169Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "d = {'SMILES': ['CN1C=NC2=C1C(=O)N(C)C(=O)N2C'], 'name': ['coffein'] }\n", "df = pd.DataFrame(data=d)\n", @@ -214,87 +215,59 @@ "\n", "df['molecule'] = df['SMILES'].apply(lambda s: import_mol(s))\n", "df\n" - ], - "id": "7cf77defedadb058", - "outputs": [ - { - "data": { - "text/plain": [ - " SMILES name \\\n", - "0 CN1C=NC2=C1C(=O)N(C)C(=O)N2C coffein \n", - "0 CC[C@H](C)[C@@H]1NC(=O)[C@H](CC2=CC=C(O)C=C2)N... oxytocin \n", - "\n", - " molecule \n", - "0 CN1C=NC2=C1C(=O)N(C)C(=O)N2C |c:2,4| \n", - "0 CC[C@H](C)[C@@H]1NC(=O)[C@H](CC2=CC=C(O)C=C2)N... " - ], - "text/html": [ - "
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\n", - "
" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + ] + }, + { + "cell_type": "markdown", + "id": "7a1336fb96c1f03f", + "metadata": {}, + "source": [ + "You can also use molecule properties in __DataFrame__ objects:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61d10376399bc9a8", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-02T15:22:55.201768939Z", + "start_time": "2026-02-02T15:22:55.143300052Z" } - ], - "execution_count": 6 + }, + "outputs": [], + "source": [ + "from chemaxon import open_for_import\n", + "\n", + "with open_for_import('mol_with_properties.mrv') as mol_iterator:\n", + " mols = list(mol_iterator)\n", + "\n", + "d = {'molecule': mols }\n", + "df_props = pd.DataFrame(data=d)\n", + "df_props['string property'] = df_props['molecule'].apply(lambda m: m.get_property('test_str_property'))\n", + "df_props['int array property'] = df_props['molecule'].apply(lambda m: m.get_property('test_int_array_property'))\n", + "\n", + "df_props" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.12.3" } }, "nbformat": 4, diff --git a/jupyter/08_fingerprint_calculations.ipynb b/jupyter/08_fingerprint_calculations.ipynb new file mode 100644 index 0000000..48112cc --- /dev/null +++ b/jupyter/08_fingerprint_calculations.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "### **Fingerprint calculations**\n", + "id": "7d616c2b9d0bfbda" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Chemaxon has a number of functions that you can use to generate fingerprints.\n", + "id": "441988c36dd15dbd" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from chemaxon import cfp, ecfp\n", + "cfp = cfp(mol)\n", + "ecfp = ecfp(mol, 4, 1024)" + ], + "id": "796299eb324647db" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "These functions return `chemaxon.fingerprints.fingerprint.Fingerprint` objects. You can get the fingerprints\n", + "in bytes or in binary string format." + ], + "id": "9a8cdfb35a01c4a7" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "ecfp.to_bytes()\n", + "ecfp.to_binary_string()" + ], + "id": "f731dd656c472138" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can also calculate pharmacophore fingerprints:\n", + "id": "222cbcbc51b6ae4" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from chemaxon import pharmacophore_fp\n", + "pf = pharmacophore_fp(mol)" + ], + "id": "fba02898f19a990a" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "This method returns a `FloatVectorFingerprint`, which contains a float array.\n", + "\n", + "You can also calculate Tanimoto Dissimilarity for the fingerprints:" + ], + "id": "276808c169d1198e" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from chemaxon import tanimoto, ecfp, pharmacophore_fp, float_vector_tanimoto\n", + "ecfp1 = ecfp(mol, 4, 1024)\n", + "ecfp2 = ecfp(mol2, 4, 1024)\n", + "\n", + "result1 = tanimoto(ecfp1, ecfp2)\n", + "\n", + "pf1 = pharmacophore_fp(mol)\n", + "pf2 = pharmacophore_fp(mol2)\n", + "result2 = float_vector_tanimoto(pf1, pf2)" + ], + "id": "3f858051237a41e5" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Reaction fingerprints can also be claucluated using the `reaction_fp` function.", + "id": "defbff32f7c6f3c1" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from chemaxon import reaction_fp, import_mol\n", + "\n", + "r_str = \"[#7:1][H:2].[#6:4][S:3]([#7,#9,#17,#35,#53:7])(=[O:5])=[O:6]>>[#6:4][S:3]([#7:1])(=[O:6])=[O:5]\"\n", + "\n", + "reaction_mol = import_mol(reaction_str)\n", + "result_reaction = reaction_fp(reaction_mol)\n", + "\n", + "result_reaction" + ], + "id": "36c51f56f3f34840" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/09_reactor.ipynb b/jupyter/09_reactor.ipynb new file mode 100644 index 0000000..38ab588 --- /dev/null +++ b/jupyter/09_reactor.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c14515e93baf5370", + "metadata": {}, + "source": [ + "### **Reactor**" + ] + }, + { + "cell_type": "code", + "id": "c817e7513f5c6d3c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:28.068750Z", + "start_time": "2026-05-18T13:12:28.055630Z" + } + }, + "source": [ + "#Helper function to show the reactor results\n", + "\n", + "def display_result(result: list):\n", + " for r in range(len(result)):\n", + " print(r + 1, \". result\", sep=\"\")\n", + " for i in range(len(result[r].product_sets)):\n", + " print(\" \", i + 1, \". product set:\", sep=\"\")\n", + " for j in range(len(result[r].product_sets[i].products)):\n", + " print(\" \", j + 1, \". product\", sep=\"\")\n", + " display(result[r].product_sets[i].products[j])" + ], + "outputs": [], + "execution_count": 35 + }, + { + "metadata": { + "SqlCellData": { + "variableName$1": "df_sql1" + } + }, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "%%sql\n", + "id": "28cacdcac20b0577" + }, + { + "cell_type": "code", + "id": "eb1f9f42-ff7d-49dc-80a3-af87bb62f52d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:07:26.255453Z", + "start_time": "2026-05-18T13:07:26.163179Z" + } + }, + "source": [ + "from chemaxon import import_mol, export_mol, single_input, parallel, Mode, ReactorOptions, ResultType, ReactionRules\n", + "\n", + "reactants1 = [import_mol(\"CC(O)=O\"), import_mol(\"CCC(O)=O\")]\n", + "reactants2 = [import_mol(\"CO\"), import_mol(\"CCCCO\")]\n", + "reaction = import_mol(\"[H:5][O:4][CH:1]=[O:2].[H:7][O:6][CH3:8]>>[CH3:8][O:6][CH:1]=[O:2]\")\n", + "display(reaction)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Molecule(_source='[H:5][O:4][CH:1]=[O:2].[H:7][O:6][CH3:8]>>[CH3:8][O:6][CH:1]=[O:2]', _format='', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(9, 10))), atoms=(Atom(symbol='H', atom_number=1, mass_number=0, mapping=5, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=4, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=1, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=2, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='H', atom_number=1, mass_number=0, mapping=7, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=6, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=8, bonds=(Bond(type=1, atoms=(5, 6)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=8, bonds=(Bond(type=1, atoms=(7, 8)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=6, bonds=(Bond(type=1, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=1, bonds=(Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(9, 10)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=2, bonds=(Bond(type=2, atoms=(9, 10)),))), native_handle=197, _to_str=, properties={})" + ], + "image/svg+xml": "H5O41O2H7O6CH38CH38O61O2+\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 24 + }, + { + "cell_type": "code", + "id": "465167d7-198a-4717-b9ac-d30cb0c627e2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:31.421163Z", + "start_time": "2026-05-18T13:12:31.381652Z" + } + }, + "source": [ + "result = single_input(reaction, [reactants1[0], reactants2[0]])\n", + "display_result([result])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))))), native_handle=274, _to_str=, properties={})" + ], + "image/svg+xml": "CH3OH3CO\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 36 + }, + { + "cell_type": "code", + "id": "bc00c94a-a31a-4afa-b670-66a77a8c6e55", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:36.495551Z", + "start_time": "2026-05-18T13:12:36.303127Z" + } + }, + "source": [ + "result = parallel(Mode.SEQUENTIAL, reaction, [reactants1, reactants2])\n", + "display_result(result)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))))), native_handle=281, _to_str=, properties={})" + ], + "image/svg+xml": "CH3OH3CO\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(7, 8))))), native_handle=284, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 37 + }, + { + "cell_type": "code", + "id": "0f6bc873-2fd3-4a89-89f6-7c019f9cb4ab", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:39.565737Z", + "start_time": "2026-05-18T13:12:39.475707Z" + } + }, + "source": [ + "result = parallel(Mode.COMBINATORIAL, reaction, [reactants1, reactants2])\n", + "display_result(result)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 4)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 4)), Bond(type=1, atoms=(3, 4))))), native_handle=292, _to_str=, properties={})" + ], + "image/svg+xml": "CH3OH3CO\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 7)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 7)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 7)), Bond(type=1, atoms=(6, 7))))), native_handle=295, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 5)), Bond(type=1, atoms=(4, 5))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 5)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 5)), Bond(type=1, atoms=(4, 5))))), native_handle=298, _to_str=, properties={})" + ], + "image/svg+xml": "CH3OH3CO\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(7, 8))))), native_handle=301, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 38 + }, + { + "cell_type": "code", + "id": "afaee5f6-ff1d-445b-b1ba-5eabc8dee808", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:46.315266Z", + "start_time": "2026-05-18T13:12:46.211055Z" + } + }, + "source": [ + "all_reactants = [[reactants1[0], reactants1[1]], [reactants1[0], reactants2[1]]]\n", + "result = parallel(Mode.SEQUENTIAL, reaction, all_reactants)\n", + "print(\"Results without reaction rules\")\n", + "display_result(result)\n", + "\n", + "print(\"Results with reaction rules\")\n", + "rules = ReactionRules()\n", + "rules.reactivity = \"match(reactant(1), alcohol) or match(reactant(1), phenol)\"\n", + "result = parallel(Mode.SEQUENTIAL, reaction, all_reactants, rules=rules)\n", + "display_result(result)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results without reaction rules\n", + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 5)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=2, atoms=(4, 6))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 5)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(1, 2)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=2, atoms=(4, 6)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 5)), Bond(type=1, atoms=(4, 5)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(4, 6)),))), native_handle=307, _to_str=, properties={})" + ], + "image/svg+xml": "CH3OCH3OO\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(7, 8))))), native_handle=310, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results with reaction rules\n", + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=2, atoms=(2, 3)), Bond(type=1, atoms=(2, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 3)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 8)), Bond(type=1, atoms=(7, 8))))), native_handle=317, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 39 + }, + { + "cell_type": "code", + "id": "fe6b91db-2f30-4293-836e-708d320dedba", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:52.154959Z", + "start_time": "2026-05-18T13:12:52.077864Z" + } + }, + "source": [ + "options = ReactorOptions()\n", + "options.reverse = True\n", + "options.result_type = ResultType.REACTION\n", + "reverse_reactants = [[import_mol(\"COC(C)=O\"), import_mol(\"CCCCOC(C)=O\")]]\n", + "result = parallel(Mode.SEQUENTIAL, reaction, reverse_reactants, options=options)\n", + "display_result(result)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)), Bond(type=2, atoms=(2, 4)), Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(5, 7)), Bond(type=1, atoms=(5, 8)), Bond(type=1, atoms=(9, 10))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)), Bond(type=2, atoms=(2, 4)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 3)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(2, 4)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(5, 7)), Bond(type=1, atoms=(5, 8)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(5, 7)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 8)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(9, 10)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(9, 10)),))), native_handle=324, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3OCH3OHOCH3HO+\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(5, 7)), Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(8, 10)), Bond(type=1, atoms=(8, 11)), Bond(type=1, atoms=(12, 13)), Bond(type=1, atoms=(13, 14)), Bond(type=1, atoms=(14, 15)), Bond(type=1, atoms=(15, 16))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=1, atoms=(1, 2)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(1, 2)), Bond(type=1, atoms=(2, 3)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 3)), Bond(type=1, atoms=(3, 4)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=2, atoms=(5, 7)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(5, 7)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(8, 9)), Bond(type=2, atoms=(8, 10)), Bond(type=1, atoms=(8, 11)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(8, 9)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(8, 10)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(8, 11)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(12, 13)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(12, 13)), Bond(type=1, atoms=(13, 14)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(13, 14)), Bond(type=1, atoms=(14, 15)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(14, 15)), Bond(type=1, atoms=(15, 16)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(15, 16)),))), native_handle=326, _to_str=, properties={})" + ], + "image/svg+xml": "H3COCH3OCH3OHOCH3HO+\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 40 + }, + { + "cell_type": "code", + "id": "bcae6f33-85c4-410d-b5ce-dbe37d9cb77e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-18T13:12:57.487807Z", + "start_time": "2026-05-18T13:12:57.323528Z" + } + }, + "source": [ + "reaction = import_mol(\"[H:5][O:4][CH:1]=[O:2].[H:7][O:6][CH3:8]>>[CH3:8][O:6][CH:1]=[O:2]\")\n", + "all_reactant = [[import_mol(\"OC(=O)CCCCC(CCC(O)=O)CC(O)=O\")], [import_mol(\"CCO\")]]\n", + "options = ReactorOptions()\n", + "print(\"Ratio 1:1\")\n", + "options.ratio = [1, 1]\n", + "result = parallel(Mode.SEQUENTIAL, reaction, all_reactant, options=options)\n", + "display_result(result)\n", + "\n", + "print(\"Ratio 1:2\")\n", + "options.ratio = [1, 2]\n", + "result = parallel(Mode.SEQUENTIAL, reaction, all_reactant, options=options)\n", + "display_result(result)\n", + "\n", + "print(\"Ratio 1:3\")\n", + "options.ratio = [1, 3]\n", + "result = parallel(Mode.SEQUENTIAL, reaction, all_reactant, options=options)\n", + "display_result(result)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ratio 1:1\n", + "1. result\n", + " 1. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=2, atoms=(0, 1)), Bond(type=1, atoms=(0, 2)), Bond(type=1, atoms=(0, 18)), Bond(type=1, atoms=(2, 3)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(6, 12)), Bond(type=1, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)), Bond(type=1, atoms=(9, 10)), Bond(type=2, atoms=(9, 11)), Bond(type=1, atoms=(12, 13)), Bond(type=1, atoms=(13, 14)), Bond(type=2, atoms=(13, 15)), Bond(type=1, atoms=(16, 17)), Bond(type=1, atoms=(17, 18))), atoms=(Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(0, 1)), Bond(type=1, atoms=(0, 2)), Bond(type=1, atoms=(0, 18)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 2)), Bond(type=1, atoms=(2, 3)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(2, 3)), Bond(type=1, atoms=(3, 4)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(6, 12)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(7, 8)), Bond(type=1, atoms=(8, 9)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(8, 9)), Bond(type=1, atoms=(9, 10)), Bond(type=2, atoms=(9, 11)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(9, 10)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(9, 11)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(6, 12)), Bond(type=1, atoms=(12, 13)))), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(12, 13)), Bond(type=1, atoms=(13, 14)), Bond(type=2, atoms=(13, 15)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(13, 14)),)), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=2, atoms=(13, 15)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(16, 17)),)), Atom(symbol='C', atom_number=6, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(16, 17)), Bond(type=1, atoms=(17, 18)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 18)), Bond(type=1, atoms=(17, 18))))), native_handle=335, _to_str=, properties={})" + ], + "image/svg+xml": "OHOOHOOCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2. product set:\n", + " 1. product\n" + ] + }, + { + "data": { + "text/plain": [ + "Molecule(_source='\\n\\n', _format='mrv', _to_svg=, bonds=(Bond(type=1, atoms=(0, 1)), Bond(type=2, atoms=(1, 2)), Bond(type=1, atoms=(1, 3)), Bond(type=1, atoms=(3, 4)), Bond(type=1, atoms=(4, 5)), Bond(type=1, atoms=(5, 6)), Bond(type=1, atoms=(6, 7)), Bond(type=1, atoms=(7, 8)), Bond(type=1, atoms=(7, 12)), Bond(type=1, atoms=(8, 9)), Bond(type=1, atoms=(9, 10)), Bond(type=2, atoms=(10, 11)), Bond(type=1, atoms=(10, 18)), Bond(type=1, atoms=(12, 13)), Bond(type=1, atoms=(13, 14)), Bond(type=2, atoms=(13, 15)), Bond(type=1, atoms=(16, 17)), Bond(type=1, atoms=(17, 18))), atoms=(Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(0, 1)),)), Atom(symbol='C', atom_number=6, 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Bond(type=1, atoms=(21, 22)))), Atom(symbol='O', atom_number=8, mass_number=0, mapping=0, bonds=(Bond(type=1, atoms=(12, 22)), Bond(type=1, atoms=(21, 22))))), native_handle=351, _to_str=, properties={})" + ], + "image/svg+xml": "OOOCH3OH3COCH3O\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 41 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter/mol_with_properties.mrv b/jupyter/mol_with_properties.mrv new file mode 100644 index 0000000..55439a2 --- /dev/null +++ b/jupyter/mol_with_properties.mrv @@ -0,0 +1,4 @@ + + +TRUEasd424.21 2 3 4 51.2 3.4 5.22 6.7889asd1 2 3 4 542TRUE1.5 2.7 3.3 4.567 + \ No newline at end of file diff --git a/jupyter/nci50.smiles b/jupyter/nci50.smiles new file mode 100644 index 0000000..471085f --- /dev/null +++ b/jupyter/nci50.smiles @@ -0,0 +1,50 @@ +CC1=CC(=O)C=CC1=O 1 +S(SC1=NC2=CC=CC=C2S1)C3=NC4=C(S3)C=CC=C4 2 +OC1=C(Cl)C=C(C=C1[N+]([O-])=O)[N+]([O-])=O 3 +[O-][N+](=O)C1=CNC(=N)S1 4 +NC1=CC2=C(C=C1)C(=O)C3=C(C=CC=C3)C2=O 5 +OC(=O)C1=C(C=CC=C1)C2=C3C=CC(=O)C(=C3OC4=C2C=CC(=C4Br)O)Br 6 +CN(C)C1=C(Cl)C(=O)C2=C(C=CC=C2)C1=O 7 +CC1=C(C2=C(C=C1)C(=O)C3=CC=CC=C3C2=O)[N+]([O-])=O 8 +CC(=NO)C(C)=NO 9 +C1=CC=C(C=C1)P(C2=CC=CC=C2)C3=CC=CC=C3 10 +CC(C)(C)C1=C(O)C=C(C(=C1)O)C(C)(C)C 11 +CC1=NN(C(=O)C1)C2=CC=CC=C2 12 +NC1=CC=NC2=C1C=CC(=C2)Cl 13 +CCCCCC[CH]1CCCCN1 14 +O=CC1=C2C=CC=CC2=CC3=C1C=CC=C3 15 +BrN1C(=O)CCC1=O 16 +CCCCCCCCCCCCCCCC1=C(N)C=CC(=C1)O 17 +C(COC1=C(C=CC=C1)C2=CC=CC=C2)OC3=CC=CC=C3C4=CC=CC=C4 18 +CCCCSCC 19 +CC(=O)NC1=NC2=C(C=C1)C(=CC=N2)O 20 +CC1=C2C=CC(=NC2=NC(=C1)O)N 21 +CCOC(=O)C1=CN=C2N=C(N)C=CC2=C1O 22 +CC1=CC(=NC=C1)N=CC2=CC=CC=C2 23 +C[N+](C)(C)CC1=CC=CC=C1 24 +C[N+](C)(C)C(=O)C1=CC=CC=C1 25 +ICCC(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=CC=C3 26 +CC1=CC(=C(C[N+](C)(C)C)C(=C1)C)C 27 +C[C](O)(CC(O)=O)C1=CC=C(C=C1)[N+]([O-])=O 28 +CC1=CC=C(C=C1)C(=O)C2=CC=C(Cl)C=C2 29 +ON=CC1=CC=C(O)C=C1 30 +CC1=CC(=C(N)C(=C1)C)C 31 +CC1=CC=C(C=C1)C(=O)C2=CC=C(C=C2)[N+]([O-])=O 32 +CC(O)(C1=CC=CC=C1)C2=CC=CC=C2 33 +ON=CC1=CC(=CC=C1)[N+]([O-])=O 34 +OC1=C2C=CC(=CC2=NC=C1[N+]([O-])=O)Cl 35 +CC1=CC=CC2=NC=C(C)C(=C12)Cl 36 +CCC(CC)([CH](OC(N)=O)C1=CC=CC=C1)C2=CC=CC=C2 37 +ON=C(CC1=CC=CC=C1)[CH](C#N)C2=CC=CC=C2 38 +O[CH](CC1=CC=CC=C1)C2=CC=CC=C2 39 +COC1=CC=C(CC2=CC=C(OC)C=C2)C=C1 40 +CN(C)[CH](C1=CC=CC=C1)C2=C(C)C=CC=C2 41 +COC1=CC(=C(N)C(=C1)[N+]([O-])=O)[N+]([O-])=O 42 +NN=C(C1=CC=CC=C1)C2=CC=CC=C2 43 +COC1=CC=C(C=C1)C=NO 44 +C1=CC=C(C=C1)C(N=C(C2=CC=CC=C2)C3=CC=CC=C3)C4=CC=CC=C4 45 +C1=CC=C(C=C1)N=C(C2=CC=CC=C2)C3=CC=CC=C3 46 +CC1=C(C2=CC=CC=C2)C(=C3C=CC=CC3=N1)O 47 +CCC1=[O+][Cu]2([O+]=C(CC)C1)[O+]=C(CC)CC(=[O+]2)CC 48 +OC(=O)[CH](CC1=CC=CC=C1)C2=CC=CC=C2 49 +CCC1=C(N)C=C(C)N=C1 50