├── ChEMBL_API_example_for_webinar.ipynb └── ChEMBL_webresource_client_examples.ipynb /ChEMBL_API_example_for_webinar.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Using a list of Schistosome parasite genes, find compounds that are active on these targets:" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "* Aim: Find novel therapeutic targets for the parasite Schistosoma mansoni. \n", 15 | "* Background: Schistosome parasites kill 250,000 people every year. Treatment of schistosomiasis relies on the drug praziquantel (only!). \n", 16 | "* But what other targets could be druggable? And are there any existing marketed drugs for these targets? \n", 17 | "* For further detail see comment at https://www.science.org/doi/10.1126/science.abe0710 \n", 18 | "* and scientific paper: https://www.science.org/doi/10.1126/science.abb7699 " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "## Method 1: Use the ChEMBL python client to access the API" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "* Available to install from Python Package Index by typing: \n", 40 | "pip install chembl_webresource_client \n", 41 | "* See https://github.com/chembl/chembl_webresource_client \n", 42 | " \n", 43 | "* Pros: simple to use, tailored for ChEMBL API endpoints\n", 44 | "* Cons: The keyword 'only' is limited to main fields, so it can't search within nested fields (e.g. in the molecule API, .only(['molecule_properties__alogp']) is equivalent to .only(['molecule_properties']). " 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 17, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "###############################\n", 54 | "#This cell imports relevant python modules:\n", 55 | "###############################\n", 56 | "import pandas as pd #Use pandas python module to view and analyse data\n", 57 | "from chembl_webresource_client.new_client import new_client #Import ChEMBL python client" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "## 1a: Find compounds that are active on the specified targets: " 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 20, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "name": "stdout", 74 | "output_type": "stream", 75 | "text": [ 76 | "(83, 7)\n" 77 | ] 78 | }, 79 | { 80 | "data": { 81 | "text/html": [ 82 | "
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molecule_chembl_idmolecule_pref_nameparent_molecule_chembl_idpchembl_valuetarget_chembl_idtarget_pref_namevalue
0CHEMBL574738AST-487CHEMBL5747385.92CHEMBL5552Serine/threonine-protein kinase 251200.0
1CHEMBL522892DOVITINIBCHEMBL5228925.92CHEMBL5552Serine/threonine-protein kinase 251200.0
2CHEMBL607707PELITINIBCHEMBL6077075.47CHEMBL5552Serine/threonine-protein kinase 253400.0
3CHEMBL191003JNJ-7706621CHEMBL1910036.44CHEMBL5552Serine/threonine-protein kinase 25360.0
4CHEMBL608533MIDOSTAURINCHEMBL6085335.75CHEMBL5552Serine/threonine-protein kinase 251800.0
........................
78CHEMBL388978STAUROSPORINECHEMBL3889788.57CHEMBL5552Serine/threonine-protein kinase 252.71
79CHEMBL388978STAUROSPORINECHEMBL3889787.93CHEMBL1075195Serine/threonine-protein kinase TAO21.17
80CHEMBL388978STAUROSPORINECHEMBL3889788.72CHEMBL5552Serine/threonine-protein kinase 251.9
81CHEMBL4569508NoneCHEMBL45695085.57CHEMBL1075195Serine/threonine-protein kinase TAO22700.0
82CHEMBL4568087NoneCHEMBL45680876.38CHEMBL1075195Serine/threonine-protein kinase TAO2420.0
\n", 222 | "

83 rows × 7 columns

\n", 223 | "
" 224 | ], 225 | "text/plain": [ 226 | " molecule_chembl_id molecule_pref_name parent_molecule_chembl_id \\\n", 227 | "0 CHEMBL574738 AST-487 CHEMBL574738 \n", 228 | "1 CHEMBL522892 DOVITINIB CHEMBL522892 \n", 229 | "2 CHEMBL607707 PELITINIB CHEMBL607707 \n", 230 | "3 CHEMBL191003 JNJ-7706621 CHEMBL191003 \n", 231 | "4 CHEMBL608533 MIDOSTAURIN CHEMBL608533 \n", 232 | ".. ... ... ... \n", 233 | "78 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 234 | "79 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 235 | "80 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 236 | "81 CHEMBL4569508 None CHEMBL4569508 \n", 237 | "82 CHEMBL4568087 None CHEMBL4568087 \n", 238 | "\n", 239 | " pchembl_value target_chembl_id target_pref_name \\\n", 240 | "0 5.92 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 241 | "1 5.92 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 242 | "2 5.47 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 243 | "3 6.44 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 244 | "4 5.75 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 245 | ".. ... ... ... \n", 246 | "78 8.57 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 247 | "79 7.93 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 248 | "80 8.72 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 249 | "81 5.57 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 250 | "82 6.38 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 251 | "\n", 252 | " value \n", 253 | "0 1200.0 \n", 254 | "1 1200.0 \n", 255 | "2 3400.0 \n", 256 | "3 360.0 \n", 257 | "4 1800.0 \n", 258 | ".. ... \n", 259 | "78 2.71 \n", 260 | "79 1.17 \n", 261 | "80 1.9 \n", 262 | "81 2700.0 \n", 263 | "82 420.0 \n", 264 | "\n", 265 | "[83 rows x 7 columns]" 266 | ] 267 | }, 268 | "execution_count": 20, 269 | "metadata": {}, 270 | "output_type": "execute_result" 271 | } 272 | ], 273 | "source": [ 274 | "###############################\n", 275 | "#Set up the call to the ChEMBL 'activity' API:\n", 276 | "###############################\n", 277 | "activities = new_client.activity.filter(target_chembl_id__in=['CHEMBL5552','CHEMBL1075195'] ##Specify a list of example targets (#Serine/threonine-protein kinase 25 (STK25) & Serine-threonine kinases TAO2)\n", 278 | " , pchembl_value__gte=5 ##Specify a minimum threshold of the pChEMBL activity value. Note that pCHEMBL = -log10(IC50, XC50, AC50, Ki, Kd, potency). Greater than or equal to 5 (10um) is a typical minimum rule of thumb for binding activity between a compound and a protein target. \n", 279 | " , assay_type='B' ##Only look for Binding Assays\n", 280 | " ).only(['target_chembl_id', 'target_pref_name', 'parent_molecule_chembl_id' ## Specify which fields (columns) to extract\n", 281 | " ,'molecule_chembl_id','molecule_pref_name', 'pchembl_value'])\n", 282 | "#Convert the list of results into a Pandas dataframe:\n", 283 | "act_df = pd.DataFrame(activities)\n", 284 | "act_df" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": null, 290 | "metadata": {}, 291 | "outputs": [], 292 | "source": [] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "## 1b: Using the active compounds from the previous step, and call the 'molecule' API to find their molecular properties etc, so that the compound list can be prioritised" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": 3, 304 | "metadata": {}, 305 | "outputs": [ 306 | { 307 | "name": "stdout", 308 | "output_type": "stream", 309 | "text": [ 310 | "There are 55 compounds initially identified as active on the known targets. e.g.['CHEMBL4568087', 'CHEMBL1721885']...\n" 311 | ] 312 | } 313 | ], 314 | "source": [ 315 | "###############################\n", 316 | "#First find the list of compounds that are within the act_df dataframe:\n", 317 | "###############################\n", 318 | "cmpd_chembl_ids = list(set(act_df['molecule_chembl_id']))\n", 319 | "print(\"There are {} compounds initially identified as active on the known targets. e.g.{}...\".format(len(cmpd_chembl_ids),cmpd_chembl_ids[0:2]))" 320 | ] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "execution_count": 5, 325 | "metadata": {}, 326 | "outputs": [ 327 | { 328 | "data": { 329 | "text/html": [ 330 | "
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max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_name
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIB
11CHEMBL296468{'molecule_chembl_id': 'CHEMBL296468', 'parent...{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...BMS-387032
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIB
32CHEMBL475251{'molecule_chembl_id': 'CHEMBL475251', 'parent...{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...R-406
\n", 390 | "
" 391 | ], 392 | "text/plain": [ 393 | " max_phase molecule_chembl_id \\\n", 394 | "0 4 CHEMBL535 \n", 395 | "1 1 CHEMBL296468 \n", 396 | "2 4 CHEMBL180022 \n", 397 | "3 2 CHEMBL475251 \n", 398 | "\n", 399 | " molecule_hierarchy \\\n", 400 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 401 | "1 {'molecule_chembl_id': 'CHEMBL296468', 'parent... \n", 402 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 403 | "3 {'molecule_chembl_id': 'CHEMBL475251', 'parent... \n", 404 | "\n", 405 | " molecule_properties pref_name \n", 406 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 407 | "1 {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log... BMS-387032 \n", 408 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 409 | "3 {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log... R-406 " 410 | ] 411 | }, 412 | "execution_count": 5, 413 | "metadata": {}, 414 | "output_type": "execute_result" 415 | } 416 | ], 417 | "source": [ 418 | "###############################\n", 419 | "#Set up the call to the ChEMBL 'molecule' API:\n", 420 | "###############################\n", 421 | "\n", 422 | "#Select a few examples of the active compounds from above (i.e. a reduced list!) : \n", 423 | "cmpd_chembl_ids = \",\".join(['CHEMBL296468', 'CHEMBL180022', 'CHEMBL475251','CHEMBL535']) # Amend the format of the text string so that it is suitable for the API call\n", 424 | "\n", 425 | "molecules = new_client.molecule.filter(molecule_chembl_id__in=cmpd_chembl_ids ##Select a few examples of the active compounds (i.e. a reduced list!)\n", 426 | " ).only([ 'molecule_chembl_id','pref_name', 'molecule_hierarchy', 'molecule_properties', 'max_phase']) ## Specify which fields (columns) to extract\n", 427 | "\n", 428 | "#Convert the list of results into a Pandas dataframe:\n", 429 | "mol_df = pd.DataFrame(molecules)\n", 430 | "mol_df" 431 | ] 432 | }, 433 | { 434 | "cell_type": "code", 435 | "execution_count": 6, 436 | "metadata": {}, 437 | "outputs": [ 438 | { 439 | "name": "stdout", 440 | "output_type": "stream", 441 | "text": [ 442 | "(4, 18)\n" 443 | ] 444 | }, 445 | { 446 | "data": { 447 | "text/html": [ 448 | "
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max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_nameparent_molecule_chembl_idcx_logdcx_logpcx_most_apkacx_most_bpkaalogphbahbdmw_freebasefull_mwtnum_ro5_violationspsaheavy_atoms
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIBCHEMBL5351.282.9311.469.043.3333398.48398.48077.2329
11CHEMBL296468{'molecule_chembl_id': 'CHEMBL296468', 'parent...{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...BMS-387032CHEMBL2964680.330.947.9410.173.6672380.54380.54080.0525
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIBCHEMBL1800223.054.4712.558.815.9382557.05557.052112.4040
32CHEMBL475251{'molecule_chembl_id': 'CHEMBL475251', 'parent...{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...R-406CHEMBL4752513.633.6310.903.053.63103470.46470.460128.7534
\n", 573 | "
" 574 | ], 575 | "text/plain": [ 576 | " max_phase molecule_chembl_id \\\n", 577 | "0 4 CHEMBL535 \n", 578 | "1 1 CHEMBL296468 \n", 579 | "2 4 CHEMBL180022 \n", 580 | "3 2 CHEMBL475251 \n", 581 | "\n", 582 | " molecule_hierarchy \\\n", 583 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 584 | "1 {'molecule_chembl_id': 'CHEMBL296468', 'parent... \n", 585 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 586 | "3 {'molecule_chembl_id': 'CHEMBL475251', 'parent... \n", 587 | "\n", 588 | " molecule_properties pref_name \\\n", 589 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 590 | "1 {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log... BMS-387032 \n", 591 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 592 | "3 {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log... R-406 \n", 593 | "\n", 594 | " parent_molecule_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp \\\n", 595 | "0 CHEMBL535 1.28 2.93 11.46 9.04 3.33 \n", 596 | "1 CHEMBL296468 0.33 0.94 7.94 10.17 3.66 \n", 597 | "2 CHEMBL180022 3.05 4.47 12.55 8.81 5.93 \n", 598 | "3 CHEMBL475251 3.63 3.63 10.90 3.05 3.63 \n", 599 | "\n", 600 | " hba hbd mw_freebase full_mwt num_ro5_violations psa heavy_atoms \n", 601 | "0 3 3 398.48 398.48 0 77.23 29 \n", 602 | "1 7 2 380.54 380.54 0 80.05 25 \n", 603 | "2 8 2 557.05 557.05 2 112.40 40 \n", 604 | "3 10 3 470.46 470.46 0 128.75 34 " 605 | ] 606 | }, 607 | "execution_count": 6, 608 | "metadata": {}, 609 | "output_type": "execute_result" 610 | } 611 | ], 612 | "source": [ 613 | "###########################\n", 614 | "# Convert nested cells (ie those containing a dictionary) to individual columns in the dataframe so that is it easier to filter!\n", 615 | "###########################\n", 616 | "# Molecule hierarchy: \n", 617 | "mol_df['parent_molecule_chembl_id'] = mol_df['molecule_hierarchy'].apply(lambda x: x['parent_chembl_id'])\n", 618 | "\n", 619 | "#Physicochemical properties (only report if cells are not null)\n", 620 | "mol_df['cx_logd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logd'])\n", 621 | "mol_df['cx_logp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logp'])\n", 622 | "mol_df['cx_most_apka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_apka'])\n", 623 | "mol_df['cx_most_bpka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_bpka'])\n", 624 | "mol_df['alogp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['alogp'])\n", 625 | "mol_df['hba'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hba'])\n", 626 | "mol_df['hbd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hbd'])\n", 627 | "mol_df['mw_freebase'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase']) #This is the mwt of the parent compound\n", 628 | "mol_df['full_mwt'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['full_mwt']) #This is the mwt of the full compound including any salt\n", 629 | "mol_df['num_ro5_violations'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['num_ro5_violations'])\n", 630 | "mol_df['psa'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['psa'])\n", 631 | "mol_df['heavy_atoms'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['heavy_atoms'])\n", 632 | "\n", 633 | "mol_df" 634 | ] 635 | }, 636 | { 637 | "cell_type": "code", 638 | "execution_count": 8, 639 | "metadata": {}, 640 | "outputs": [ 641 | { 642 | "data": { 643 | "text/html": [ 644 | "
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max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_nameparent_molecule_chembl_idcx_logdcx_logpcx_most_apkacx_most_bpkaalogphbahbdmw_freebasefull_mwtnum_ro5_violationspsaheavy_atoms
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIBCHEMBL5351.282.9311.469.043.3333398.48398.48077.2329
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIBCHEMBL1800223.054.4712.558.815.9382557.05557.052112.4040
\n", 727 | "
" 728 | ], 729 | "text/plain": [ 730 | " max_phase molecule_chembl_id \\\n", 731 | "0 4 CHEMBL535 \n", 732 | "2 4 CHEMBL180022 \n", 733 | "\n", 734 | " molecule_hierarchy \\\n", 735 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 736 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 737 | "\n", 738 | " molecule_properties pref_name \\\n", 739 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 740 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 741 | "\n", 742 | " parent_molecule_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp \\\n", 743 | "0 CHEMBL535 1.28 2.93 11.46 9.04 3.33 \n", 744 | "2 CHEMBL180022 3.05 4.47 12.55 8.81 5.93 \n", 745 | "\n", 746 | " hba hbd mw_freebase full_mwt num_ro5_violations psa heavy_atoms \n", 747 | "0 3 3 398.48 398.48 0 77.23 29 \n", 748 | "2 8 2 557.05 557.05 2 112.40 40 " 749 | ] 750 | }, 751 | "execution_count": 8, 752 | "metadata": {}, 753 | "output_type": "execute_result" 754 | } 755 | ], 756 | "source": [ 757 | "###########################\n", 758 | "#Filter the compounds based on their molecular properties (e.g. molecular weight < 400 amu), or max_phase, for example:\n", 759 | "###########################\n", 760 | "\n", 761 | "#Now keep only compounds with max_phase = 4 (ie approved drugs): \n", 762 | "res = mol_df[ mol_df['max_phase'] == 4 ]\n", 763 | "\n", 764 | "#Display results:\n", 765 | "res" 766 | ] 767 | }, 768 | { 769 | "cell_type": "code", 770 | "execution_count": null, 771 | "metadata": {}, 772 | "outputs": [], 773 | "source": [] 774 | }, 775 | { 776 | "cell_type": "markdown", 777 | "metadata": {}, 778 | "source": [ 779 | "## Method 2: Use the python 'requests' module to access the API" 780 | ] 781 | }, 782 | { 783 | "cell_type": "markdown", 784 | "metadata": {}, 785 | "source": [ 786 | "* Pros: Use if don't want to install ChEMBL python client, e.g. if 'requests' module already in use as part of an existing workflow, or using a non-python coding language\n", 787 | "* Cons: Slightly more code required than for ChEMBL python client. Need to include code to iterate over multiple pages of records.\n", 788 | "* See https://docs.python-requests.org/en/latest/ " 789 | ] 790 | }, 791 | { 792 | "cell_type": "code", 793 | "execution_count": 9, 794 | "metadata": {}, 795 | "outputs": [], 796 | "source": [ 797 | "###############################\n", 798 | "#This cell imports relevant python modules:\n", 799 | "###############################\n", 800 | "import pandas as pd #Use pandas python module to view and analyse data\n", 801 | "import requests #This is used to access json files!" 802 | ] 803 | }, 804 | { 805 | "cell_type": "markdown", 806 | "metadata": {}, 807 | "source": [ 808 | "## 2a: Find compounds that are active on the specified targets:" 809 | ] 810 | }, 811 | { 812 | "cell_type": "code", 813 | "execution_count": 10, 814 | "metadata": {}, 815 | "outputs": [ 816 | { 817 | "name": "stdout", 818 | "output_type": "stream", 819 | "text": [ 820 | "This is the url string that calls the 'Activities' API with the initial query specification:\n", 821 | "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id__in=CHEMBL5552,CHEMBL1075195&pchembl_value__gte=5&assay_type=B&only=target_chembl_id,target_pref_name,parent_molecule_chembl_id,molecule_chembl_id,molecule_pref_name,pchembl_value&format=json\n" 822 | ] 823 | }, 824 | { 825 | "data": { 826 | "text/html": [ 827 | "
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molecule_chembl_idmolecule_pref_nameparent_molecule_chembl_idpchembl_valuetarget_chembl_idtarget_pref_namevalue
0CHEMBL574738AST-487CHEMBL5747385.92CHEMBL5552Serine/threonine-protein kinase 251200.0
1CHEMBL522892DOVITINIBCHEMBL5228925.92CHEMBL5552Serine/threonine-protein kinase 251200.0
2CHEMBL607707PELITINIBCHEMBL6077075.47CHEMBL5552Serine/threonine-protein kinase 253400.0
3CHEMBL191003JNJ-7706621CHEMBL1910036.44CHEMBL5552Serine/threonine-protein kinase 25360.0
4CHEMBL608533MIDOSTAURINCHEMBL6085335.75CHEMBL5552Serine/threonine-protein kinase 251800.0
........................
78CHEMBL388978STAUROSPORINECHEMBL3889788.57CHEMBL5552Serine/threonine-protein kinase 252.71
79CHEMBL388978STAUROSPORINECHEMBL3889787.93CHEMBL1075195Serine/threonine-protein kinase TAO21.17
80CHEMBL388978STAUROSPORINECHEMBL3889788.72CHEMBL5552Serine/threonine-protein kinase 251.9
81CHEMBL4569508NoneCHEMBL45695085.57CHEMBL1075195Serine/threonine-protein kinase TAO22700.0
82CHEMBL4568087NoneCHEMBL45680876.38CHEMBL1075195Serine/threonine-protein kinase TAO2420.0
\n", 967 | "

83 rows × 7 columns

\n", 968 | "
" 969 | ], 970 | "text/plain": [ 971 | " molecule_chembl_id molecule_pref_name parent_molecule_chembl_id \\\n", 972 | "0 CHEMBL574738 AST-487 CHEMBL574738 \n", 973 | "1 CHEMBL522892 DOVITINIB CHEMBL522892 \n", 974 | "2 CHEMBL607707 PELITINIB CHEMBL607707 \n", 975 | "3 CHEMBL191003 JNJ-7706621 CHEMBL191003 \n", 976 | "4 CHEMBL608533 MIDOSTAURIN CHEMBL608533 \n", 977 | ".. ... ... ... \n", 978 | "78 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 979 | "79 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 980 | "80 CHEMBL388978 STAUROSPORINE CHEMBL388978 \n", 981 | "81 CHEMBL4569508 None CHEMBL4569508 \n", 982 | "82 CHEMBL4568087 None CHEMBL4568087 \n", 983 | "\n", 984 | " pchembl_value target_chembl_id target_pref_name \\\n", 985 | "0 5.92 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 986 | "1 5.92 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 987 | "2 5.47 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 988 | "3 6.44 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 989 | "4 5.75 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 990 | ".. ... ... ... \n", 991 | "78 8.57 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 992 | "79 7.93 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 993 | "80 8.72 CHEMBL5552 Serine/threonine-protein kinase 25 \n", 994 | "81 5.57 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 995 | "82 6.38 CHEMBL1075195 Serine/threonine-protein kinase TAO2 \n", 996 | "\n", 997 | " value \n", 998 | "0 1200.0 \n", 999 | "1 1200.0 \n", 1000 | "2 3400.0 \n", 1001 | "3 360.0 \n", 1002 | "4 1800.0 \n", 1003 | ".. ... \n", 1004 | "78 2.71 \n", 1005 | "79 1.17 \n", 1006 | "80 1.9 \n", 1007 | "81 2700.0 \n", 1008 | "82 420.0 \n", 1009 | "\n", 1010 | "[83 rows x 7 columns]" 1011 | ] 1012 | }, 1013 | "execution_count": 10, 1014 | "metadata": {}, 1015 | "output_type": "execute_result" 1016 | } 1017 | ], 1018 | "source": [ 1019 | "###############################\n", 1020 | "#Search for activity for a list of targets:\n", 1021 | "###############################\n", 1022 | "\n", 1023 | "#Specify the input parameters: \n", 1024 | "targets = ['CHEMBL5552', 'CHEMBL1075195'] # Select a few example targets: Serine/threonine-protein kinase 25 (STK25) & Serine-threonine kinases TAO2.\n", 1025 | "targets = \",\".join(targets) #Join the targets into a suitable string to fulfil the search conditions of the API\n", 1026 | "pchembl_value = 5 #Specify a minimum threshold of the pChEMBL activity value. Note that pCHEMBL = -log10(IC50, XC50, AC50, Ki, Kd, potency). Greater than or equal to 5 (10um) is a typical minimum rule of thumb for binding activity between a compound and a protein target. \n", 1027 | "assay_type = 'B' #Only look for Binding Assays\n", 1028 | "cols = \",\".join(['target_chembl_id', 'target_pref_name', 'parent_molecule_chembl_id','molecule_chembl_id','molecule_pref_name', 'pchembl_value']) # Only return the specified data fields \n", 1029 | "\n", 1030 | "###############################\n", 1031 | "url_stem = \"https://www.ebi.ac.uk\" #This is the stem of the url\n", 1032 | "url_string = url_stem + \"/chembl/api/data/activity?target_chembl_id__in={}&pchembl_value__gte={}&assay_type={}&only={}&format=json\".format(targets, pchembl_value, assay_type, cols) #This is the full url with the specified input parameters\n", 1033 | "url = requests.get( url_string ).json() #This calls the information back from the API using the 'requests' module, and converts it to json format\n", 1034 | "results = url['activities'] #This is a list of the results for activities\n", 1035 | "\n", 1036 | "#This 'while' loop iterates over several pages of records (if required), and collates the list of results\n", 1037 | "#Remember that there is a limit to the number of records returned in any one API call (default is 20 records, maximum is 1000 records) e.g. include \"&limit=50\" in the url string\n", 1038 | "#So need to iterate over several pages of records to gather all relevant information together!\n", 1039 | "while url['page_meta']['next']:\n", 1040 | " url = requests.get(url_stem + url['page_meta']['next']).json()\n", 1041 | " results = results + url['activities'] #Add result (as a list) to previous list of results\n", 1042 | "\n", 1043 | "#Convert the list of results into a Pandas dataframe:\n", 1044 | "act_df = pd.DataFrame(results)\n", 1045 | "\n", 1046 | "#Print out some useful information:\n", 1047 | "print(\"This is the url string that calls the 'Activities' API with the initial query specification:\\n{}\".format(url_string) )\n", 1048 | "act_df" 1049 | ] 1050 | }, 1051 | { 1052 | "cell_type": "code", 1053 | "execution_count": null, 1054 | "metadata": {}, 1055 | "outputs": [], 1056 | "source": [] 1057 | }, 1058 | { 1059 | "cell_type": "markdown", 1060 | "metadata": {}, 1061 | "source": [ 1062 | "## 2b: Using the active compounds from the previous step, and call the 'molecule' API to find their molecular properties etc, so that the compound list can be prioritised" 1063 | ] 1064 | }, 1065 | { 1066 | "cell_type": "code", 1067 | "execution_count": 12, 1068 | "metadata": {}, 1069 | "outputs": [ 1070 | { 1071 | "name": "stdout", 1072 | "output_type": "stream", 1073 | "text": [ 1074 | "There are 55 compounds initially identified as active on the known targets. e.g.['CHEMBL4568087', 'CHEMBL1721885']...\n" 1075 | ] 1076 | } 1077 | ], 1078 | "source": [ 1079 | "###############################\n", 1080 | "#First find the list of compounds that are within the act_df dataframe:\n", 1081 | "###############################\n", 1082 | "cmpd_chembl_ids = list(set(act_df['molecule_chembl_id']))\n", 1083 | "print(\"There are {} compounds initially identified as active on the known targets. e.g.{}...\".format(len(cmpd_chembl_ids),cmpd_chembl_ids[0:2]))" 1084 | ] 1085 | }, 1086 | { 1087 | "cell_type": "code", 1088 | "execution_count": 13, 1089 | "metadata": {}, 1090 | "outputs": [ 1091 | { 1092 | "name": "stdout", 1093 | "output_type": "stream", 1094 | "text": [ 1095 | "This is the url string that calls the 'Molecule' API with the specified query\n", 1096 | "https://www.ebi.ac.uk/chembl/api/data/molecule?molecule_chembl_id__in=CHEMBL296468,CHEMBL180022,CHEMBL475251,CHEMBL535&only=molecule_chembl_id,pref_name,molecule_hierarchy,molecule_properties,max_phase&format=json\n" 1097 | ] 1098 | }, 1099 | { 1100 | "data": { 1101 | "text/html": [ 1102 | "
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max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_name
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIB
11CHEMBL296468{'molecule_chembl_id': 'CHEMBL296468', 'parent...{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...BMS-387032
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIB
32CHEMBL475251{'molecule_chembl_id': 'CHEMBL475251', 'parent...{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...R-406
\n", 1162 | "
" 1163 | ], 1164 | "text/plain": [ 1165 | " max_phase molecule_chembl_id \\\n", 1166 | "0 4 CHEMBL535 \n", 1167 | "1 1 CHEMBL296468 \n", 1168 | "2 4 CHEMBL180022 \n", 1169 | "3 2 CHEMBL475251 \n", 1170 | "\n", 1171 | " molecule_hierarchy \\\n", 1172 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 1173 | "1 {'molecule_chembl_id': 'CHEMBL296468', 'parent... \n", 1174 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 1175 | "3 {'molecule_chembl_id': 'CHEMBL475251', 'parent... \n", 1176 | "\n", 1177 | " molecule_properties pref_name \n", 1178 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 1179 | "1 {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log... BMS-387032 \n", 1180 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 1181 | "3 {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log... R-406 " 1182 | ] 1183 | }, 1184 | "execution_count": 13, 1185 | "metadata": {}, 1186 | "output_type": "execute_result" 1187 | } 1188 | ], 1189 | "source": [ 1190 | "###############################\n", 1191 | "#For the identified compounds, extract their molecular properties and other information from the 'molecule' ChEMBL API\n", 1192 | "###############################\n", 1193 | "\n", 1194 | "#Select a few examples of the active compounds from above (i.e. a reduced list!) : \n", 1195 | "cmpd_chembl_ids = \",\".join(['CHEMBL296468', 'CHEMBL180022', 'CHEMBL475251','CHEMBL535']) #Amend the format of the text string so that it is suitable for the API call\n", 1196 | "cols = \",\".join([ 'molecule_chembl_id','pref_name', 'molecule_hierarchy', 'molecule_properties', 'max_phase']) # Only return the specified data fields \n", 1197 | "\n", 1198 | "###############################\n", 1199 | "url_stem = \"https://www.ebi.ac.uk\" #This is the stem of the url\n", 1200 | "url_string = url_stem + \"/chembl/api/data/molecule?molecule_chembl_id__in={}&only={}&format=json\".format(cmpd_chembl_ids,cols) #This is the full url with the specified input parameters\n", 1201 | "url = requests.get( url_string ).json() #This calls the information back from the API using the 'requests' module, and converts it to json format\n", 1202 | "results = url['molecules'] #This is a list of the results for activities\n", 1203 | "\n", 1204 | "#This 'while' loop iterates over several pages of records (if required), and collates the list of results\n", 1205 | "#Remember that there is a limit to the number of records returned in any one API call (default is 20 records, maximum is 1000 records) e.g. include \"&limit=50\" in the url string\n", 1206 | "#So need to iterate over several pages of records to gather all relevant information together!\n", 1207 | "while url['page_meta']['next']:\n", 1208 | " url = requests.get(url_stem + url['page_meta']['next']).json()\n", 1209 | " results = results + url['molecules'] #Add result (as a list) to previous list of results\n", 1210 | "\n", 1211 | "#Convert the list of results into a Pandas dataframe:\n", 1212 | "mol_df = pd.DataFrame(results)\n", 1213 | "\n", 1214 | "#Print out some useful information:\n", 1215 | "print(\"This is the url string that calls the 'Molecule' API with the specified query\\n{}\".format(url_string) )\n", 1216 | "mol_df" 1217 | ] 1218 | }, 1219 | { 1220 | "cell_type": "code", 1221 | "execution_count": 14, 1222 | "metadata": {}, 1223 | "outputs": [ 1224 | { 1225 | "name": "stdout", 1226 | "output_type": "stream", 1227 | "text": [ 1228 | "(4, 18)\n" 1229 | ] 1230 | }, 1231 | { 1232 | "data": { 1233 | "text/html": [ 1234 | "
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max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_nameparent_chembl_idcx_logdcx_logpcx_most_apkacx_most_bpkaalogphbahbdmw_freebasefull_mwtnum_ro5_violationspsaheavy_atoms
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIBCHEMBL5351.282.9311.469.043.3333398.48398.48077.2329
11CHEMBL296468{'molecule_chembl_id': 'CHEMBL296468', 'parent...{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...BMS-387032CHEMBL2964680.330.947.9410.173.6672380.54380.54080.0525
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIBCHEMBL1800223.054.4712.558.815.9382557.05557.052112.4040
32CHEMBL475251{'molecule_chembl_id': 'CHEMBL475251', 'parent...{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...R-406CHEMBL4752513.633.6310.903.053.63103470.46470.460128.7534
\n", 1359 | "
" 1360 | ], 1361 | "text/plain": [ 1362 | " max_phase molecule_chembl_id \\\n", 1363 | "0 4 CHEMBL535 \n", 1364 | "1 1 CHEMBL296468 \n", 1365 | "2 4 CHEMBL180022 \n", 1366 | "3 2 CHEMBL475251 \n", 1367 | "\n", 1368 | " molecule_hierarchy \\\n", 1369 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 1370 | "1 {'molecule_chembl_id': 'CHEMBL296468', 'parent... \n", 1371 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 1372 | "3 {'molecule_chembl_id': 'CHEMBL475251', 'parent... \n", 1373 | "\n", 1374 | " molecule_properties pref_name \\\n", 1375 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 1376 | "1 {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log... BMS-387032 \n", 1377 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 1378 | "3 {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log... R-406 \n", 1379 | "\n", 1380 | " parent_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp hba hbd \\\n", 1381 | "0 CHEMBL535 1.28 2.93 11.46 9.04 3.33 3 3 \n", 1382 | "1 CHEMBL296468 0.33 0.94 7.94 10.17 3.66 7 2 \n", 1383 | "2 CHEMBL180022 3.05 4.47 12.55 8.81 5.93 8 2 \n", 1384 | "3 CHEMBL475251 3.63 3.63 10.90 3.05 3.63 10 3 \n", 1385 | "\n", 1386 | " mw_freebase full_mwt num_ro5_violations psa heavy_atoms \n", 1387 | "0 398.48 398.48 0 77.23 29 \n", 1388 | "1 380.54 380.54 0 80.05 25 \n", 1389 | "2 557.05 557.05 2 112.40 40 \n", 1390 | "3 470.46 470.46 0 128.75 34 " 1391 | ] 1392 | }, 1393 | "execution_count": 14, 1394 | "metadata": {}, 1395 | "output_type": "execute_result" 1396 | } 1397 | ], 1398 | "source": [ 1399 | "###########################\n", 1400 | "# Convert nested cells (ie those containing a dictionary) to individual columns in the dataframe so that is it easier to filter!\n", 1401 | "###########################\n", 1402 | "# Molecule hierarchy: \n", 1403 | "mol_df['parent_chembl_id'] = mol_df['molecule_hierarchy'].apply(lambda x: x['parent_chembl_id'])\n", 1404 | "\n", 1405 | "#Physicochemical properties (only report if cells are not null)\n", 1406 | "mol_df['cx_logd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logd'])\n", 1407 | "mol_df['cx_logp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logp'])\n", 1408 | "mol_df['cx_most_apka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_apka'])\n", 1409 | "mol_df['cx_most_bpka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_bpka'])\n", 1410 | "mol_df['alogp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['alogp'])\n", 1411 | "mol_df['hba'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hba'])\n", 1412 | "mol_df['hbd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hbd'])\n", 1413 | "mol_df['mw_freebase'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase']) #This is the mwt of the parent compound\n", 1414 | "mol_df['full_mwt'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['full_mwt']) #This is the mwt of the full compound including any salt\n", 1415 | "mol_df['num_ro5_violations'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['num_ro5_violations'])\n", 1416 | "mol_df['psa'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['psa'])\n", 1417 | "mol_df['heavy_atoms'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['heavy_atoms'])\n", 1418 | "\n", 1419 | "mol_df" 1420 | ] 1421 | }, 1422 | { 1423 | "cell_type": "code", 1424 | "execution_count": 16, 1425 | "metadata": {}, 1426 | "outputs": [ 1427 | { 1428 | "data": { 1429 | "text/html": [ 1430 | "
\n", 1431 | "\n", 1444 | "\n", 1445 | " \n", 1446 | " \n", 1447 | " \n", 1448 | " \n", 1449 | " \n", 1450 | " \n", 1451 | " \n", 1452 | " \n", 1453 | " \n", 1454 | " \n", 1455 | " \n", 1456 | " \n", 1457 | " \n", 1458 | " \n", 1459 | " \n", 1460 | " \n", 1461 | " \n", 1462 | " \n", 1463 | " \n", 1464 | " \n", 1465 | " \n", 1466 | " \n", 1467 | " \n", 1468 | " \n", 1469 | " \n", 1470 | " \n", 1471 | " \n", 1472 | " \n", 1473 | " \n", 1474 | " \n", 1475 | " \n", 1476 | " \n", 1477 | " \n", 1478 | " \n", 1479 | " \n", 1480 | " \n", 1481 | " \n", 1482 | " \n", 1483 | " \n", 1484 | " \n", 1485 | " \n", 1486 | " \n", 1487 | " \n", 1488 | " \n", 1489 | " \n", 1490 | " \n", 1491 | " \n", 1492 | " \n", 1493 | " \n", 1494 | " \n", 1495 | " \n", 1496 | " \n", 1497 | " \n", 1498 | " \n", 1499 | " \n", 1500 | " \n", 1501 | " \n", 1502 | " \n", 1503 | " \n", 1504 | " \n", 1505 | " \n", 1506 | " \n", 1507 | " \n", 1508 | " \n", 1509 | " \n", 1510 | " \n", 1511 | " \n", 1512 | "
max_phasemolecule_chembl_idmolecule_hierarchymolecule_propertiespref_nameparent_chembl_idcx_logdcx_logpcx_most_apkacx_most_bpkaalogphbahbdmw_freebasefull_mwtnum_ro5_violationspsaheavy_atoms
04CHEMBL535{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...SUNITINIBCHEMBL5351.282.9311.469.043.3333398.48398.48077.2329
24CHEMBL180022{'molecule_chembl_id': 'CHEMBL180022', 'parent...{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...NERATINIBCHEMBL1800223.054.4712.558.815.9382557.05557.052112.4040
\n", 1513 | "
" 1514 | ], 1515 | "text/plain": [ 1516 | " max_phase molecule_chembl_id \\\n", 1517 | "0 4 CHEMBL535 \n", 1518 | "2 4 CHEMBL180022 \n", 1519 | "\n", 1520 | " molecule_hierarchy \\\n", 1521 | "0 {'molecule_chembl_id': 'CHEMBL535', 'parent_ch... \n", 1522 | "2 {'molecule_chembl_id': 'CHEMBL180022', 'parent... \n", 1523 | "\n", 1524 | " molecule_properties pref_name \\\n", 1525 | "0 {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log... SUNITINIB \n", 1526 | "2 {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log... NERATINIB \n", 1527 | "\n", 1528 | " parent_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp hba hbd \\\n", 1529 | "0 CHEMBL535 1.28 2.93 11.46 9.04 3.33 3 3 \n", 1530 | "2 CHEMBL180022 3.05 4.47 12.55 8.81 5.93 8 2 \n", 1531 | "\n", 1532 | " mw_freebase full_mwt num_ro5_violations psa heavy_atoms \n", 1533 | "0 398.48 398.48 0 77.23 29 \n", 1534 | "2 557.05 557.05 2 112.40 40 " 1535 | ] 1536 | }, 1537 | "execution_count": 16, 1538 | "metadata": {}, 1539 | "output_type": "execute_result" 1540 | } 1541 | ], 1542 | "source": [ 1543 | "###########################\n", 1544 | "#Filter the compounds based on their molecular properties (e.g. molecular weight < 400 amu), or max_phase, for example:\n", 1545 | "###########################\n", 1546 | "\n", 1547 | "#Now keep only compounds with max_phase = 4 (ie approved drugs), for example: \n", 1548 | "res = mol_df[ mol_df['max_phase'] == 4 ]\n", 1549 | "\n", 1550 | "#Display results:\n", 1551 | "res" 1552 | ] 1553 | }, 1554 | { 1555 | "cell_type": "code", 1556 | "execution_count": null, 1557 | "metadata": {}, 1558 | "outputs": [], 1559 | "source": [] 1560 | }, 1561 | { 1562 | "cell_type": "code", 1563 | "execution_count": null, 1564 | "metadata": {}, 1565 | "outputs": [], 1566 | "source": [] 1567 | } 1568 | ], 1569 | "metadata": { 1570 | "kernelspec": { 1571 | "display_name": "Python (my-rdkit-env)", 1572 | "language": "python", 1573 | "name": "my-rdkit-env" 1574 | }, 1575 | "language_info": { 1576 | "codemirror_mode": { 1577 | "name": "ipython", 1578 | "version": 3 1579 | }, 1580 | "file_extension": ".py", 1581 | "mimetype": "text/x-python", 1582 | "name": "python", 1583 | "nbconvert_exporter": "python", 1584 | "pygments_lexer": "ipython3", 1585 | "version": "3.7.3" 1586 | } 1587 | }, 1588 | "nbformat": 4, 1589 | "nbformat_minor": 4 1590 | } 1591 | -------------------------------------------------------------------------------- /ChEMBL_webresource_client_examples.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# ChEMBL webresource client examples\n", 8 | "\n", 9 | "The library helps to access ChEMBL data and cheminformatics tools from Python. You don't need to know how to write SQL. You don't need to know how to interact with REST APIs. You don't need to compile or install any cheminformatics frameworks. Results are cached.\n", 10 | "\n", 11 | "The client handles interaction with the HTTPS protocol and caches all results in the local file system for faster retrieval. Abstracting away all network-related tasks, the client provides the end user with a convenient interface, giving the impression of working with a local resource. The design is based on the Django QuerySet interface. The client also implements lazy evaluation of results, which means it will only evaluate a request for data when a value is required. This approach reduces the number of network requests and increases performance." 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "1. [Compounds](#section1)\n", 19 | "1. [Drugs](#section2)\n", 20 | "1. [Targets](#section3)\n", 21 | "1. [Activities](#section4)\n", 22 | "1. [Assays](#section5)\n", 23 | "1. [Tissues](#section6)\n", 24 | "1. [Cells](#section7)\n", 25 | "1. [Utils](#section8)" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "## Available data entities\n", 33 | "\n", 34 | "You can list available data entities using the following code" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 1, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "['activity', 'activity_supplementary_data_by_activity', 'assay', 'assay_class', 'atc_class', 'binding_site', 'biotherapeutic', 'cell_line', 'chembl_id_lookup', 'compound_record', 'compound_structural_alert', 'description', 'document', 'document_similarity', 'drug', 'drug_indication', 'drug_warning', 'go_slim', 'image', 'mechanism', 'metabolism', 'molecule', 'molecule_form', 'official', 'organism', 'protein_class', 'similarity', 'source', 'substructure', 'target', 'target_component', 'target_relation', 'tissue', 'xref_source']\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "from chembl_webresource_client.new_client import new_client\n", 52 | "\n", 53 | "available_resources = [resource for resource in dir(new_client) if not resource.startswith('_')]\n", 54 | "print(available_resources)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "## Available filters\n", 62 | "\n", 63 | "The design of the client is based on Django QuerySet (https://docs.djangoproject.com/en/1.11/ref/models/querysets) and most important lookup types are supported. These are:\n", 64 | "\n", 65 | "- exact\n", 66 | "- iexact\n", 67 | "- contains\n", 68 | "- icontains\n", 69 | "- in\n", 70 | "- gt\n", 71 | "- gte\n", 72 | "- lt\n", 73 | "- lte\n", 74 | "- startswith\n", 75 | "- istartswith\n", 76 | "- endswith\n", 77 | "- iendswith\n", 78 | "- range\n", 79 | "- isnull\n", 80 | "- regex\n", 81 | "- iregex" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "## `Only` operator\n", 89 | "\n", 90 | "`only` is a special method that limits the results to a selected set of database fields. `only` takes a list of fields (as a single argument) to be included in the results. Note that the specified fields have to exist in the API endpoint against which `only` is executed. Using `only` normally makes an API call faster because less information returned will save bandwidth. The API logic will also check if any SQL joins are necessary to return the specified field(s) and will exclude unnecessary joins which critically improves performance.\n", 91 | "\n", 92 | "Please note that `only` has one limitation: any specified fields will ignore nested fields i.e. calling only(['molecule_properties__alogp']) is equivalent to only(['molecule_properties']).\n", 93 | "\n", 94 | "For many-to-many relationships `only` will not make any SQL join optimisation." 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "# 1. Compounds\n", 102 | "- Compounds in ChEMBL usually have associated bioactivity data. For example, the activity of a compound may have been measured in an experiment against a particular target and results in a certain IC50 value that has been published in the scientific literature.\n", 103 | "\n", 104 | "- Compound records may be retrieved in a number of ways, such as a lookup of an individual compound using various identifiers or by searching for compounds via similarity." 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "## Find a compound by pref_name using the `molecule` endpoint\n", 112 | "- Note the double underscore to filter for a (case insensitive) `iexact` name match within the 'pref_name' database field" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 2, 118 | "metadata": {}, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "[{'atc_classifications': ['B01AC06', 'N02BA01', 'N02BA51', 'A01AD05', 'N02BA71'], 'availability_type': 2, 'biotherapeutic': None, 'black_box_warning': 0, 'chebi_par_id': 15365, 'chirality': 2, 'cross_references': [{'xref_id': 'aspirin', 'xref_name': 'aspirin', 'xref_src': 'DailyMed'}, {'xref_id': '144203627', 'xref_name': 'SID: 144203627', 'xref_src': 'PubChem'}, {'xref_id': '144209315', 'xref_name': 'SID: 144209315', 'xref_src': 'PubChem'}, {'xref_id': '144210466', 'xref_name': 'SID: 144210466', 'xref_src': 'PubChem'}, {'xref_id': '170465039', 'xref_name': 'SID: 170465039', 'xref_src': 'PubChem'}, {'xref_id': '17389202', 'xref_name': 'SID: 17389202', 'xref_src': 'PubChem'}, {'xref_id': '17390036', 'xref_name': 'SID: 17390036', 'xref_src': 'PubChem'}, {'xref_id': '174007205', 'xref_name': 'SID: 174007205', 'xref_src': 'PubChem'}, {'xref_id': '26747283', 'xref_name': 'SID: 26747283', 'xref_src': 'PubChem'}, {'xref_id': '26752858', 'xref_name': 'SID: 26752858', 'xref_src': 'PubChem'}, {'xref_id': '47193676', 'xref_name': 'SID: 47193676', 'xref_src': 'PubChem'}, {'xref_id': '50105490', 'xref_name': 'SID: 50105490', 'xref_src': 'PubChem'}, {'xref_id': '85230910', 'xref_name': 'SID: 85230910', 'xref_src': 'PubChem'}, {'xref_id': '87798', 'xref_name': 'SID: 87798', 'xref_src': 'PubChem'}, {'xref_id': '90340586', 'xref_name': 'SID: 90340586', 'xref_src': 'PubChem'}, {'xref_id': '14', 'xref_name': 'aspirin', 'xref_src': 'TG-GATEs'}, {'xref_id': 'Aspirin', 'xref_name': None, 'xref_src': 'Wikipedia'}], 'dosed_ingredient': True, 'first_approval': 1950, 'first_in_class': 0, 'helm_notation': None, 'indication_class': 'Analgesic; Antirheumatic; Antipyretic', 'inorganic_flag': 0, 'max_phase': 4, 'molecule_chembl_id': 'CHEMBL25', 'molecule_hierarchy': {'molecule_chembl_id': 'CHEMBL25', 'parent_chembl_id': 'CHEMBL25'}, 'molecule_properties': {'alogp': '1.31', 'aromatic_rings': 1, 'cx_logd': '-2.16', 'cx_logp': '1.24', 'cx_most_apka': '3.41', 'cx_most_bpka': None, 'full_molformula': 'C9H8O4', 'full_mwt': '180.16', 'hba': 3, 'hba_lipinski': 4, 'hbd': 1, 'hbd_lipinski': 1, 'heavy_atoms': 13, 'molecular_species': 'ACID', 'mw_freebase': '180.16', 'mw_monoisotopic': '180.0423', 'num_lipinski_ro5_violations': 0, 'num_ro5_violations': 0, 'psa': '63.60', 'qed_weighted': '0.55', 'ro3_pass': 'N', 'rtb': 2}, 'molecule_structures': {'canonical_smiles': 'CC(=O)Oc1ccccc1C(=O)O', 'molfile': '\\n RDKit 2D\\n\\n 13 13 0 0 0 0 0 0 0 0999 V2000\\n 8.8810 -2.1206 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 8.8798 -2.9479 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 9.5946 -3.3607 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3110 -2.9474 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3081 -2.1170 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 9.5928 -1.7078 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0210 -1.7018 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.7369 -2.1116 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0260 -3.3588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0273 -4.1837 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.7423 -4.5949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3136 -4.5972 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0178 -0.8769 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 1 2 2 0\\n 5 7 1 0\\n 3 4 2 0\\n 7 8 2 0\\n 4 9 1 0\\n 4 5 1 0\\n 9 10 1 0\\n 2 3 1 0\\n 10 11 1 0\\n 5 6 2 0\\n 10 12 2 0\\n 6 1 1 0\\n 7 13 1 0\\nM END\\n\\n> \\nCHEMBL25\\n\\n> \\nASPIRIN\\n\\n', 'standard_inchi': 'InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)12/h2-5H,1H3,(H,11,12)', 'standard_inchi_key': 'BSYNRYMUTXBXSQ-UHFFFAOYSA-N'}, 'molecule_synonyms': [{'molecule_synonym': '8-hour bayer', 'syn_type': 'TRADE_NAME', 'synonyms': '8-HOUR BAYER'}, {'molecule_synonym': 'Acetosalic Acid', 'syn_type': 'TRADE_NAME', 'synonyms': 'Acetosalic Acid'}, {'molecule_synonym': 'Acetylsalic acid', 'syn_type': 'TRADE_NAME', 'synonyms': 'ACETYLSALIC ACID'}, {'molecule_synonym': 'Acetylsalicylic Acid', 'syn_type': 'INN', 'synonyms': 'Acetylsalicylic Acid'}, {'molecule_synonym': 'Acetylsalicylic Acid', 'syn_type': 'TRADE_NAME', 'synonyms': 'Acetylsalicylic Acid'}, {'molecule_synonym': 'Acetylsalicylic acid', 'syn_type': 'ATC', 'synonyms': 'ACETYLSALICYLIC ACID'}, {'molecule_synonym': 'Acetylsalicylic acid', 'syn_type': 'OTHER', 'synonyms': 'ACETYLSALICYLIC ACID'}, {'molecule_synonym': 'Alka rapid', 'syn_type': 'TRADE_NAME', 'synonyms': 'ALKA RAPID'}, {'molecule_synonym': 'Anadin all night', 'syn_type': 'TRADE_NAME', 'synonyms': 'ANADIN ALL NIGHT'}, {'molecule_synonym': 'Angettes 75', 'syn_type': 'TRADE_NAME', 'synonyms': 'ANGETTES 75'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'USAN', 'synonyms': 'Aspirin'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'BAN', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'BNF', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'FDA', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'JAN', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'MERCK_INDEX', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'OTHER', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'TRADE_NAME', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspirin', 'syn_type': 'USP', 'synonyms': 'ASPIRIN'}, {'molecule_synonym': 'Aspro clr', 'syn_type': 'TRADE_NAME', 'synonyms': 'ASPRO CLR'}, {'molecule_synonym': 'BAY1019036', 'syn_type': 'RESEARCH_CODE', 'synonyms': 'BAY1019036'}, {'molecule_synonym': 'Bayer extra strength aspirin for migraine pain', 'syn_type': 'TRADE_NAME', 'synonyms': 'BAYER EXTRA STRENGTH ASPIRIN FOR MIGRAINE PAIN'}, {'molecule_synonym': 'Danamep', 'syn_type': 'TRADE_NAME', 'synonyms': 'DANAMEP'}, {'molecule_synonym': 'Disprin cv', 'syn_type': 'TRADE_NAME', 'synonyms': 'DISPRIN CV'}, {'molecule_synonym': 'Disprin direct', 'syn_type': 'TRADE_NAME', 'synonyms': 'DISPRIN DIRECT'}, {'molecule_synonym': 'Durlaza', 'syn_type': 'TRADE_NAME', 'synonyms': 'DURLAZA'}, {'molecule_synonym': 'Ecotrin', 'syn_type': 'TRADE_NAME', 'synonyms': 'Ecotrin'}, {'molecule_synonym': 'Enprin', 'syn_type': 'TRADE_NAME', 'synonyms': 'ENPRIN'}, {'molecule_synonym': 'Equi-Prin', 'syn_type': 'TRADE_NAME', 'synonyms': 'Equi-Prin'}, {'molecule_synonym': 'Gencardia', 'syn_type': 'TRADE_NAME', 'synonyms': 'GENCARDIA'}, {'molecule_synonym': 'Levius', 'syn_type': 'TRADE_NAME', 'synonyms': 'LEVIUS'}, {'molecule_synonym': 'Max strgh aspro clr', 'syn_type': 'TRADE_NAME', 'synonyms': 'MAX STRGH ASPRO CLR'}, {'molecule_synonym': 'Measurin', 'syn_type': 'TRADE_NAME', 'synonyms': 'MEASURIN'}, {'molecule_synonym': 'Micropirin ec', 'syn_type': 'TRADE_NAME', 'synonyms': 'MICROPIRIN EC'}, {'molecule_synonym': 'NSC-27223', 'syn_type': 'RESEARCH_CODE', 'synonyms': 'NSC-27223'}, {'molecule_synonym': 'NSC-406186', 'syn_type': 'RESEARCH_CODE', 'synonyms': 'NSC-406186'}, {'molecule_synonym': 'Nu-seals 300', 'syn_type': 'TRADE_NAME', 'synonyms': 'NU-SEALS 300'}, {'molecule_synonym': 'Nu-seals 600', 'syn_type': 'TRADE_NAME', 'synonyms': 'NU-SEALS 600'}, {'molecule_synonym': 'Nu-seals 75', 'syn_type': 'TRADE_NAME', 'synonyms': 'NU-SEALS 75'}, {'molecule_synonym': 'Nu-seals cardio 75', 'syn_type': 'TRADE_NAME', 'synonyms': 'NU-SEALS CARDIO 75'}, {'molecule_synonym': 'Paynocil', 'syn_type': 'TRADE_NAME', 'synonyms': 'PAYNOCIL'}, {'molecule_synonym': 'Platet', 'syn_type': 'TRADE_NAME', 'synonyms': 'PLATET'}, {'molecule_synonym': 'Platet 300', 'syn_type': 'TRADE_NAME', 'synonyms': 'PLATET 300'}, {'molecule_synonym': 'Postmi 300', 'syn_type': 'TRADE_NAME', 'synonyms': 'POSTMI 300'}, {'molecule_synonym': 'Postmi 75', 'syn_type': 'TRADE_NAME', 'synonyms': 'POSTMI 75'}, {'molecule_synonym': 'Salicylic Acid Acetate', 'syn_type': 'TRADE_NAME', 'synonyms': 'Salicylic Acid Acetate'}, {'molecule_synonym': 'Vazalore', 'syn_type': 'TRADE_NAME', 'synonyms': 'VAZALORE'}], 'molecule_type': 'Small molecule', 'natural_product': 0, 'oral': True, 'parenteral': False, 'polymer_flag': False, 'pref_name': 'ASPIRIN', 'prodrug': 0, 'structure_type': 'MOL', 'therapeutic_flag': True, 'topical': False, 'usan_stem': None, 'usan_stem_definition': None, 'usan_substem': None, 'usan_year': None, 'withdrawn_class': None, 'withdrawn_country': None, 'withdrawn_flag': False, 'withdrawn_reason': None, 'withdrawn_year': None}]" 124 | ] 125 | }, 126 | "execution_count": 2, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | } 130 | ], 131 | "source": [ 132 | "from chembl_webresource_client.new_client import new_client #Import ChEMBL python client\n", 133 | "\n", 134 | "molecule = new_client.molecule\n", 135 | "mols = molecule.filter(pref_name__iexact='aspirin')\n", 136 | "mols" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "## Find a compound by its synonyms\n", 144 | "\n", 145 | "- In some cases a compound may be more commonly known by a synonym than its preferred name in ChEMBL (pref_name)\n", 146 | "- The 'molecule_synonym' field is nested within the 'molecule_synonyms' field hence the first double underscore, followed by a second double underscore to filter for a (case insensitive) iexact name match within the 'pref_name' database field\n", 147 | "- Use the `only` method to specify which database fields you want to be included in response" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 3, 153 | "metadata": {}, 154 | "outputs": [ 155 | { 156 | "data": { 157 | "text/plain": [ 158 | "[{'molecule_chembl_id': 'CHEMBL192'}, {'molecule_chembl_id': 'CHEMBL1737'}]" 159 | ] 160 | }, 161 | "execution_count": 3, 162 | "metadata": {}, 163 | "output_type": "execute_result" 164 | } 165 | ], 166 | "source": [ 167 | "from chembl_webresource_client.new_client import new_client\n", 168 | "\n", 169 | "molecule = new_client.molecule\n", 170 | "mols = molecule.filter(molecule_synonyms__molecule_synonym__iexact='viagra').only('molecule_chembl_id')\n", 171 | "mols" 172 | ] 173 | }, 174 | { 175 | "cell_type": "markdown", 176 | "metadata": {}, 177 | "source": [ 178 | "## Get a single compound by ChEMBL id\n", 179 | "\n", 180 | "- All the main entities in the ChEMBL database have a ChEMBL ID. This is a stable identifier designed for straightforward lookup of data." 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 4, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "[{'molecule_chembl_id': 'CHEMBL192', 'molecule_structures': {'canonical_smiles': 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12', 'molfile': '\\n RDKit 2D\\n\\n 33 36 0 0 0 0 0 0 0 0999 V2000\\n 2.1000 -0.0042 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.1000 0.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.5375 -0.0042 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4917 -0.3667 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.8792 -0.0042 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.8042 0.9083 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4917 1.0625 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.8792 0.6833 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.2042 0.3458 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.8042 -0.2417 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.2875 -0.3750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.1583 -0.3750 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.9333 -0.3750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3208 -0.0333 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.1875 0.6083 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.8958 0.6083 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -3.3958 -1.0917 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.7833 -0.0042 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.1583 -1.0917 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.2875 -1.1125 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4917 1.7708 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.9333 -1.1125 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3208 -1.4542 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -3.3958 -0.3750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.7833 -1.4417 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.0750 1.5750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.8042 -0.9500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.8792 -1.4542 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -3.9958 -1.4292 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4958 -1.1000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.4167 -1.3125 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.1125 -1.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 4.0375 -0.9542 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2 1 2 0\\n 3 13 1 0\\n 4 1 1 0\\n 5 4 2 0\\n 6 2 1 0\\n 7 2 1 0\\n 8 5 1 0\\n 9 10 2 0\\n 10 1 1 0\\n 11 5 1 0\\n 12 3 1 0\\n 13 14 2 0\\n 14 11 1 0\\n 15 3 2 0\\n 16 3 2 0\\n 17 25 1 0\\n 18 12 1 0\\n 19 12 1 0\\n 20 11 2 0\\n 21 7 2 0\\n 22 23 2 0\\n 23 20 1 0\\n 24 18 1 0\\n 25 19 1 0\\n 26 6 1 0\\n 27 10 1 0\\n 28 20 1 0\\n 29 17 1 0\\n 30 28 1 0\\n 31 27 1 0\\n 32 30 1 0\\n 33 31 1 0\\n 9 6 1 0\\n 8 7 1 0\\n 22 13 1 0\\n 17 24 1 0\\nM END\\n\\n> \\nCHEMBL192\\n\\n> \\nSILDENAFIL\\n\\n', 'standard_inchi': 'InChI=1S/C22H30N6O4S/c1-5-7-17-19-20(27(4)25-17)22(29)24-21(23-19)16-14-15(8-9-18(16)32-6-2)33(30,31)28-12-10-26(3)11-13-28/h8-9,14H,5-7,10-13H2,1-4H3,(H,23,24,29)', 'standard_inchi_key': 'BNRNXUUZRGQAQC-UHFFFAOYSA-N'}, 'pref_name': 'SILDENAFIL'}]" 192 | ] 193 | }, 194 | "execution_count": 4, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "from chembl_webresource_client.new_client import new_client\n", 201 | "\n", 202 | "molecule = new_client.molecule\n", 203 | "m1 = molecule.filter(chembl_id='CHEMBL192').only(['molecule_chembl_id', 'pref_name', 'molecule_structures'])\n", 204 | "m1" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": {}, 210 | "source": [ 211 | "## Get many compounds using a list of their identifiers (ChEMBL_id)\n", 212 | "- Use the double underscore followed by the `in` keyword to find a list of molecule_chembl_id's" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 5, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "data": { 222 | "text/plain": [ 223 | "[{'molecule_chembl_id': 'CHEMBL25', 'pref_name': 'ASPIRIN'}, {'molecule_chembl_id': 'CHEMBL27', 'pref_name': 'PROPRANOLOL'}, {'molecule_chembl_id': 'CHEMBL192', 'pref_name': 'SILDENAFIL'}]" 224 | ] 225 | }, 226 | "execution_count": 5, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "from chembl_webresource_client.new_client import new_client\n", 233 | "\n", 234 | "molecule = new_client.molecule\n", 235 | "mols = molecule.filter(molecule_chembl_id__in=['CHEMBL25', 'CHEMBL192', 'CHEMBL27']).only(['molecule_chembl_id', 'pref_name'])\n", 236 | "mols" 237 | ] 238 | }, 239 | { 240 | "cell_type": "markdown", 241 | "metadata": {}, 242 | "source": [ 243 | "## Display a compound image using the `image` endpoint" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 6, 249 | "metadata": {}, 250 | "outputs": [ 251 | { 252 | "data": { 253 | "image/svg+xml": [ 254 | "\n", 255 | "\n", 256 | "\n", 257 | "\n", 258 | "\n", 259 | "\n", 260 | "\n", 261 | "\n", 262 | "\n", 263 | "\n", 264 | "\n", 265 | "\n", 266 | "\n", 267 | "\n", 268 | "\n", 269 | "\n", 270 | "\n", 271 | "\n", 272 | "\n", 273 | "\n", 274 | "\n", 275 | "\n", 276 | "\n", 277 | "\n", 278 | "\n", 279 | "\n", 280 | "\n", 281 | "O\n", 282 | "O\n", 283 | "O\n", 284 | "OH\n", 285 | "" 286 | ], 287 | "text/plain": [ 288 | "" 289 | ] 290 | }, 291 | "execution_count": 6, 292 | "metadata": {}, 293 | "output_type": "execute_result" 294 | } 295 | ], 296 | "source": [ 297 | "from chembl_webresource_client.new_client import new_client\n", 298 | "from IPython.display import SVG\n", 299 | "\n", 300 | "image = new_client.image\n", 301 | "image.set_format('svg')\n", 302 | "SVG(image.get('CHEMBL25'))" 303 | ] 304 | }, 305 | { 306 | "cell_type": "markdown", 307 | "metadata": {}, 308 | "source": [ 309 | "## Get a single compound by standard inchi key" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 7, 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "[{'molecule_chembl_id': 'CHEMBL25', 'molecule_structures': {'canonical_smiles': 'CC(=O)Oc1ccccc1C(=O)O', 'molfile': '\\n RDKit 2D\\n\\n 13 13 0 0 0 0 0 0 0 0999 V2000\\n 8.8810 -2.1206 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 8.8798 -2.9479 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 9.5946 -3.3607 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3110 -2.9474 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3081 -2.1170 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 9.5928 -1.7078 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0210 -1.7018 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.7369 -2.1116 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0260 -3.3588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0273 -4.1837 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.7423 -4.5949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 10.3136 -4.5972 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 11.0178 -0.8769 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 1 2 2 0\\n 5 7 1 0\\n 3 4 2 0\\n 7 8 2 0\\n 4 9 1 0\\n 4 5 1 0\\n 9 10 1 0\\n 2 3 1 0\\n 10 11 1 0\\n 5 6 2 0\\n 10 12 2 0\\n 6 1 1 0\\n 7 13 1 0\\nM END\\n\\n> \\nCHEMBL25\\n\\n> \\nASPIRIN\\n\\n', 'standard_inchi': 'InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)12/h2-5H,1H3,(H,11,12)', 'standard_inchi_key': 'BSYNRYMUTXBXSQ-UHFFFAOYSA-N'}, 'pref_name': 'ASPIRIN'}]" 321 | ] 322 | }, 323 | "execution_count": 7, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "from chembl_webresource_client.new_client import new_client\n", 330 | "\n", 331 | "molecule = new_client.molecule\n", 332 | "mol = molecule.filter(molecule_structures__standard_inchi_key='BSYNRYMUTXBXSQ-UHFFFAOYSA-N').only(['molecule_chembl_id', 'pref_name', 'molecule_structures'])\n", 333 | "mol" 334 | ] 335 | }, 336 | { 337 | "cell_type": "markdown", 338 | "metadata": {}, 339 | "source": [ 340 | "## Find compounds similar to a given SMILES query, with a similarity threshold of 70% using the `similarity` endpoint" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 8, 346 | "metadata": {}, 347 | "outputs": [ 348 | { 349 | "name": "stdout", 350 | "output_type": "stream", 351 | "text": [ 352 | "{'molecule_chembl_id': 'CHEMBL477888', 'similarity': '85.4166686534881591796875'}\n", 353 | "{'molecule_chembl_id': 'CHEMBL477889', 'similarity': '85.4166686534881591796875'}\n", 354 | "{'molecule_chembl_id': 'CHEMBL478779', 'similarity': '85.4166686534881591796875'}\n", 355 | "{'molecule_chembl_id': 'CHEMBL2304268', 'similarity': '70.1754391193389892578125'}\n" 356 | ] 357 | } 358 | ], 359 | "source": [ 360 | "from chembl_webresource_client.new_client import new_client\n", 361 | "\n", 362 | "similarity = new_client.similarity\n", 363 | "res = similarity.filter(smiles=\"CO[C@@H](CCC#C\\C=C/CCCC(C)CCCCC=C)C(=O)[O-]\", similarity=70).only(['molecule_chembl_id', 'similarity'])\n", 364 | "for i in res:\n", 365 | " print(i)" 366 | ] 367 | }, 368 | { 369 | "cell_type": "markdown", 370 | "metadata": {}, 371 | "source": [ 372 | "## Find compounds similar to aspirin (CHEMBL25) with similarity threshold of 70% using the `similarity` endpoint\n", 373 | "- Use pandas python module to view the result as a table of data" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 9, 379 | "metadata": {}, 380 | "outputs": [ 381 | { 382 | "data": { 383 | "text/html": [ 384 | "
\n", 385 | "\n", 398 | "\n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | "
molecule_chembl_idpref_namesimilarity
0CHEMBL2296002None100
1CHEMBL1697753ASPIRIN DL-LYSINE100
2CHEMBL3833325CARBASPIRIN CALCIUM88.8888895511627197265625
3CHEMBL3833404CARBASPIRIN88.8888895511627197265625
4CHEMBL350343DIPLOSALSALATE85.7142865657806396484375
5CHEMBL4515737None70.3703701496124267578125
6CHEMBL1451173DIPYROCETYL69.9999988079071044921875
\n", 452 | "
" 453 | ], 454 | "text/plain": [ 455 | " molecule_chembl_id pref_name similarity\n", 456 | "0 CHEMBL2296002 None 100\n", 457 | "1 CHEMBL1697753 ASPIRIN DL-LYSINE 100\n", 458 | "2 CHEMBL3833325 CARBASPIRIN CALCIUM 88.8888895511627197265625\n", 459 | "3 CHEMBL3833404 CARBASPIRIN 88.8888895511627197265625\n", 460 | "4 CHEMBL350343 DIPLOSALSALATE 85.7142865657806396484375\n", 461 | "5 CHEMBL4515737 None 70.3703701496124267578125\n", 462 | "6 CHEMBL1451173 DIPYROCETYL 69.9999988079071044921875" 463 | ] 464 | }, 465 | "execution_count": 9, 466 | "metadata": {}, 467 | "output_type": "execute_result" 468 | } 469 | ], 470 | "source": [ 471 | "from chembl_webresource_client.new_client import new_client\n", 472 | "import pandas as pd #Use pandas python module to view data\n", 473 | "\n", 474 | "similarity = new_client.similarity\n", 475 | "res = similarity.filter(chembl_id='CHEMBL25', similarity=70).only(['molecule_chembl_id', 'pref_name', 'similarity'])\n", 476 | "pd.DataFrame(res)" 477 | ] 478 | }, 479 | { 480 | "cell_type": "markdown", 481 | "metadata": {}, 482 | "source": [ 483 | "## Find compounds with the same connectivity" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 10, 489 | "metadata": {}, 490 | "outputs": [ 491 | { 492 | "data": { 493 | "text/html": [ 494 | "
\n", 495 | "\n", 508 | "\n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | "
molecule_chembl_idpref_name
0CHEMBL1431METFORMIN
1CHEMBL1703METFORMIN HYDROCHLORIDE
2CHEMBL3094198None
\n", 534 | "
" 535 | ], 536 | "text/plain": [ 537 | " molecule_chembl_id pref_name\n", 538 | "0 CHEMBL1431 METFORMIN\n", 539 | "1 CHEMBL1703 METFORMIN HYDROCHLORIDE\n", 540 | "2 CHEMBL3094198 None" 541 | ] 542 | }, 543 | "execution_count": 10, 544 | "metadata": {}, 545 | "output_type": "execute_result" 546 | } 547 | ], 548 | "source": [ 549 | "from chembl_webresource_client.new_client import new_client\n", 550 | "import pandas as pd #Use pandas python module to view data\n", 551 | "\n", 552 | "molecule = new_client.molecule\n", 553 | "res = molecule.filter(molecule_structures__canonical_smiles__connectivity='CN(C)C(=N)N=C(N)N').only(['molecule_chembl_id', 'pref_name'])\n", 554 | "pd.DataFrame(res)" 555 | ] 556 | }, 557 | { 558 | "cell_type": "markdown", 559 | "metadata": {}, 560 | "source": [ 561 | "## Get all biotherapeutic compounds" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": 11, 567 | "metadata": {}, 568 | "outputs": [ 569 | { 570 | "data": { 571 | "text/plain": [ 572 | "22963" 573 | ] 574 | }, 575 | "execution_count": 11, 576 | "metadata": {}, 577 | "output_type": "execute_result" 578 | } 579 | ], 580 | "source": [ 581 | "from chembl_webresource_client.new_client import new_client\n", 582 | "\n", 583 | "molecule = new_client.molecule\n", 584 | "biotherapeutics = molecule.filter(biotherapeutic__isnull=False)\n", 585 | "len(biotherapeutics)" 586 | ] 587 | }, 588 | { 589 | "cell_type": "markdown", 590 | "metadata": {}, 591 | "source": [ 592 | "## Get compounds with molecular weight <= 300\n", 593 | "- The `lte` keyword means 'less than or equal to'" 594 | ] 595 | }, 596 | { 597 | "cell_type": "code", 598 | "execution_count": 12, 599 | "metadata": {}, 600 | "outputs": [ 601 | { 602 | "data": { 603 | "text/plain": [ 604 | "367682" 605 | ] 606 | }, 607 | "execution_count": 12, 608 | "metadata": {}, 609 | "output_type": "execute_result" 610 | } 611 | ], 612 | "source": [ 613 | "from chembl_webresource_client.new_client import new_client\n", 614 | "\n", 615 | "molecule = new_client.molecule\n", 616 | "light_molecules = molecule.filter(molecule_properties__mw_freebase__lte=300)\n", 617 | "\n", 618 | "len(light_molecules)" 619 | ] 620 | }, 621 | { 622 | "cell_type": "markdown", 623 | "metadata": {}, 624 | "source": [ 625 | "## Get compounds with molecular weight <= 300 AND a pref_name ending with nib (ie a small molecule inhibitor)\n", 626 | "- Note the double underscore to access the 'mw_freebase' database field that is nested within 'molecule_properties' field\n", 627 | "- Multiple `filter` conditions can be combined into a list separated by \",\"" 628 | ] 629 | }, 630 | { 631 | "cell_type": "code", 632 | "execution_count": 13, 633 | "metadata": {}, 634 | "outputs": [ 635 | { 636 | "data": { 637 | "text/html": [ 638 | "
\n", 639 | "\n", 652 | "\n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | "
molecule_chembl_idpref_name
0CHEMBL276711SEMAXANIB
1CHEMBL4594348ELSUBRUTINIB
\n", 673 | "
" 674 | ], 675 | "text/plain": [ 676 | " molecule_chembl_id pref_name\n", 677 | "0 CHEMBL276711 SEMAXANIB\n", 678 | "1 CHEMBL4594348 ELSUBRUTINIB" 679 | ] 680 | }, 681 | "execution_count": 13, 682 | "metadata": {}, 683 | "output_type": "execute_result" 684 | } 685 | ], 686 | "source": [ 687 | "from chembl_webresource_client.new_client import new_client\n", 688 | "import pandas as pd #Use pandas python module to view data as tables\n", 689 | "\n", 690 | "molecule = new_client.molecule\n", 691 | "light_nib_molecules = molecule.filter(molecule_properties__mw_freebase__lte=300, pref_name__iendswith=\"nib\").only(['molecule_chembl_id', 'pref_name'])\n", 692 | "\n", 693 | "pd.DataFrame(light_nib_molecules)" 694 | ] 695 | }, 696 | { 697 | "cell_type": "markdown", 698 | "metadata": {}, 699 | "source": [ 700 | "## Get all compounds in ChEMBL with no Rule-of-Five violations" 701 | ] 702 | }, 703 | { 704 | "cell_type": "code", 705 | "execution_count": 14, 706 | "metadata": {}, 707 | "outputs": [ 708 | { 709 | "data": { 710 | "text/plain": [ 711 | "1441706" 712 | ] 713 | }, 714 | "execution_count": 14, 715 | "metadata": {}, 716 | "output_type": "execute_result" 717 | } 718 | ], 719 | "source": [ 720 | "from chembl_webresource_client.new_client import new_client\n", 721 | "\n", 722 | "molecule = new_client.molecule\n", 723 | "no_violations = molecule.filter(molecule_properties__num_ro5_violations=0)\n", 724 | "len(no_violations)" 725 | ] 726 | }, 727 | { 728 | "cell_type": "markdown", 729 | "metadata": {}, 730 | "source": [ 731 | "# 2. Drugs\n", 732 | "- By contrast to compounds, marketed drugs and clinical candidates in ChEMBL normally have curated information for their synonyms, disease indication, mechanism of action, safety warnings etc, and do not necessarily have to have associated measured bioactivity data. \n", 733 | "- Clinical candidates are drugs that are progressing through Phases 1, 2 and 3 of the drug discovery pipeline, while approved drugs are considered to be Phase 4 (ie max_phase=4).\n", 734 | "- Note that the data in the `drugs` API endpoint is aggregated onto the parent drug form within each compound family" 735 | ] 736 | }, 737 | { 738 | "cell_type": "markdown", 739 | "metadata": {}, 740 | "source": [ 741 | "## Filter drugs by approval year and name using the `drug` endpoint" 742 | ] 743 | }, 744 | { 745 | "cell_type": "code", 746 | "execution_count": 15, 747 | "metadata": {}, 748 | "outputs": [ 749 | { 750 | "data": { 751 | "text/html": [ 752 | "
\n", 753 | "\n", 766 | "\n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | " \n", 790 | " \n", 791 | " \n", 792 | " \n", 793 | " \n", 794 | " \n", 795 | " \n", 796 | " \n", 797 | " \n", 798 | " \n", 799 | " \n", 800 | " \n", 801 | " \n", 802 | " \n", 803 | " \n", 804 | " \n", 805 | " \n", 806 | " \n", 807 | " \n", 808 | " \n", 809 | " \n", 810 | "
applicantsatc_code_descriptiondevelopment_phasefirst_approvalmolecule_chembl_idresearch_codessynonymsusan_stemusan_stem_definitionusan_year
0NoneNone41987CHEMBL611None[Terazosin (BAN, INN, MI), Terazosin hydrochlo...-azosinantihypertensives (prazosin type)1980
1NoneNone41990CHEMBL707None[Doxazosin mesilate (JAN), Doxazosin mesylate ...-azosinantihypertensives (prazosin type)1981
\n", 811 | "
" 812 | ], 813 | "text/plain": [ 814 | " applicants atc_code_description development_phase first_approval \\\n", 815 | "0 None None 4 1987 \n", 816 | "1 None None 4 1990 \n", 817 | "\n", 818 | " molecule_chembl_id research_codes \\\n", 819 | "0 CHEMBL611 None \n", 820 | "1 CHEMBL707 None \n", 821 | "\n", 822 | " synonyms usan_stem \\\n", 823 | "0 [Terazosin (BAN, INN, MI), Terazosin hydrochlo... -azosin \n", 824 | "1 [Doxazosin mesilate (JAN), Doxazosin mesylate ... -azosin \n", 825 | "\n", 826 | " usan_stem_definition usan_year \n", 827 | "0 antihypertensives (prazosin type) 1980 \n", 828 | "1 antihypertensives (prazosin type) 1981 " 829 | ] 830 | }, 831 | "execution_count": 15, 832 | "metadata": {}, 833 | "output_type": "execute_result" 834 | } 835 | ], 836 | "source": [ 837 | "from chembl_webresource_client.new_client import new_client\n", 838 | "import pandas as pd #Use pandas python module to view data as tables\n", 839 | "\n", 840 | "drug = new_client.drug\n", 841 | "res = drug.filter(first_approval__gte=1980).filter(usan_stem=\"-azosin\").only(['development_phase','first_approval','molecule_chembl_id','synonyms', 'usan_stem','usan_stem_definition', 'usan_year'])\n", 842 | "pd.DataFrame(res)" 843 | ] 844 | }, 845 | { 846 | "cell_type": "markdown", 847 | "metadata": {}, 848 | "source": [ 849 | "## Find compounds that have been recently approved as marketed drugs and are immunosupressants\n", 850 | "- Use the `molecule` endpoint to find compounds (an alternative would be to use the `drugs` endpoint)\n", 851 | "- Use `order_by` to sort the result" 852 | ] 853 | }, 854 | { 855 | "cell_type": "code", 856 | "execution_count": 17, 857 | "metadata": {}, 858 | "outputs": [ 859 | { 860 | "data": { 861 | "text/html": [ 862 | "
\n", 863 | "\n", 876 | "\n", 877 | " \n", 878 | " \n", 879 | " \n", 880 | " \n", 881 | " \n", 882 | " \n", 883 | " \n", 884 | " \n", 885 | " \n", 886 | " \n", 887 | " \n", 888 | " \n", 889 | " \n", 890 | " \n", 891 | " \n", 892 | " \n", 893 | " \n", 894 | " \n", 895 | " \n", 896 | " \n", 897 | " \n", 898 | " \n", 899 | " \n", 900 | " \n", 901 | " \n", 902 | " \n", 903 | " \n", 904 | " \n", 905 | " \n", 906 | " \n", 907 | " \n", 908 | " \n", 909 | " \n", 910 | " \n", 911 | " \n", 912 | " \n", 913 | " \n", 914 | " \n", 915 | " \n", 916 | " \n", 917 | " \n", 918 | " \n", 919 | " \n", 920 | " \n", 921 | " \n", 922 | " \n", 923 | " \n", 924 | " \n", 925 | " \n", 926 | " \n", 927 | " \n", 928 | " \n", 929 | " \n", 930 | " \n", 931 | "
atc_classificationsfirst_approvalindication_classmax_phasemolecule_chembl_idmolecule_typepref_name
0[L04AA47]2020None4CHEMBL2109334AntibodyINEBILIZUMAB
1[L04AA38]2020None4CHEMBL3707247Small moleculeOZANIMOD
2[L04AC19]2020None4CHEMBL3833307AntibodySATRALIZUMAB
3[L04AD03]2021None4CHEMBL2218919ProteinVOCLOSPORIN
\n", 932 | "
" 933 | ], 934 | "text/plain": [ 935 | " atc_classifications first_approval indication_class max_phase \\\n", 936 | "0 [L04AA47] 2020 None 4 \n", 937 | "1 [L04AA38] 2020 None 4 \n", 938 | "2 [L04AC19] 2020 None 4 \n", 939 | "3 [L04AD03] 2021 None 4 \n", 940 | "\n", 941 | " molecule_chembl_id molecule_type pref_name \n", 942 | "0 CHEMBL2109334 Antibody INEBILIZUMAB \n", 943 | "1 CHEMBL3707247 Small molecule OZANIMOD \n", 944 | "2 CHEMBL3833307 Antibody SATRALIZUMAB \n", 945 | "3 CHEMBL2218919 Protein VOCLOSPORIN " 946 | ] 947 | }, 948 | "execution_count": 17, 949 | "metadata": {}, 950 | "output_type": "execute_result" 951 | } 952 | ], 953 | "source": [ 954 | "from chembl_webresource_client.new_client import new_client\n", 955 | "import pandas as pd #Use pandas python module to view data as tables\n", 956 | "\n", 957 | "molecule = new_client.molecule\n", 958 | "approved_drugs = molecule.filter(max_phase=4 #approved drugs only\n", 959 | " , atc_classifications__level2='L04' #ATC classification as Immunosupressants\n", 960 | " , first_approval__gte=2020 #first_approval after 2020\n", 961 | " ).order_by('first_approval').only(['atc_classifications','first_approval','indication_class','max_phase','pref_name','molecule_type', 'molecule_chembl_id'])\n", 962 | "\n", 963 | "pd.DataFrame(approved_drugs) #Convert the list of results into a Pandas dataframe" 964 | ] 965 | }, 966 | { 967 | "cell_type": "markdown", 968 | "metadata": {}, 969 | "source": [ 970 | "## Get Phase 3 clinical candidates for lung cancer, and examine their molecular properties\n", 971 | "- First use the `drug_indication` API endpoint, specifying 'max_phase_for_ind' to be 3 (ie clinical candidates in Phase 3 progressing through the drug discovery pipeline for lung cancer)\n", 972 | "- And then feed the results into the `molecule` API endpoint to extract their molecular properties" 973 | ] 974 | }, 975 | { 976 | "cell_type": "code", 977 | "execution_count": 18, 978 | "metadata": {}, 979 | "outputs": [ 980 | { 981 | "data": { 982 | "text/html": [ 983 | "
\n", 984 | "\n", 997 | "\n", 998 | " \n", 999 | " \n", 1000 | " \n", 1001 | " \n", 1002 | " \n", 1003 | " \n", 1004 | " \n", 1005 | " \n", 1006 | " \n", 1007 | " \n", 1008 | " \n", 1009 | " \n", 1010 | " \n", 1011 | " \n", 1012 | " \n", 1013 | " \n", 1014 | " \n", 1015 | " \n", 1016 | " \n", 1017 | " \n", 1018 | " \n", 1019 | " \n", 1020 | " \n", 1021 | " \n", 1022 | " \n", 1023 | " \n", 1024 | " \n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | " \n", 1030 | " \n", 1031 | " \n", 1032 | " \n", 1033 | " \n", 1034 | " \n", 1035 | " \n", 1036 | " \n", 1037 | " \n", 1038 | "
molecule_chembl_idmolecule_propertiesmw_freebase
0CHEMBL25{'alogp': '1.31', 'aromatic_rings': 1, 'cx_log...180.16
1CHEMBL38{'alogp': '5.60', 'aromatic_rings': 0, 'cx_log...300.44
2CHEMBL45{'alogp': '1.86', 'aromatic_rings': 2, 'cx_log...232.28
3CHEMBL481{'alogp': '4.09', 'aromatic_rings': 3, 'cx_log...586.69
4CHEMBL84{'alogp': '1.85', 'aromatic_rings': 3, 'cx_log...421.45
\n", 1039 | "
" 1040 | ], 1041 | "text/plain": [ 1042 | " molecule_chembl_id molecule_properties \\\n", 1043 | "0 CHEMBL25 {'alogp': '1.31', 'aromatic_rings': 1, 'cx_log... \n", 1044 | "1 CHEMBL38 {'alogp': '5.60', 'aromatic_rings': 0, 'cx_log... \n", 1045 | "2 CHEMBL45 {'alogp': '1.86', 'aromatic_rings': 2, 'cx_log... \n", 1046 | "3 CHEMBL481 {'alogp': '4.09', 'aromatic_rings': 3, 'cx_log... \n", 1047 | "4 CHEMBL84 {'alogp': '1.85', 'aromatic_rings': 3, 'cx_log... \n", 1048 | "\n", 1049 | " mw_freebase \n", 1050 | "0 180.16 \n", 1051 | "1 300.44 \n", 1052 | "2 232.28 \n", 1053 | "3 586.69 \n", 1054 | "4 421.45 " 1055 | ] 1056 | }, 1057 | "execution_count": 18, 1058 | "metadata": {}, 1059 | "output_type": "execute_result" 1060 | } 1061 | ], 1062 | "source": [ 1063 | "from chembl_webresource_client.new_client import new_client\n", 1064 | "import pandas as pd #Use pandas python module to view data as tables\n", 1065 | "\n", 1066 | "drug_indication = new_client.drug_indication\n", 1067 | "molecules = new_client.molecule\n", 1068 | "\n", 1069 | "lung_cancer_ind = drug_indication.filter(efo_term__icontains=\"LUNG CARCINOMA\", max_phase_for_ind = 3)\n", 1070 | "lung_cancer_phase3 = molecules.filter(molecule_chembl_id__in=[x['molecule_chembl_id'] for x in lung_cancer_ind]).only(['molecule_chembl_id','molecule_properties'])\n", 1071 | "lung_cancer_phase3 = pd.DataFrame(lung_cancer_phase3) #Convert results to dataframe\n", 1072 | "\n", 1073 | "#Expand out nested cells for 'mw_freebase':\n", 1074 | "lung_cancer_phase3['mw_freebase'] = lung_cancer_phase3.loc[ lung_cancer_phase3['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase']) #This is the mwt of the parent compound\n", 1075 | "\n", 1076 | "lung_cancer_phase3.head() #Display first 5 rows of result" 1077 | ] 1078 | }, 1079 | { 1080 | "cell_type": "markdown", 1081 | "metadata": {}, 1082 | "source": [ 1083 | "## What is the (therapeutic) mechanism of action for Sodium channel protein type V alpha subunit? For which drugs?" 1084 | ] 1085 | }, 1086 | { 1087 | "cell_type": "code", 1088 | "execution_count": 48, 1089 | "metadata": {}, 1090 | "outputs": [ 1091 | { 1092 | "data": { 1093 | "text/html": [ 1094 | "
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action_typemax_phasemechanism_commentmechanism_of_actionmolecule_chembl_idtarget_chembl_id
0BLOCKER4NoneSodium channel protein type V alpha subunit bl...CHEMBL1200822CHEMBL1980
1BLOCKER4NoneSodium channel protein type V alpha subunit bl...CHEMBL1200612CHEMBL1980
2BLOCKER4NoneSodium channel protein type V alpha subunit bl...CHEMBL1404CHEMBL1980
3BLOCKER2NoneSodium channel protein type V alpha subunit bl...CHEMBL3545040CHEMBL1980
4BLOCKER2L-type calcium channel (CACNA1C, CACNA1D, CACN...Sodium channel protein type V alpha subunit bl...CHEMBL3545169CHEMBL1980
5BLOCKER4Prenylamine is a potent sodium channel (NaCh) ...Sodium channel protein type V alpha subunit bl...CHEMBL24072CHEMBL1980
6BLOCKER3Also partially blocks the hERG channel.Sodium channel protein type V alpha subunit bl...CHEMBL2107383CHEMBL1980
\n", 1186 | "
" 1187 | ], 1188 | "text/plain": [ 1189 | " action_type max_phase mechanism_comment \\\n", 1190 | "0 BLOCKER 4 None \n", 1191 | "1 BLOCKER 4 None \n", 1192 | "2 BLOCKER 4 None \n", 1193 | "3 BLOCKER 2 None \n", 1194 | "4 BLOCKER 2 L-type calcium channel (CACNA1C, CACNA1D, CACN... \n", 1195 | "5 BLOCKER 4 Prenylamine is a potent sodium channel (NaCh) ... \n", 1196 | "6 BLOCKER 3 Also partially blocks the hERG channel. \n", 1197 | "\n", 1198 | " mechanism_of_action molecule_chembl_id \\\n", 1199 | "0 Sodium channel protein type V alpha subunit bl... CHEMBL1200822 \n", 1200 | "1 Sodium channel protein type V alpha subunit bl... CHEMBL1200612 \n", 1201 | "2 Sodium channel protein type V alpha subunit bl... CHEMBL1404 \n", 1202 | "3 Sodium channel protein type V alpha subunit bl... CHEMBL3545040 \n", 1203 | "4 Sodium channel protein type V alpha subunit bl... CHEMBL3545169 \n", 1204 | "5 Sodium channel protein type V alpha subunit bl... CHEMBL24072 \n", 1205 | "6 Sodium channel protein type V alpha subunit bl... CHEMBL2107383 \n", 1206 | "\n", 1207 | " target_chembl_id \n", 1208 | "0 CHEMBL1980 \n", 1209 | "1 CHEMBL1980 \n", 1210 | "2 CHEMBL1980 \n", 1211 | "3 CHEMBL1980 \n", 1212 | "4 CHEMBL1980 \n", 1213 | "5 CHEMBL1980 \n", 1214 | "6 CHEMBL1980 " 1215 | ] 1216 | }, 1217 | "execution_count": 48, 1218 | "metadata": {}, 1219 | "output_type": "execute_result" 1220 | } 1221 | ], 1222 | "source": [ 1223 | "from chembl_webresource_client.new_client import new_client\n", 1224 | "import pandas as pd #Use pandas python module to view data as tables\n", 1225 | "\n", 1226 | "mechanism = new_client.mechanism\n", 1227 | "res = mechanism.filter(target_chembl_id='CHEMBL1980' #Sodium channel protein type V alpha subunit target\n", 1228 | " ).only(['action_type','max_phase','mechanism_comment','mechanism_of_action','molecule_chembl_id', 'target_chembl_id'])\n", 1229 | "res = pd.DataFrame(res)\n", 1230 | "res" 1231 | ] 1232 | }, 1233 | { 1234 | "cell_type": "markdown", 1235 | "metadata": {}, 1236 | "source": [ 1237 | "## Find marketed drugs that have been subsequently withdrawn for hepatotoxicity reasons " 1238 | ] 1239 | }, 1240 | { 1241 | "cell_type": "code", 1242 | "execution_count": 19, 1243 | "metadata": {}, 1244 | "outputs": [ 1245 | { 1246 | "data": { 1247 | "text/html": [ 1248 | "
\n", 1249 | "\n", 1262 | "\n", 1263 | " \n", 1264 | " \n", 1265 | " \n", 1266 | " \n", 1267 | " \n", 1268 | " \n", 1269 | " \n", 1270 | " \n", 1271 | " \n", 1272 | " \n", 1273 | " \n", 1274 | " \n", 1275 | " \n", 1276 | " \n", 1277 | " \n", 1278 | " \n", 1279 | " \n", 1280 | " \n", 1281 | " \n", 1282 | " \n", 1283 | " \n", 1284 | " \n", 1285 | " \n", 1286 | " \n", 1287 | " \n", 1288 | " \n", 1289 | " \n", 1290 | " \n", 1291 | " \n", 1292 | " \n", 1293 | " \n", 1294 | " \n", 1295 | " \n", 1296 | " \n", 1297 | " \n", 1298 | " \n", 1299 | " \n", 1300 | " \n", 1301 | " \n", 1302 | " \n", 1303 | " \n", 1304 | " \n", 1305 | " \n", 1306 | " \n", 1307 | " \n", 1308 | " \n", 1309 | " \n", 1310 | " \n", 1311 | " \n", 1312 | " \n", 1313 | " \n", 1314 | " \n", 1315 | " \n", 1316 | " \n", 1317 | " \n", 1318 | " \n", 1319 | " \n", 1320 | " \n", 1321 | " \n", 1322 | " \n", 1323 | " \n", 1324 | " \n", 1325 | " \n", 1326 | " \n", 1327 | " \n", 1328 | " \n", 1329 | " \n", 1330 | " \n", 1331 | " \n", 1332 | " \n", 1333 | " \n", 1334 | " \n", 1335 | " \n", 1336 | " \n", 1337 | " \n", 1338 | " \n", 1339 | "
molecule_chembl_idparent_molecule_chembl_idwarning_classwarning_countrywarning_descriptionwarning_idwarning_refswarning_typewarning_year
0CHEMBL2105740CHEMBL282724HepatotoxicityEuropean UnionLiver toxicity2049[{'ref_id': 'Public_statement/2011/03/WC500102...Withdrawn2010
1CHEMBL1341CHEMBL1341HepatotoxicityUnited StatesSerious, irreversible, and even fatal nephroto...2059[{'ref_id': 'FR-2014-07-02/pdf/2014-15371.pdf'...Withdrawn2001
2CHEMBL3183453CHEMBL36506HepatotoxicityUnited StatesRelatively common skin reactions; Jaundice; He...2069[{'ref_id': 'FR-2014-07-02/pdf/2014-15371.pdf'...Withdrawn2009
3CHEMBL1200498CHEMBL1201197HepatotoxicityEuropean Union; United StatesLiver toxicity; Serious liver injury leading t...2070[{'ref_id': 'public-statement-trovan/trovan-iv...Withdrawn2006
4CHEMBL54349CHEMBL54349HepatotoxicityFranceHepatitis2076[{'ref_id': '10.1177/009286150103500134', 'ref...Withdrawn1993
\n", 1340 | "
" 1341 | ], 1342 | "text/plain": [ 1343 | " molecule_chembl_id parent_molecule_chembl_id warning_class \\\n", 1344 | "0 CHEMBL2105740 CHEMBL282724 Hepatotoxicity \n", 1345 | "1 CHEMBL1341 CHEMBL1341 Hepatotoxicity \n", 1346 | "2 CHEMBL3183453 CHEMBL36506 Hepatotoxicity \n", 1347 | "3 CHEMBL1200498 CHEMBL1201197 Hepatotoxicity \n", 1348 | "4 CHEMBL54349 CHEMBL54349 Hepatotoxicity \n", 1349 | "\n", 1350 | " warning_country \\\n", 1351 | "0 European Union \n", 1352 | "1 United States \n", 1353 | "2 United States \n", 1354 | "3 European Union; United States \n", 1355 | "4 France \n", 1356 | "\n", 1357 | " warning_description warning_id \\\n", 1358 | "0 Liver toxicity 2049 \n", 1359 | "1 Serious, irreversible, and even fatal nephroto... 2059 \n", 1360 | "2 Relatively common skin reactions; Jaundice; He... 2069 \n", 1361 | "3 Liver toxicity; Serious liver injury leading t... 2070 \n", 1362 | "4 Hepatitis 2076 \n", 1363 | "\n", 1364 | " warning_refs warning_type \\\n", 1365 | "0 [{'ref_id': 'Public_statement/2011/03/WC500102... Withdrawn \n", 1366 | "1 [{'ref_id': 'FR-2014-07-02/pdf/2014-15371.pdf'... Withdrawn \n", 1367 | "2 [{'ref_id': 'FR-2014-07-02/pdf/2014-15371.pdf'... Withdrawn \n", 1368 | "3 [{'ref_id': 'public-statement-trovan/trovan-iv... Withdrawn \n", 1369 | "4 [{'ref_id': '10.1177/009286150103500134', 'ref... Withdrawn \n", 1370 | "\n", 1371 | " warning_year \n", 1372 | "0 2010 \n", 1373 | "1 2001 \n", 1374 | "2 2009 \n", 1375 | "3 2006 \n", 1376 | "4 1993 " 1377 | ] 1378 | }, 1379 | "execution_count": 19, 1380 | "metadata": {}, 1381 | "output_type": "execute_result" 1382 | } 1383 | ], 1384 | "source": [ 1385 | "from chembl_webresource_client.new_client import new_client\n", 1386 | "import pandas as pd #Use pandas python module to view data as tables\n", 1387 | "\n", 1388 | "drug_warning = new_client.drug_warning\n", 1389 | "res = drug_warning.filter(warning_type='Withdrawn', warning_class='Hepatotoxicity')\n", 1390 | "res = pd.DataFrame(res)\n", 1391 | "res.head() #Display top five rows only" 1392 | ] 1393 | }, 1394 | { 1395 | "cell_type": "markdown", 1396 | "metadata": {}, 1397 | "source": [ 1398 | "## Find FDA drugs that carry a black box warning for cardiotoxicity" 1399 | ] 1400 | }, 1401 | { 1402 | "cell_type": "code", 1403 | "execution_count": 20, 1404 | "metadata": {}, 1405 | "outputs": [ 1406 | { 1407 | "data": { 1408 | "text/html": [ 1409 | "
\n", 1410 | "\n", 1423 | "\n", 1424 | " \n", 1425 | " \n", 1426 | " \n", 1427 | " \n", 1428 | " \n", 1429 | " \n", 1430 | " \n", 1431 | " \n", 1432 | " \n", 1433 | " \n", 1434 | " \n", 1435 | " \n", 1436 | " \n", 1437 | " \n", 1438 | " \n", 1439 | " \n", 1440 | " \n", 1441 | " \n", 1442 | " \n", 1443 | " \n", 1444 | " \n", 1445 | " \n", 1446 | " \n", 1447 | " \n", 1448 | " \n", 1449 | " \n", 1450 | " \n", 1451 | " \n", 1452 | " \n", 1453 | " \n", 1454 | " \n", 1455 | " \n", 1456 | " \n", 1457 | " \n", 1458 | " \n", 1459 | " \n", 1460 | " \n", 1461 | " \n", 1462 | " \n", 1463 | " \n", 1464 | " \n", 1465 | " \n", 1466 | " \n", 1467 | " \n", 1468 | " \n", 1469 | " \n", 1470 | " \n", 1471 | " \n", 1472 | " \n", 1473 | " \n", 1474 | " \n", 1475 | " \n", 1476 | " \n", 1477 | " \n", 1478 | " \n", 1479 | " \n", 1480 | " \n", 1481 | " \n", 1482 | " \n", 1483 | " \n", 1484 | " \n", 1485 | " \n", 1486 | " \n", 1487 | " \n", 1488 | " \n", 1489 | " \n", 1490 | " \n", 1491 | " \n", 1492 | " \n", 1493 | " \n", 1494 | " \n", 1495 | " \n", 1496 | " \n", 1497 | " \n", 1498 | " \n", 1499 | " \n", 1500 | " \n", 1501 | " \n", 1502 | " \n", 1503 | " \n", 1504 | " \n", 1505 | " \n", 1506 | " \n", 1507 | " \n", 1508 | " \n", 1509 | " \n", 1510 | " \n", 1511 | " \n", 1512 | " \n", 1513 | " \n", 1514 | " \n", 1515 | " \n", 1516 | " \n", 1517 | " \n", 1518 | " \n", 1519 | " \n", 1520 | " \n", 1521 | " \n", 1522 | " \n", 1523 | " \n", 1524 | " \n", 1525 | " \n", 1526 | " \n", 1527 | " \n", 1528 | " \n", 1529 | " \n", 1530 | " \n", 1531 | " \n", 1532 | " \n", 1533 | " \n", 1534 | " \n", 1535 | " \n", 1536 | " \n", 1537 | " \n", 1538 | " \n", 1539 | " \n", 1540 | " \n", 1541 | " \n", 1542 | " \n", 1543 | " \n", 1544 | " \n", 1545 | " \n", 1546 | " \n", 1547 | " \n", 1548 | " \n", 1549 | " \n", 1550 | " \n", 1551 | " \n", 1552 | " \n", 1553 | " \n", 1554 | " \n", 1555 | " \n", 1556 | " \n", 1557 | " \n", 1558 | " \n", 1559 | " \n", 1560 | " \n", 1561 | " \n", 1562 | " \n", 1563 | " \n", 1564 | " \n", 1565 | " \n", 1566 | " \n", 1567 | " \n", 1568 | " \n", 1569 | " \n", 1570 | " \n", 1571 | " \n", 1572 | "
molecule_chembl_idparent_molecule_chembl_idwarning_classwarning_countrywarning_descriptionwarning_idwarning_refswarning_typewarning_year
0CHEMBL270190CHEMBL270190CardiotoxicityUnited StatesNone81[{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442...Black Box WarningNone
1CHEMBL1083993CHEMBL633CardiotoxicityUnited StatesNone98[{'ref_id': '730039c2-0a32-4775-855d-98b2207e9...Black Box WarningNone
2CHEMBL501CHEMBL405CardiotoxicityUnited StatesNone109[{'ref_id': 'f469fb38-0380-4621-9db3-a4f429126...Black Box WarningNone
3CHEMBL2362016CHEMBL2362016CardiotoxicityUnited StatesNone135[{'ref_id': 'a482eccd-8837-47ea-904d-2f2c294d1...Black Box WarningNone
4CHEMBL24CHEMBL24CardiotoxicityUnited StatesNone145[{'ref_id': '06c0a04f-a77e-4871-9bb4-13abe2cbb...Black Box WarningNone
..............................
87CHEMBL1201132CHEMBL895CardiotoxicityUnited StatesNone2615[{'ref_id': '96128b54-24d6-88a3-e053-2a95a90a1...Black Box WarningNone
88CHEMBL1200890CHEMBL656CardiotoxicityUnited StatesNone2646[{'ref_id': '1c674d3e-c80a-47a5-a3ea-e76bb4f53...Black Box WarningNone
89CHEMBL2007641CHEMBL2007641CardiotoxicityUnited StatesNone2659[{'ref_id': '17f85d17-ab71-4f5b-9fe3-0b8c822f6...Black Box WarningNone
90CHEMBL2107874CHEMBL2107874CardiotoxicityUnited StatesNone2711[{'ref_id': '471baba2-7154-4488-9891-0db2f4679...Black Box WarningNone
91CHEMBL2107067CHEMBL2107067CardiotoxicityUnited StatesNone2767[{'ref_id': 'ed7b5d41-7475-4c10-99b9-b62b3434a...Black Box WarningNone
\n", 1573 | "

92 rows × 9 columns

\n", 1574 | "
" 1575 | ], 1576 | "text/plain": [ 1577 | " molecule_chembl_id parent_molecule_chembl_id warning_class \\\n", 1578 | "0 CHEMBL270190 CHEMBL270190 Cardiotoxicity \n", 1579 | "1 CHEMBL1083993 CHEMBL633 Cardiotoxicity \n", 1580 | "2 CHEMBL501 CHEMBL405 Cardiotoxicity \n", 1581 | "3 CHEMBL2362016 CHEMBL2362016 Cardiotoxicity \n", 1582 | "4 CHEMBL24 CHEMBL24 Cardiotoxicity \n", 1583 | ".. ... ... ... \n", 1584 | "87 CHEMBL1201132 CHEMBL895 Cardiotoxicity \n", 1585 | "88 CHEMBL1200890 CHEMBL656 Cardiotoxicity \n", 1586 | "89 CHEMBL2007641 CHEMBL2007641 Cardiotoxicity \n", 1587 | "90 CHEMBL2107874 CHEMBL2107874 Cardiotoxicity \n", 1588 | "91 CHEMBL2107067 CHEMBL2107067 Cardiotoxicity \n", 1589 | "\n", 1590 | " warning_country warning_description warning_id \\\n", 1591 | "0 United States None 81 \n", 1592 | "1 United States None 98 \n", 1593 | "2 United States None 109 \n", 1594 | "3 United States None 135 \n", 1595 | "4 United States None 145 \n", 1596 | ".. ... ... ... \n", 1597 | "87 United States None 2615 \n", 1598 | "88 United States None 2646 \n", 1599 | "89 United States None 2659 \n", 1600 | "90 United States None 2711 \n", 1601 | "91 United States None 2767 \n", 1602 | "\n", 1603 | " warning_refs warning_type \\\n", 1604 | "0 [{'ref_id': '77a67dc6-35d3-48ff-9d18-292d4d442... Black Box Warning \n", 1605 | "1 [{'ref_id': '730039c2-0a32-4775-855d-98b2207e9... Black Box Warning \n", 1606 | "2 [{'ref_id': 'f469fb38-0380-4621-9db3-a4f429126... Black Box Warning \n", 1607 | "3 [{'ref_id': 'a482eccd-8837-47ea-904d-2f2c294d1... Black Box Warning \n", 1608 | "4 [{'ref_id': '06c0a04f-a77e-4871-9bb4-13abe2cbb... Black Box Warning \n", 1609 | ".. ... ... \n", 1610 | "87 [{'ref_id': '96128b54-24d6-88a3-e053-2a95a90a1... Black Box Warning \n", 1611 | "88 [{'ref_id': '1c674d3e-c80a-47a5-a3ea-e76bb4f53... Black Box Warning \n", 1612 | "89 [{'ref_id': '17f85d17-ab71-4f5b-9fe3-0b8c822f6... Black Box Warning \n", 1613 | "90 [{'ref_id': '471baba2-7154-4488-9891-0db2f4679... Black Box Warning \n", 1614 | "91 [{'ref_id': 'ed7b5d41-7475-4c10-99b9-b62b3434a... Black Box Warning \n", 1615 | "\n", 1616 | " warning_year \n", 1617 | "0 None \n", 1618 | "1 None \n", 1619 | "2 None \n", 1620 | "3 None \n", 1621 | "4 None \n", 1622 | ".. ... \n", 1623 | "87 None \n", 1624 | "88 None \n", 1625 | "89 None \n", 1626 | "90 None \n", 1627 | "91 None \n", 1628 | "\n", 1629 | "[92 rows x 9 columns]" 1630 | ] 1631 | }, 1632 | "execution_count": 20, 1633 | "metadata": {}, 1634 | "output_type": "execute_result" 1635 | } 1636 | ], 1637 | "source": [ 1638 | "from chembl_webresource_client.new_client import new_client\n", 1639 | "import pandas as pd #Use pandas python module to view data as tables\n", 1640 | "\n", 1641 | "drug_warning = new_client.drug_warning\n", 1642 | "res = drug_warning.filter(warning_class__icontains='cardio', warning_type='Black Box Warning')\n", 1643 | "res = pd.DataFrame(res)\n", 1644 | "res" 1645 | ] 1646 | }, 1647 | { 1648 | "cell_type": "markdown", 1649 | "metadata": {}, 1650 | "source": [ 1651 | "# 3. Targets" 1652 | ] 1653 | }, 1654 | { 1655 | "cell_type": "markdown", 1656 | "metadata": {}, 1657 | "source": [ 1658 | "## Find a target by gene name" 1659 | ] 1660 | }, 1661 | { 1662 | "cell_type": "code", 1663 | "execution_count": 21, 1664 | "metadata": {}, 1665 | "outputs": [ 1666 | { 1667 | "name": "stdout", 1668 | "output_type": "stream", 1669 | "text": [ 1670 | "{'organism': 'Homo sapiens', 'pref_name': 'Bromodomain-containing protein 4', 'target_type': 'SINGLE PROTEIN'}\n", 1671 | "{'organism': 'Mus musculus', 'pref_name': 'Bromodomain-containing protein 4', 'target_type': 'SINGLE PROTEIN'}\n", 1672 | "{'organism': 'Homo sapiens', 'pref_name': 'BRD4/HDAC1', 'target_type': 'PROTEIN COMPLEX'}\n", 1673 | "{'organism': 'Homo sapiens', 'pref_name': 'Cereblon/Cullin-4A/Bromodomain-containing protein 4', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1674 | "{'organism': 'Homo sapiens', 'pref_name': 'Cereblon/Bromodomain-containing protein 4', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1675 | "{'organism': 'Homo sapiens', 'pref_name': 'von Hippel-Lindau disease tumor suppressor/Bromodomain-containing protein 4', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1676 | "{'organism': 'Homo sapiens', 'pref_name': 'Cereblon/DNA damage-binding protein 1/Bromodomain-containing protein 4', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1677 | "{'organism': 'Homo sapiens', 'pref_name': 'von Hippel-Lindau disease tumor suppressor/Elongin-B/Elongin-C/Bromodomain-containing protein 4', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1678 | "{'organism': 'Homo sapiens', 'pref_name': 'Bromodomain and extra-terminal motif (BET)', 'target_type': 'PROTEIN FAMILY'}\n", 1679 | "{'organism': 'Homo sapiens', 'pref_name': 'BRD4/E3 ubiquitin-protein ligase Mdm2', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n", 1680 | "{'organism': 'Homo sapiens', 'pref_name': 'BRD4/E3 ubiquitin-protein ligase XIAP', 'target_type': 'PROTEIN-PROTEIN INTERACTION'}\n" 1681 | ] 1682 | } 1683 | ], 1684 | "source": [ 1685 | "from chembl_webresource_client.new_client import new_client\n", 1686 | "\n", 1687 | "target = new_client.target\n", 1688 | "gene_name = 'BRD4'\n", 1689 | "res = target.filter(target_synonym__icontains=gene_name).only(['organism', 'pref_name', 'target_type'])\n", 1690 | "for i in res:\n", 1691 | " print(i)" 1692 | ] 1693 | }, 1694 | { 1695 | "cell_type": "markdown", 1696 | "metadata": {}, 1697 | "source": [ 1698 | "## Find a protein target using its Uniprot_id" 1699 | ] 1700 | }, 1701 | { 1702 | "cell_type": "code", 1703 | "execution_count": 22, 1704 | "metadata": {}, 1705 | "outputs": [ 1706 | { 1707 | "data": { 1708 | "text/html": [ 1709 | "
\n", 1710 | "\n", 1723 | "\n", 1724 | " \n", 1725 | " \n", 1726 | " \n", 1727 | " \n", 1728 | " \n", 1729 | " \n", 1730 | " \n", 1731 | " \n", 1732 | " \n", 1733 | " \n", 1734 | " \n", 1735 | " \n", 1736 | " \n", 1737 | " \n", 1738 | " \n", 1739 | " \n", 1740 | " \n", 1741 | " \n", 1742 | " \n", 1743 | " \n", 1744 | " \n", 1745 | " \n", 1746 | " \n", 1747 | " \n", 1748 | " \n", 1749 | " \n", 1750 | " \n", 1751 | " \n", 1752 | " \n", 1753 | " \n", 1754 | " \n", 1755 | " \n", 1756 | " \n", 1757 | " \n", 1758 | " \n", 1759 | " \n", 1760 | " \n", 1761 | " \n", 1762 | " \n", 1763 | " \n", 1764 | " \n", 1765 | " \n", 1766 | " \n", 1767 | " \n", 1768 | " \n", 1769 | " \n", 1770 | " \n", 1771 | " \n", 1772 | " \n", 1773 | " \n", 1774 | " \n", 1775 | " \n", 1776 | " \n", 1777 | " \n", 1778 | " \n", 1779 | " \n", 1780 | " \n", 1781 | " \n", 1782 | " \n", 1783 | "
cross_referencesorganismpref_namespecies_group_flagtarget_chembl_idtarget_componentstarget_typetax_id
0[{'xref_id': 'FLT4', 'xref_name': None, 'xref_...Homo sapiensVascular endothelial growth factor receptor 3FalseCHEMBL1955[{'accession': 'P35916', 'component_descriptio...SINGLE PROTEIN9606
1[]Homo sapiensVascular endothelial growth factor receptorFalseCHEMBL2095227[{'accession': 'P17948', 'component_descriptio...PROTEIN FAMILY9606
2[]Homo sapiensVascular endothelial growth factor receptor 2 ...FalseCHEMBL2111409[{'accession': 'P35916', 'component_descriptio...SELECTIVITY GROUP9606
3[]Homo sapiensFocal adhesion kinase 1/vascular endothelial g...FalseCHEMBL3301389[{'accession': 'P35916', 'component_descriptio...PROTEIN-PROTEIN INTERACTION9606
\n", 1784 | "
" 1785 | ], 1786 | "text/plain": [ 1787 | " cross_references organism \\\n", 1788 | "0 [{'xref_id': 'FLT4', 'xref_name': None, 'xref_... Homo sapiens \n", 1789 | "1 [] Homo sapiens \n", 1790 | "2 [] Homo sapiens \n", 1791 | "3 [] Homo sapiens \n", 1792 | "\n", 1793 | " pref_name species_group_flag \\\n", 1794 | "0 Vascular endothelial growth factor receptor 3 False \n", 1795 | "1 Vascular endothelial growth factor receptor False \n", 1796 | "2 Vascular endothelial growth factor receptor 2 ... False \n", 1797 | "3 Focal adhesion kinase 1/vascular endothelial g... False \n", 1798 | "\n", 1799 | " target_chembl_id target_components \\\n", 1800 | "0 CHEMBL1955 [{'accession': 'P35916', 'component_descriptio... \n", 1801 | "1 CHEMBL2095227 [{'accession': 'P17948', 'component_descriptio... \n", 1802 | "2 CHEMBL2111409 [{'accession': 'P35916', 'component_descriptio... \n", 1803 | "3 CHEMBL3301389 [{'accession': 'P35916', 'component_descriptio... \n", 1804 | "\n", 1805 | " target_type tax_id \n", 1806 | "0 SINGLE PROTEIN 9606 \n", 1807 | "1 PROTEIN FAMILY 9606 \n", 1808 | "2 SELECTIVITY GROUP 9606 \n", 1809 | "3 PROTEIN-PROTEIN INTERACTION 9606 " 1810 | ] 1811 | }, 1812 | "execution_count": 22, 1813 | "metadata": {}, 1814 | "output_type": "execute_result" 1815 | } 1816 | ], 1817 | "source": [ 1818 | "from chembl_webresource_client.new_client import new_client\n", 1819 | "\n", 1820 | "target = new_client.target\n", 1821 | "uniprot_id = 'P35916' #Vascular endothelial growth factor receptor 3; Uniprot accession P35916\n", 1822 | "res = target.filter(target_components__accession=uniprot_id)\n", 1823 | "\n", 1824 | "res = pd.DataFrame(res)\n", 1825 | "res" 1826 | ] 1827 | }, 1828 | { 1829 | "cell_type": "markdown", 1830 | "metadata": {}, 1831 | "source": [ 1832 | "## Which species have available data in ChEMBL for cannabinoid receptor target(s)? What target types are they?" 1833 | ] 1834 | }, 1835 | { 1836 | "cell_type": "code", 1837 | "execution_count": 23, 1838 | "metadata": {}, 1839 | "outputs": [ 1840 | { 1841 | "data": { 1842 | "text/html": [ 1843 | "
\n", 1844 | "\n", 1857 | "\n", 1858 | " \n", 1859 | " \n", 1860 | " \n", 1861 | " \n", 1862 | " \n", 1863 | " \n", 1864 | " \n", 1865 | " \n", 1866 | " \n", 1867 | " \n", 1868 | " \n", 1869 | " \n", 1870 | " \n", 1871 | " \n", 1872 | " \n", 1873 | " \n", 1874 | " \n", 1875 | " \n", 1876 | " \n", 1877 | " \n", 1878 | " \n", 1879 | " \n", 1880 | " \n", 1881 | " \n", 1882 | " \n", 1883 | " \n", 1884 | " \n", 1885 | " \n", 1886 | " \n", 1887 | " \n", 1888 | " \n", 1889 | " \n", 1890 | " \n", 1891 | " \n", 1892 | " \n", 1893 | " \n", 1894 | " \n", 1895 | " \n", 1896 | " \n", 1897 | " \n", 1898 | " \n", 1899 | " \n", 1900 | " \n", 1901 | " \n", 1902 | " \n", 1903 | " \n", 1904 | " \n", 1905 | " \n", 1906 | " \n", 1907 | " \n", 1908 | " \n", 1909 | " \n", 1910 | " \n", 1911 | " \n", 1912 | " \n", 1913 | " \n", 1914 | " \n", 1915 | " \n", 1916 | " \n", 1917 | " \n", 1918 | " \n", 1919 | " \n", 1920 | " \n", 1921 | " \n", 1922 | " \n", 1923 | " \n", 1924 | " \n", 1925 | " \n", 1926 | " \n", 1927 | " \n", 1928 | " \n", 1929 | " \n", 1930 | " \n", 1931 | " \n", 1932 | " \n", 1933 | " \n", 1934 | " \n", 1935 | " \n", 1936 | " \n", 1937 | " \n", 1938 | " \n", 1939 | " \n", 1940 | " \n", 1941 | " \n", 1942 | " \n", 1943 | " \n", 1944 | " \n", 1945 | " \n", 1946 | "
organismpref_nametarget_chembl_idtarget_type
0Homo sapiensCannabinoid CB1 receptorCHEMBL218SINGLE PROTEIN
1Mus musculusCannabinoid CB1 receptorCHEMBL3037SINGLE PROTEIN
2Rattus norvegicusCannabinoid CB1 receptorCHEMBL3571SINGLE PROTEIN
3Homo sapiensCannabinoid CB1 receptor/orexin receptor 1 com...CHEMBL3301387PROTEIN COMPLEX
4Homo sapiensCannabinoid CB2 receptorCHEMBL253SINGLE PROTEIN
5Mus musculusCannabinoid CB2 receptorCHEMBL5373SINGLE PROTEIN
6Rattus norvegicusCannabinoid CB2 receptorCHEMBL2470SINGLE PROTEIN
7Homo sapiensCannabinoid receptorCHEMBL2096981PROTEIN FAMILY
8Mus musculusCannabinoid receptorCHEMBL2111415PROTEIN FAMILY
9Rattus norvegicusCannabinoid receptorCHEMBL2111385PROTEIN FAMILY
10Homo sapiensCannabinoid receptor 1/Mu-type opioid receptorCHEMBL3885538PROTEIN COMPLEX
\n", 1947 | "
" 1948 | ], 1949 | "text/plain": [ 1950 | " organism pref_name \\\n", 1951 | "0 Homo sapiens Cannabinoid CB1 receptor \n", 1952 | "1 Mus musculus Cannabinoid CB1 receptor \n", 1953 | "2 Rattus norvegicus Cannabinoid CB1 receptor \n", 1954 | "3 Homo sapiens Cannabinoid CB1 receptor/orexin receptor 1 com... \n", 1955 | "4 Homo sapiens Cannabinoid CB2 receptor \n", 1956 | "5 Mus musculus Cannabinoid CB2 receptor \n", 1957 | "6 Rattus norvegicus Cannabinoid CB2 receptor \n", 1958 | "7 Homo sapiens Cannabinoid receptor \n", 1959 | "8 Mus musculus Cannabinoid receptor \n", 1960 | "9 Rattus norvegicus Cannabinoid receptor \n", 1961 | "10 Homo sapiens Cannabinoid receptor 1/Mu-type opioid receptor \n", 1962 | "\n", 1963 | " target_chembl_id target_type \n", 1964 | "0 CHEMBL218 SINGLE PROTEIN \n", 1965 | "1 CHEMBL3037 SINGLE PROTEIN \n", 1966 | "2 CHEMBL3571 SINGLE PROTEIN \n", 1967 | "3 CHEMBL3301387 PROTEIN COMPLEX \n", 1968 | "4 CHEMBL253 SINGLE PROTEIN \n", 1969 | "5 CHEMBL5373 SINGLE PROTEIN \n", 1970 | "6 CHEMBL2470 SINGLE PROTEIN \n", 1971 | "7 CHEMBL2096981 PROTEIN FAMILY \n", 1972 | "8 CHEMBL2111415 PROTEIN FAMILY \n", 1973 | "9 CHEMBL2111385 PROTEIN FAMILY \n", 1974 | "10 CHEMBL3885538 PROTEIN COMPLEX " 1975 | ] 1976 | }, 1977 | "execution_count": 23, 1978 | "metadata": {}, 1979 | "output_type": "execute_result" 1980 | } 1981 | ], 1982 | "source": [ 1983 | "from chembl_webresource_client.new_client import new_client\n", 1984 | "import pandas as pd #Use pandas python module to view data as tables\n", 1985 | "\n", 1986 | "target = new_client.target\n", 1987 | "protein_name = 'cannabinoid' \n", 1988 | "res = target.filter(pref_name__icontains=protein_name).only(['organism','pref_name','target_chembl_id','target_type']).order_by(['pref_name','organism'])\n", 1989 | "\n", 1990 | "pd.DataFrame(res)" 1991 | ] 1992 | }, 1993 | { 1994 | "cell_type": "markdown", 1995 | "metadata": {}, 1996 | "source": [ 1997 | "## What does the protein classification look like for chemokine receptor targets?" 1998 | ] 1999 | }, 2000 | { 2001 | "cell_type": "code", 2002 | "execution_count": 24, 2003 | "metadata": {}, 2004 | "outputs": [ 2005 | { 2006 | "data": { 2007 | "text/html": [ 2008 | "
\n", 2009 | "\n", 2022 | "\n", 2023 | " \n", 2024 | " \n", 2025 | " \n", 2026 | " \n", 2027 | " \n", 2028 | " \n", 2029 | " \n", 2030 | " \n", 2031 | " \n", 2032 | " \n", 2033 | " \n", 2034 | " \n", 2035 | " \n", 2036 | " \n", 2037 | " \n", 2038 | " \n", 2039 | " \n", 2040 | " \n", 2041 | " \n", 2042 | " \n", 2043 | " \n", 2044 | " \n", 2045 | " \n", 2046 | " \n", 2047 | " \n", 2048 | " \n", 2049 | " \n", 2050 | " \n", 2051 | " \n", 2052 | " \n", 2053 | " \n", 2054 | " \n", 2055 | " \n", 2056 | " \n", 2057 | " \n", 2058 | " \n", 2059 | " \n", 2060 | " \n", 2061 | " \n", 2062 | " \n", 2063 | " \n", 2064 | " \n", 2065 | " \n", 2066 | " \n", 2067 | " \n", 2068 | " \n", 2069 | " \n", 2070 | " \n", 2071 | " \n", 2072 | " \n", 2073 | " \n", 2074 | " \n", 2075 | " \n", 2076 | " \n", 2077 | " \n", 2078 | " \n", 2079 | " \n", 2080 | " \n", 2081 | " \n", 2082 | " \n", 2083 | " \n", 2084 | " \n", 2085 | " \n", 2086 | " \n", 2087 | " \n", 2088 | " \n", 2089 | " \n", 2090 | " \n", 2091 | " \n", 2092 | " \n", 2093 | " \n", 2094 | " \n", 2095 | " \n", 2096 | " \n", 2097 | " \n", 2098 | " \n", 2099 | " \n", 2100 | " \n", 2101 | " \n", 2102 | " \n", 2103 | " \n", 2104 | " \n", 2105 | " \n", 2106 | " \n", 2107 | " \n", 2108 | " \n", 2109 | " \n", 2110 | " \n", 2111 | " \n", 2112 | " \n", 2113 | " \n", 2114 | " \n", 2115 | " \n", 2116 | " \n", 2117 | " \n", 2118 | " \n", 2119 | " \n", 2120 | " \n", 2121 | " \n", 2122 | " \n", 2123 | "
l1l2l3l4l5l6l7l8protein_class_id
0Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptorCC chemokine receptorNoneNoneNone547
1Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptorXC chemokine receptorNoneNoneNone548
2Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptorCXC chemokine receptorNoneNoneNone554
3Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptor-likeChemerin receptorNoneNoneNone555
4Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptorCX3C chemokine receptorNoneNoneNone558
5Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptorNoneNoneNoneNone1265
6Membrane receptorFamily A G protein-coupled receptorPeptide receptor (family A GPCR)Chemokine receptor-likeNoneNoneNoneNone1270
\n", 2124 | "
" 2125 | ], 2126 | "text/plain": [ 2127 | " l1 l2 \\\n", 2128 | "0 Membrane receptor Family A G protein-coupled receptor \n", 2129 | "1 Membrane receptor Family A G protein-coupled receptor \n", 2130 | "2 Membrane receptor Family A G protein-coupled receptor \n", 2131 | "3 Membrane receptor Family A G protein-coupled receptor \n", 2132 | "4 Membrane receptor Family A G protein-coupled receptor \n", 2133 | "5 Membrane receptor Family A G protein-coupled receptor \n", 2134 | "6 Membrane receptor Family A G protein-coupled receptor \n", 2135 | "\n", 2136 | " l3 l4 \\\n", 2137 | "0 Peptide receptor (family A GPCR) Chemokine receptor \n", 2138 | "1 Peptide receptor (family A GPCR) Chemokine receptor \n", 2139 | "2 Peptide receptor (family A GPCR) Chemokine receptor \n", 2140 | "3 Peptide receptor (family A GPCR) Chemokine receptor-like \n", 2141 | "4 Peptide receptor (family A GPCR) Chemokine receptor \n", 2142 | "5 Peptide receptor (family A GPCR) Chemokine receptor \n", 2143 | "6 Peptide receptor (family A GPCR) Chemokine receptor-like \n", 2144 | "\n", 2145 | " l5 l6 l7 l8 protein_class_id \n", 2146 | "0 CC chemokine receptor None None None 547 \n", 2147 | "1 XC chemokine receptor None None None 548 \n", 2148 | "2 CXC chemokine receptor None None None 554 \n", 2149 | "3 Chemerin receptor None None None 555 \n", 2150 | "4 CX3C chemokine receptor None None None 558 \n", 2151 | "5 None None None None 1265 \n", 2152 | "6 None None None None 1270 " 2153 | ] 2154 | }, 2155 | "execution_count": 24, 2156 | "metadata": {}, 2157 | "output_type": "execute_result" 2158 | } 2159 | ], 2160 | "source": [ 2161 | "from chembl_webresource_client.new_client import new_client\n", 2162 | "import pandas as pd #Use pandas python module to view data as tables\n", 2163 | "\n", 2164 | "protein_class = new_client.protein_class\n", 2165 | "res = protein_class.filter(l4__icontains='chemokine receptor')\n", 2166 | "\n", 2167 | "pd.DataFrame(res)" 2168 | ] 2169 | }, 2170 | { 2171 | "cell_type": "markdown", 2172 | "metadata": {}, 2173 | "source": [ 2174 | "# 4. Activities\n", 2175 | "- The `activitity` API endpoint provides bioactivity data for compounds that have been measured against target(s) in an assay" 2176 | ] 2177 | }, 2178 | { 2179 | "cell_type": "markdown", 2180 | "metadata": {}, 2181 | "source": [ 2182 | "## Get all IC50 activities related to the hERG target" 2183 | ] 2184 | }, 2185 | { 2186 | "cell_type": "code", 2187 | "execution_count": 25, 2188 | "metadata": {}, 2189 | "outputs": [ 2190 | { 2191 | "data": { 2192 | "text/plain": [ 2193 | "13200" 2194 | ] 2195 | }, 2196 | "execution_count": 25, 2197 | "metadata": {}, 2198 | "output_type": "execute_result" 2199 | } 2200 | ], 2201 | "source": [ 2202 | "from chembl_webresource_client.new_client import new_client\n", 2203 | "\n", 2204 | "target = new_client.target\n", 2205 | "activity = new_client.activity\n", 2206 | "herg = target.filter(pref_name__iexact='hERG').only('target_chembl_id')[0]\n", 2207 | "herg_activities = activity.filter(target_chembl_id=herg['target_chembl_id']).filter(standard_type=\"IC50\")\n", 2208 | "\n", 2209 | "len(herg_activities)" 2210 | ] 2211 | }, 2212 | { 2213 | "cell_type": "markdown", 2214 | "metadata": {}, 2215 | "source": [ 2216 | "## Get all activities for a specific target with assay type B (binding):" 2217 | ] 2218 | }, 2219 | { 2220 | "cell_type": "code", 2221 | "execution_count": 26, 2222 | "metadata": {}, 2223 | "outputs": [ 2224 | { 2225 | "data": { 2226 | "text/plain": [ 2227 | "860" 2228 | ] 2229 | }, 2230 | "execution_count": 26, 2231 | "metadata": {}, 2232 | "output_type": "execute_result" 2233 | } 2234 | ], 2235 | "source": [ 2236 | "from chembl_webresource_client.new_client import new_client\n", 2237 | "\n", 2238 | "activity = new_client.activity\n", 2239 | "res = activity.filter(target_chembl_id='CHEMBL3938', assay_type='B')\n", 2240 | "\n", 2241 | "len(res)" 2242 | ] 2243 | }, 2244 | { 2245 | "cell_type": "markdown", 2246 | "metadata": {}, 2247 | "source": [ 2248 | "## Get all activities with a pChEMBL value for Aspirin" 2249 | ] 2250 | }, 2251 | { 2252 | "cell_type": "code", 2253 | "execution_count": 27, 2254 | "metadata": {}, 2255 | "outputs": [ 2256 | { 2257 | "data": { 2258 | "text/plain": [ 2259 | "138" 2260 | ] 2261 | }, 2262 | "execution_count": 27, 2263 | "metadata": {}, 2264 | "output_type": "execute_result" 2265 | } 2266 | ], 2267 | "source": [ 2268 | "from chembl_webresource_client.new_client import new_client\n", 2269 | "\n", 2270 | "activities = new_client.activity\n", 2271 | "res = activities.filter(molecule_chembl_id=\"CHEMBL25\", pchembl_value__isnull=False)\n", 2272 | "\n", 2273 | "len(res)" 2274 | ] 2275 | }, 2276 | { 2277 | "cell_type": "markdown", 2278 | "metadata": {}, 2279 | "source": [ 2280 | "# 5. Assays" 2281 | ] 2282 | }, 2283 | { 2284 | "cell_type": "markdown", 2285 | "metadata": {}, 2286 | "source": [ 2287 | "## Search for ADMET-related inhibitor assays (assay_type A) measured in Rat" 2288 | ] 2289 | }, 2290 | { 2291 | "cell_type": "code", 2292 | "execution_count": 28, 2293 | "metadata": {}, 2294 | "outputs": [ 2295 | { 2296 | "data": { 2297 | "text/html": [ 2298 | "
\n", 2299 | "\n", 2312 | "\n", 2313 | " \n", 2314 | " \n", 2315 | " \n", 2316 | " \n", 2317 | " \n", 2318 | " \n", 2319 | " \n", 2320 | " \n", 2321 | " \n", 2322 | " \n", 2323 | " \n", 2324 | " \n", 2325 | " \n", 2326 | " \n", 2327 | " \n", 2328 | " \n", 2329 | " \n", 2330 | " \n", 2331 | " \n", 2332 | " \n", 2333 | " \n", 2334 | " \n", 2335 | " \n", 2336 | " \n", 2337 | " \n", 2338 | " \n", 2339 | " \n", 2340 | " \n", 2341 | " \n", 2342 | " \n", 2343 | " \n", 2344 | " \n", 2345 | " \n", 2346 | " \n", 2347 | " \n", 2348 | " \n", 2349 | " \n", 2350 | " \n", 2351 | " \n", 2352 | " \n", 2353 | " \n", 2354 | " \n", 2355 | " \n", 2356 | " \n", 2357 | " \n", 2358 | " \n", 2359 | " \n", 2360 | " \n", 2361 | " \n", 2362 | " \n", 2363 | " \n", 2364 | " \n", 2365 | " \n", 2366 | " \n", 2367 | " \n", 2368 | " \n", 2369 | " \n", 2370 | " \n", 2371 | " \n", 2372 | " \n", 2373 | " \n", 2374 | " \n", 2375 | " \n", 2376 | " \n", 2377 | " \n", 2378 | " \n", 2379 | " \n", 2380 | " \n", 2381 | " \n", 2382 | " \n", 2383 | " \n", 2384 | " \n", 2385 | " \n", 2386 | " \n", 2387 | " \n", 2388 | " \n", 2389 | "
assay_chembl_idassay_organismdescription
0CHEMBL884521Rattus norvegicusInhibition of cytochrome P450 progesterone 15-...
1CHEMBL615148Rattus norvegicusInhibition of cytochrome P450 progesterone 16-...
2CHEMBL615199Rattus norvegicusInhibition of cytochrome P450 progesterone 2-a...
3CHEMBL883800Rattus norvegicusInhibition of progesterone 6-beta-hydroxylase ...
4CHEMBL618439Rattus norvegicusInhibition of progesterone 6-beta-hydroxylase ...
............
582CHEMBL4626205Rattus norvegicusInhibition of rat adrenergic alpha1A receptor ...
583CHEMBL4630381Rattus norvegicusDrug metabolism in selective CYP3A inhibitor K...
584CHEMBL4630382Rattus norvegicusDrug metabolism in SULT inhibitor DCNP pre-tre...
585CHEMBL4630392Rattus norvegicusDrug metabolism in selective CYP3A inhibitor K...
586CHEMBL4630393Rattus norvegicusDrug metabolism in selective SULT inhibitor DC...
\n", 2390 | "

587 rows × 3 columns

\n", 2391 | "
" 2392 | ], 2393 | "text/plain": [ 2394 | " assay_chembl_id assay_organism \\\n", 2395 | "0 CHEMBL884521 Rattus norvegicus \n", 2396 | "1 CHEMBL615148 Rattus norvegicus \n", 2397 | "2 CHEMBL615199 Rattus norvegicus \n", 2398 | "3 CHEMBL883800 Rattus norvegicus \n", 2399 | "4 CHEMBL618439 Rattus norvegicus \n", 2400 | ".. ... ... \n", 2401 | "582 CHEMBL4626205 Rattus norvegicus \n", 2402 | "583 CHEMBL4630381 Rattus norvegicus \n", 2403 | "584 CHEMBL4630382 Rattus norvegicus \n", 2404 | "585 CHEMBL4630392 Rattus norvegicus \n", 2405 | "586 CHEMBL4630393 Rattus norvegicus \n", 2406 | "\n", 2407 | " description \n", 2408 | "0 Inhibition of cytochrome P450 progesterone 15-... \n", 2409 | "1 Inhibition of cytochrome P450 progesterone 16-... \n", 2410 | "2 Inhibition of cytochrome P450 progesterone 2-a... \n", 2411 | "3 Inhibition of progesterone 6-beta-hydroxylase ... \n", 2412 | "4 Inhibition of progesterone 6-beta-hydroxylase ... \n", 2413 | ".. ... \n", 2414 | "582 Inhibition of rat adrenergic alpha1A receptor ... \n", 2415 | "583 Drug metabolism in selective CYP3A inhibitor K... \n", 2416 | "584 Drug metabolism in SULT inhibitor DCNP pre-tre... \n", 2417 | "585 Drug metabolism in selective CYP3A inhibitor K... \n", 2418 | "586 Drug metabolism in selective SULT inhibitor DC... \n", 2419 | "\n", 2420 | "[587 rows x 3 columns]" 2421 | ] 2422 | }, 2423 | "execution_count": 28, 2424 | "metadata": {}, 2425 | "output_type": "execute_result" 2426 | } 2427 | ], 2428 | "source": [ 2429 | "from chembl_webresource_client.new_client import new_client\n", 2430 | "import pandas as pd #Use pandas python module to view data as tables\n", 2431 | "\n", 2432 | "assay = new_client.assay\n", 2433 | "res = assay.filter(description__icontains='inhibit', assay_type='A', assay_organism='Rattus norvegicus').only(['assay_chembl_id','assay_organism','description'])\n", 2434 | "pd.DataFrame(res)" 2435 | ] 2436 | }, 2437 | { 2438 | "cell_type": "markdown", 2439 | "metadata": {}, 2440 | "source": [ 2441 | "## Find assays that assess nephrotoxicity (kidney toxicity)\n", 2442 | "- Note that nephrotoxic drugs could be searched for using the `drug_warning` API endpoint" 2443 | ] 2444 | }, 2445 | { 2446 | "cell_type": "code", 2447 | "execution_count": 29, 2448 | "metadata": {}, 2449 | "outputs": [ 2450 | { 2451 | "data": { 2452 | "text/html": [ 2453 | "
\n", 2454 | "\n", 2467 | "\n", 2468 | " \n", 2469 | " \n", 2470 | " \n", 2471 | " \n", 2472 | " \n", 2473 | " \n", 2474 | " \n", 2475 | " \n", 2476 | " \n", 2477 | " \n", 2478 | " \n", 2479 | " \n", 2480 | " \n", 2481 | " \n", 2482 | " \n", 2483 | " \n", 2484 | " \n", 2485 | " \n", 2486 | " \n", 2487 | " \n", 2488 | " \n", 2489 | " \n", 2490 | " \n", 2491 | " \n", 2492 | " \n", 2493 | " \n", 2494 | " \n", 2495 | " \n", 2496 | " \n", 2497 | " \n", 2498 | " \n", 2499 | " \n", 2500 | " \n", 2501 | " \n", 2502 | " \n", 2503 | " \n", 2504 | " \n", 2505 | " \n", 2506 | " \n", 2507 | " \n", 2508 | " \n", 2509 | " \n", 2510 | " \n", 2511 | " \n", 2512 | " \n", 2513 | " \n", 2514 | " \n", 2515 | " \n", 2516 | " \n", 2517 | " \n", 2518 | " \n", 2519 | " \n", 2520 | " \n", 2521 | " \n", 2522 | " \n", 2523 | " \n", 2524 | " \n", 2525 | " \n", 2526 | " \n", 2527 | " \n", 2528 | " \n", 2529 | " \n", 2530 | " \n", 2531 | " \n", 2532 | " \n", 2533 | " \n", 2534 | " \n", 2535 | " \n", 2536 | " \n", 2537 | " \n", 2538 | " \n", 2539 | " \n", 2540 | " \n", 2541 | " \n", 2542 | " \n", 2543 | " \n", 2544 | "
assay_chembl_idassay_organismdescription
0CHEMBL730330Mus musculusNephrotoxicity upon intragastric administratio...
1CHEMBL730331Mus musculusNephrotoxicity upon intragastric administratio...
2CHEMBL729614Mus musculusNephrotoxicity upon intragastric administratio...
3CHEMBL729615Mus musculusNephrotoxicity upon intragastric administratio...
4CHEMBL729616Mus musculusNephrotoxicity upon intragastric administratio...
............
349CHEMBL4422918Mus musculusNephrotoxicity in C57BL/6 mouse assessed as ab...
350CHEMBL4422919Mus musculusNephrotoxicity in C57BL/6 mouse assessed as ti...
351CHEMBL4480539Rattus norvegicusRenal toxicity in Wistar rat assessed as effec...
352CHEMBL4623569Homo sapiensNephrotoxicity in human patients assessed as p...
353CHEMBL4626515Mus musculusRenal toxicity in C57BL/6 mouse assessed effec...
\n", 2545 | "

354 rows × 3 columns

\n", 2546 | "
" 2547 | ], 2548 | "text/plain": [ 2549 | " assay_chembl_id assay_organism \\\n", 2550 | "0 CHEMBL730330 Mus musculus \n", 2551 | "1 CHEMBL730331 Mus musculus \n", 2552 | "2 CHEMBL729614 Mus musculus \n", 2553 | "3 CHEMBL729615 Mus musculus \n", 2554 | "4 CHEMBL729616 Mus musculus \n", 2555 | ".. ... ... \n", 2556 | "349 CHEMBL4422918 Mus musculus \n", 2557 | "350 CHEMBL4422919 Mus musculus \n", 2558 | "351 CHEMBL4480539 Rattus norvegicus \n", 2559 | "352 CHEMBL4623569 Homo sapiens \n", 2560 | "353 CHEMBL4626515 Mus musculus \n", 2561 | "\n", 2562 | " description \n", 2563 | "0 Nephrotoxicity upon intragastric administratio... \n", 2564 | "1 Nephrotoxicity upon intragastric administratio... \n", 2565 | "2 Nephrotoxicity upon intragastric administratio... \n", 2566 | "3 Nephrotoxicity upon intragastric administratio... \n", 2567 | "4 Nephrotoxicity upon intragastric administratio... \n", 2568 | ".. ... \n", 2569 | "349 Nephrotoxicity in C57BL/6 mouse assessed as ab... \n", 2570 | "350 Nephrotoxicity in C57BL/6 mouse assessed as ti... \n", 2571 | "351 Renal toxicity in Wistar rat assessed as effec... \n", 2572 | "352 Nephrotoxicity in human patients assessed as p... \n", 2573 | "353 Renal toxicity in C57BL/6 mouse assessed effec... \n", 2574 | "\n", 2575 | "[354 rows x 3 columns]" 2576 | ] 2577 | }, 2578 | "execution_count": 29, 2579 | "metadata": {}, 2580 | "output_type": "execute_result" 2581 | } 2582 | ], 2583 | "source": [ 2584 | "from chembl_webresource_client.new_client import new_client\n", 2585 | "import pandas as pd #Use pandas python module to view data as tables\n", 2586 | "\n", 2587 | "assay = new_client.assay\n", 2588 | "res = assay.filter(description__iregex='nephrotoxicity|renal toxicity').only(['assay_chembl_id','assay_organism','description'])\n", 2589 | "pd.DataFrame(res)" 2590 | ] 2591 | }, 2592 | { 2593 | "cell_type": "markdown", 2594 | "metadata": {}, 2595 | "source": [ 2596 | "# Tissues" 2597 | ] 2598 | }, 2599 | { 2600 | "cell_type": "markdown", 2601 | "metadata": {}, 2602 | "source": [ 2603 | "## Get tissue by Uberon id (UBERON is a cross-species anatomy ontology)" 2604 | ] 2605 | }, 2606 | { 2607 | "cell_type": "code", 2608 | "execution_count": 30, 2609 | "metadata": {}, 2610 | "outputs": [ 2611 | { 2612 | "data": { 2613 | "text/plain": [ 2614 | "[{'bto_id': 'BTO:0000068', 'caloha_id': 'TS-0034', 'efo_id': None, 'pref_name': 'Amniotic fluid', 'tissue_chembl_id': 'CHEMBL3638177', 'uberon_id': 'UBERON:0000173'}]" 2615 | ] 2616 | }, 2617 | "execution_count": 30, 2618 | "metadata": {}, 2619 | "output_type": "execute_result" 2620 | } 2621 | ], 2622 | "source": [ 2623 | "from chembl_webresource_client.new_client import new_client\n", 2624 | "\n", 2625 | "tissue = new_client.tissue\n", 2626 | "res = tissue.filter(uberon_id=\"UBERON:0000173\")\n", 2627 | "res" 2628 | ] 2629 | }, 2630 | { 2631 | "cell_type": "markdown", 2632 | "metadata": {}, 2633 | "source": [ 2634 | "## Get tissue by name" 2635 | ] 2636 | }, 2637 | { 2638 | "cell_type": "code", 2639 | "execution_count": 31, 2640 | "metadata": {}, 2641 | "outputs": [ 2642 | { 2643 | "data": { 2644 | "text/html": [ 2645 | "
\n", 2646 | "\n", 2659 | "\n", 2660 | " \n", 2661 | " \n", 2662 | " \n", 2663 | " \n", 2664 | " \n", 2665 | " \n", 2666 | " \n", 2667 | " \n", 2668 | " \n", 2669 | " \n", 2670 | " \n", 2671 | " \n", 2672 | " \n", 2673 | " \n", 2674 | " \n", 2675 | " \n", 2676 | " \n", 2677 | " \n", 2678 | " \n", 2679 | " \n", 2680 | " \n", 2681 | " \n", 2682 | " \n", 2683 | " \n", 2684 | " \n", 2685 | " \n", 2686 | " \n", 2687 | " \n", 2688 | " \n", 2689 | " \n", 2690 | " \n", 2691 | " \n", 2692 | " \n", 2693 | " \n", 2694 | " \n", 2695 | " \n", 2696 | " \n", 2697 | " \n", 2698 | " \n", 2699 | " \n", 2700 | " \n", 2701 | " \n", 2702 | " \n", 2703 | " \n", 2704 | " \n", 2705 | " \n", 2706 | " \n", 2707 | " \n", 2708 | " \n", 2709 | " \n", 2710 | " \n", 2711 | " \n", 2712 | " \n", 2713 | " \n", 2714 | " \n", 2715 | " \n", 2716 | " \n", 2717 | " \n", 2718 | "
bto_idcaloha_idefo_idpref_nametissue_chembl_iduberon_id
0NoneNoneNoneBlood brain barrierCHEMBL3987461UBERON:0000120
1BTO:0000089TS-0079EFO:0000296BloodCHEMBL3638178UBERON:0000178
2BTO:0001102TS-0080EFO:0000817Blood vesselCHEMBL3987656UBERON:0001981
3BTO:0000102NoneNoneBlood clotCHEMBL3987655UBERON:0010210
4NoneNoneNoneBlood/BrainCHEMBL3832984None
\n", 2719 | "
" 2720 | ], 2721 | "text/plain": [ 2722 | " bto_id caloha_id efo_id pref_name tissue_chembl_id \\\n", 2723 | "0 None None None Blood brain barrier CHEMBL3987461 \n", 2724 | "1 BTO:0000089 TS-0079 EFO:0000296 Blood CHEMBL3638178 \n", 2725 | "2 BTO:0001102 TS-0080 EFO:0000817 Blood vessel CHEMBL3987656 \n", 2726 | "3 BTO:0000102 None None Blood clot CHEMBL3987655 \n", 2727 | "4 None None None Blood/Brain CHEMBL3832984 \n", 2728 | "\n", 2729 | " uberon_id \n", 2730 | "0 UBERON:0000120 \n", 2731 | "1 UBERON:0000178 \n", 2732 | "2 UBERON:0001981 \n", 2733 | "3 UBERON:0010210 \n", 2734 | "4 None " 2735 | ] 2736 | }, 2737 | "execution_count": 31, 2738 | "metadata": {}, 2739 | "output_type": "execute_result" 2740 | } 2741 | ], 2742 | "source": [ 2743 | "from chembl_webresource_client.new_client import new_client\n", 2744 | "import pandas as pd #Use pandas python module to view data as tables\n", 2745 | "\n", 2746 | "tissue = new_client.tissue\n", 2747 | "res = tissue.filter(pref_name__istartswith='blood')\n", 2748 | "res = pd.DataFrame(res)\n", 2749 | "res.head() #Display top five rows" 2750 | ] 2751 | }, 2752 | { 2753 | "cell_type": "markdown", 2754 | "metadata": {}, 2755 | "source": [ 2756 | "## Get tissue by BTO ID (Brenda Tissue Ontology)" 2757 | ] 2758 | }, 2759 | { 2760 | "cell_type": "code", 2761 | "execution_count": 32, 2762 | "metadata": {}, 2763 | "outputs": [ 2764 | { 2765 | "data": { 2766 | "text/plain": [ 2767 | "[{'bto_id': 'BTO:0001073', 'caloha_id': 'TS-0798', 'efo_id': 'EFO:0000857', 'pref_name': 'Pituitary gland', 'tissue_chembl_id': 'CHEMBL3638173', 'uberon_id': 'UBERON:0000007'}]" 2768 | ] 2769 | }, 2770 | "execution_count": 32, 2771 | "metadata": {}, 2772 | "output_type": "execute_result" 2773 | } 2774 | ], 2775 | "source": [ 2776 | "from chembl_webresource_client.new_client import new_client\n", 2777 | "\n", 2778 | "tissue = new_client.tissue\n", 2779 | "res = tissue.filter(bto_id=\"BTO:0001073\")\n", 2780 | "res" 2781 | ] 2782 | }, 2783 | { 2784 | "cell_type": "markdown", 2785 | "metadata": {}, 2786 | "source": [ 2787 | "## Get tissue by EFO id (Experimental Factor Ontology)" 2788 | ] 2789 | }, 2790 | { 2791 | "cell_type": "code", 2792 | "execution_count": 33, 2793 | "metadata": {}, 2794 | "outputs": [ 2795 | { 2796 | "data": { 2797 | "text/html": [ 2798 | "
\n", 2799 | "\n", 2812 | "\n", 2813 | " \n", 2814 | " \n", 2815 | " \n", 2816 | " \n", 2817 | " \n", 2818 | " \n", 2819 | " \n", 2820 | " \n", 2821 | " \n", 2822 | " \n", 2823 | " \n", 2824 | " \n", 2825 | " \n", 2826 | " \n", 2827 | " \n", 2828 | " \n", 2829 | " \n", 2830 | " \n", 2831 | " \n", 2832 | " \n", 2833 | " \n", 2834 | " \n", 2835 | "
bto_idcaloha_idefo_idpref_nametissue_chembl_iduberon_id
0BTO:0001281TS-0956EFO:0000869SpleenCHEMBL3559722UBERON:0002106
\n", 2836 | "
" 2837 | ], 2838 | "text/plain": [ 2839 | " bto_id caloha_id efo_id pref_name tissue_chembl_id \\\n", 2840 | "0 BTO:0001281 TS-0956 EFO:0000869 Spleen CHEMBL3559722 \n", 2841 | "\n", 2842 | " uberon_id \n", 2843 | "0 UBERON:0002106 " 2844 | ] 2845 | }, 2846 | "execution_count": 33, 2847 | "metadata": {}, 2848 | "output_type": "execute_result" 2849 | } 2850 | ], 2851 | "source": [ 2852 | "from chembl_webresource_client.new_client import new_client\n", 2853 | "import pandas as pd #Use pandas python module to view data as tables\n", 2854 | "\n", 2855 | "tissue = new_client.tissue\n", 2856 | "res = tissue.filter(efo_id=\"EFO:0000869\") #spleen\n", 2857 | "pd.DataFrame(res)" 2858 | ] 2859 | }, 2860 | { 2861 | "cell_type": "markdown", 2862 | "metadata": {}, 2863 | "source": [ 2864 | "# Cells" 2865 | ] 2866 | }, 2867 | { 2868 | "cell_type": "markdown", 2869 | "metadata": {}, 2870 | "source": [ 2871 | "## Get cell line by cellosaurus id" 2872 | ] 2873 | }, 2874 | { 2875 | "cell_type": "code", 2876 | "execution_count": 34, 2877 | "metadata": {}, 2878 | "outputs": [ 2879 | { 2880 | "data": { 2881 | "text/html": [ 2882 | "
\n", 2883 | "\n", 2896 | "\n", 2897 | " \n", 2898 | " \n", 2899 | " \n", 2900 | " \n", 2901 | " \n", 2902 | " \n", 2903 | " \n", 2904 | " \n", 2905 | " \n", 2906 | " \n", 2907 | " \n", 2908 | " \n", 2909 | " \n", 2910 | " \n", 2911 | " \n", 2912 | " \n", 2913 | " \n", 2914 | " \n", 2915 | " \n", 2916 | " \n", 2917 | " \n", 2918 | " \n", 2919 | " \n", 2920 | " \n", 2921 | " \n", 2922 | " \n", 2923 | " \n", 2924 | " \n", 2925 | " \n", 2926 | " \n", 2927 | " \n", 2928 | " \n", 2929 | "
cell_chembl_idcell_descriptioncell_idcell_namecell_source_organismcell_source_tax_idcell_source_tissuecellosaurus_idcl_lincs_idclo_idefo_id
0CHEMBL3307686MDA-MB-435 (Breast metastasis of melanoma cells687MDA-MB-435Homo sapiens9606Breast metastasis of melanoma cellsCVCL_0417NoneNoneEFO_0001213
\n", 2930 | "
" 2931 | ], 2932 | "text/plain": [ 2933 | " cell_chembl_id cell_description cell_id \\\n", 2934 | "0 CHEMBL3307686 MDA-MB-435 (Breast metastasis of melanoma cells 687 \n", 2935 | "\n", 2936 | " cell_name cell_source_organism cell_source_tax_id \\\n", 2937 | "0 MDA-MB-435 Homo sapiens 9606 \n", 2938 | "\n", 2939 | " cell_source_tissue cellosaurus_id cl_lincs_id clo_id \\\n", 2940 | "0 Breast metastasis of melanoma cells CVCL_0417 None None \n", 2941 | "\n", 2942 | " efo_id \n", 2943 | "0 EFO_0001213 " 2944 | ] 2945 | }, 2946 | "execution_count": 34, 2947 | "metadata": {}, 2948 | "output_type": "execute_result" 2949 | } 2950 | ], 2951 | "source": [ 2952 | "from chembl_webresource_client.new_client import new_client\n", 2953 | "import pandas as pd #Use pandas python module to view data as tables\n", 2954 | "\n", 2955 | "cell_line = new_client.cell_line\n", 2956 | "res = cell_line.filter(cellosaurus_id=\"CVCL_0417\")\n", 2957 | "pd.DataFrame(res)" 2958 | ] 2959 | }, 2960 | { 2961 | "cell_type": "markdown", 2962 | "metadata": {}, 2963 | "source": [ 2964 | "## Get cells described as lymphoma" 2965 | ] 2966 | }, 2967 | { 2968 | "cell_type": "code", 2969 | "execution_count": 35, 2970 | "metadata": {}, 2971 | "outputs": [ 2972 | { 2973 | "data": { 2974 | "text/html": [ 2975 | "
\n", 2976 | "\n", 2989 | "\n", 2990 | " \n", 2991 | " \n", 2992 | " \n", 2993 | " \n", 2994 | " \n", 2995 | " \n", 2996 | " \n", 2997 | " \n", 2998 | " \n", 2999 | " \n", 3000 | " \n", 3001 | " \n", 3002 | " \n", 3003 | " \n", 3004 | " \n", 3005 | " \n", 3006 | " \n", 3007 | " \n", 3008 | " \n", 3009 | " \n", 3010 | " \n", 3011 | " \n", 3012 | " \n", 3013 | " \n", 3014 | " \n", 3015 | " \n", 3016 | " \n", 3017 | " \n", 3018 | " \n", 3019 | " \n", 3020 | " \n", 3021 | " \n", 3022 | " \n", 3023 | " \n", 3024 | " \n", 3025 | " \n", 3026 | " \n", 3027 | " \n", 3028 | " \n", 3029 | " \n", 3030 | " \n", 3031 | " \n", 3032 | " \n", 3033 | " \n", 3034 | " \n", 3035 | " \n", 3036 | " \n", 3037 | " \n", 3038 | " \n", 3039 | " \n", 3040 | " \n", 3041 | " \n", 3042 | " \n", 3043 | " \n", 3044 | " \n", 3045 | " \n", 3046 | " \n", 3047 | " \n", 3048 | " \n", 3049 | " \n", 3050 | " \n", 3051 | " \n", 3052 | " \n", 3053 | " \n", 3054 | " \n", 3055 | " \n", 3056 | " \n", 3057 | " \n", 3058 | " \n", 3059 | " \n", 3060 | " \n", 3061 | " \n", 3062 | " \n", 3063 | " \n", 3064 | " \n", 3065 | " \n", 3066 | " \n", 3067 | " \n", 3068 | " \n", 3069 | " \n", 3070 | " \n", 3071 | " \n", 3072 | " \n", 3073 | " \n", 3074 | " \n", 3075 | " \n", 3076 | " \n", 3077 | " \n", 3078 | " \n", 3079 | " \n", 3080 | " \n", 3081 | " \n", 3082 | " \n", 3083 | " \n", 3084 | " \n", 3085 | " \n", 3086 | " \n", 3087 | " \n", 3088 | " \n", 3089 | " \n", 3090 | " \n", 3091 | " \n", 3092 | " \n", 3093 | " \n", 3094 | " \n", 3095 | " \n", 3096 | " \n", 3097 | " \n", 3098 | " \n", 3099 | " \n", 3100 | " \n", 3101 | " \n", 3102 | " \n", 3103 | " \n", 3104 | " \n", 3105 | " \n", 3106 | " \n", 3107 | " \n", 3108 | " \n", 3109 | " \n", 3110 | " \n", 3111 | " \n", 3112 | " \n", 3113 | " \n", 3114 | " \n", 3115 | " \n", 3116 | " \n", 3117 | " \n", 3118 | " \n", 3119 | " \n", 3120 | " \n", 3121 | " \n", 3122 | " \n", 3123 | " \n", 3124 | " \n", 3125 | " \n", 3126 | " \n", 3127 | " \n", 3128 | " \n", 3129 | " \n", 3130 | " \n", 3131 | " \n", 3132 | " \n", 3133 | " \n", 3134 | " \n", 3135 | " \n", 3136 | " \n", 3137 | " \n", 3138 | " \n", 3139 | " \n", 3140 | " \n", 3141 | " \n", 3142 | " \n", 3143 | " \n", 3144 | " \n", 3145 | " \n", 3146 | " \n", 3147 | " \n", 3148 | " \n", 3149 | " \n", 3150 | " \n", 3151 | " \n", 3152 | " \n", 3153 | " \n", 3154 | " \n", 3155 | " \n", 3156 | " \n", 3157 | " \n", 3158 | " \n", 3159 | " \n", 3160 | " \n", 3161 | " \n", 3162 | " \n", 3163 | " \n", 3164 | " \n", 3165 | " \n", 3166 | " \n", 3167 | " \n", 3168 | " \n", 3169 | " \n", 3170 | " \n", 3171 | " \n", 3172 | " \n", 3173 | " \n", 3174 | " \n", 3175 | " \n", 3176 | " \n", 3177 | " \n", 3178 | " \n", 3179 | " \n", 3180 | " \n", 3181 | " \n", 3182 | " \n", 3183 | " \n", 3184 | " \n", 3185 | " \n", 3186 | " \n", 3187 | " \n", 3188 | " \n", 3189 | " \n", 3190 | " \n", 3191 | " \n", 3192 | " \n", 3193 | " \n", 3194 | " \n", 3195 | " \n", 3196 | " \n", 3197 | " \n", 3198 | " \n", 3199 | " \n", 3200 | " \n", 3201 | " \n", 3202 | " \n", 3203 | " \n", 3204 | " \n", 3205 | " \n", 3206 | " \n", 3207 | " \n", 3208 | " \n", 3209 | " \n", 3210 | " \n", 3211 | " \n", 3212 | " \n", 3213 | " \n", 3214 | " \n", 3215 | " \n", 3216 | " \n", 3217 | " \n", 3218 | "
cell_chembl_idcell_descriptioncell_idcell_namecell_source_organismcell_source_tax_idcell_source_tissuecellosaurus_idcl_lincs_idclo_idefo_id
0CHEMBL3308488CA46 (Burkitts lymphoma cells)346CA46Homo sapiens9606.0Burkitts lymphoma cellsCVCL_1101LCL-2017CLO_0002168EFO_0002124
1CHEMBL3308006U-937 (Histiocytic lymphoma cells)379U-937Homo sapiens9606.0Histiocytic lymphoma cellsCVCL_0007LCL-1125CLO_0009465EFO_0001257
2CHEMBL3308007U-937/GTB (Histiocytic lymphoma cells)380U-937/GTBHomo sapiens9606.0Histiocytic lymphoma cellsCVCL_U631NoneNoneNone
3CHEMBL3308492BJAB (Burkitts lymphoma cells)412BJABHomo sapiens9606.0Burkitts lymphoma cellsCVCL_5711NoneNoneEFO_0002815
4CHEMBL3308050W4 (Lymphoma cells)426W4Mus musculus10090.0Lymphoma cellsCVCL_H588NoneNoneNone
5CHEMBL3307500HuT78 (T-lymphoma cells)472HuT78Homo sapiens9606.0T-lymphoma cellsCVCL_0337LCL-2005CLO_0004304EFO_0002209
6CHEMBL3308401P388 (Lymphoma cells)549P388Mus musculus10090.0Lymphoma cellsCVCL_7222NoneCLO_0008321None
7CHEMBL3307566P388/ADR (Lymphoma cells)550P388/ADRMus musculus10090.0Lymphoma cellsCVCL_IZ75NoneNoneNone
8CHEMBL3307567P388CPT45 (Lymphoma cells)551P388CPT45Mus musculus10090.0Lymphoma cellsCVCL_D638NoneNoneNone
9CHEMBL3307579Daudi (Burkitts lymphoma cells)564DaudiHomo sapiens9606.0Burkitts lymphoma cellsCVCL_0008LCL-2018CLO_0002708EFO_0002169
10CHEMBL3307608Burkitts lymphoma cells596Burkitts lymphoma cellNoneNaNBurkitts lymphoma cellsNoneNoneNoneNone
11CHEMBL3307658JIJOYE (Burkitts lymphoma cells)654JIYOYEHomo sapiens9606.0Burkitts lymphoma cellsCVCL_1317NoneCLO_0007025EFO_0002215
12CHEMBL3307690L5178Y (Lymphoma cells)692L5178YMus musculus10090.0Lymphoma cellsCVCL_2097NoneCLO_0007195None
13CHEMBL3307722FEPD (Anaplastic lymphoma cells)731FEPDHomo sapiens9606.0Anaplastic lymphoma cellsCVCL_H614NoneNoneNone
14CHEMBL3307875Lymphoma cell line949Lymphoma cell lineNoneNaNNoneNoneNoneNoneNone
\n", 3219 | "
" 3220 | ], 3221 | "text/plain": [ 3222 | " cell_chembl_id cell_description cell_id \\\n", 3223 | "0 CHEMBL3308488 CA46 (Burkitts lymphoma cells) 346 \n", 3224 | "1 CHEMBL3308006 U-937 (Histiocytic lymphoma cells) 379 \n", 3225 | "2 CHEMBL3308007 U-937/GTB (Histiocytic lymphoma cells) 380 \n", 3226 | "3 CHEMBL3308492 BJAB (Burkitts lymphoma cells) 412 \n", 3227 | "4 CHEMBL3308050 W4 (Lymphoma cells) 426 \n", 3228 | "5 CHEMBL3307500 HuT78 (T-lymphoma cells) 472 \n", 3229 | "6 CHEMBL3308401 P388 (Lymphoma cells) 549 \n", 3230 | "7 CHEMBL3307566 P388/ADR (Lymphoma cells) 550 \n", 3231 | "8 CHEMBL3307567 P388CPT45 (Lymphoma cells) 551 \n", 3232 | "9 CHEMBL3307579 Daudi (Burkitts lymphoma cells) 564 \n", 3233 | "10 CHEMBL3307608 Burkitts lymphoma cells 596 \n", 3234 | "11 CHEMBL3307658 JIJOYE (Burkitts lymphoma cells) 654 \n", 3235 | "12 CHEMBL3307690 L5178Y (Lymphoma cells) 692 \n", 3236 | "13 CHEMBL3307722 FEPD (Anaplastic lymphoma cells) 731 \n", 3237 | "14 CHEMBL3307875 Lymphoma cell line 949 \n", 3238 | "\n", 3239 | " cell_name cell_source_organism cell_source_tax_id \\\n", 3240 | "0 CA46 Homo sapiens 9606.0 \n", 3241 | "1 U-937 Homo sapiens 9606.0 \n", 3242 | "2 U-937/GTB Homo sapiens 9606.0 \n", 3243 | "3 BJAB Homo sapiens 9606.0 \n", 3244 | "4 W4 Mus musculus 10090.0 \n", 3245 | "5 HuT78 Homo sapiens 9606.0 \n", 3246 | "6 P388 Mus musculus 10090.0 \n", 3247 | "7 P388/ADR Mus musculus 10090.0 \n", 3248 | "8 P388CPT45 Mus musculus 10090.0 \n", 3249 | "9 Daudi Homo sapiens 9606.0 \n", 3250 | "10 Burkitts lymphoma cell None NaN \n", 3251 | "11 JIYOYE Homo sapiens 9606.0 \n", 3252 | "12 L5178Y Mus musculus 10090.0 \n", 3253 | "13 FEPD Homo sapiens 9606.0 \n", 3254 | "14 Lymphoma cell line None NaN \n", 3255 | "\n", 3256 | " cell_source_tissue cellosaurus_id cl_lincs_id clo_id \\\n", 3257 | "0 Burkitts lymphoma cells CVCL_1101 LCL-2017 CLO_0002168 \n", 3258 | "1 Histiocytic lymphoma cells CVCL_0007 LCL-1125 CLO_0009465 \n", 3259 | "2 Histiocytic lymphoma cells CVCL_U631 None None \n", 3260 | "3 Burkitts lymphoma cells CVCL_5711 None None \n", 3261 | "4 Lymphoma cells CVCL_H588 None None \n", 3262 | "5 T-lymphoma cells CVCL_0337 LCL-2005 CLO_0004304 \n", 3263 | "6 Lymphoma cells CVCL_7222 None CLO_0008321 \n", 3264 | "7 Lymphoma cells CVCL_IZ75 None None \n", 3265 | "8 Lymphoma cells CVCL_D638 None None \n", 3266 | "9 Burkitts lymphoma cells CVCL_0008 LCL-2018 CLO_0002708 \n", 3267 | "10 Burkitts lymphoma cells None None None \n", 3268 | "11 Burkitts lymphoma cells CVCL_1317 None CLO_0007025 \n", 3269 | "12 Lymphoma cells CVCL_2097 None CLO_0007195 \n", 3270 | "13 Anaplastic lymphoma cells CVCL_H614 None None \n", 3271 | "14 None None None None \n", 3272 | "\n", 3273 | " efo_id \n", 3274 | "0 EFO_0002124 \n", 3275 | "1 EFO_0001257 \n", 3276 | "2 None \n", 3277 | "3 EFO_0002815 \n", 3278 | "4 None \n", 3279 | "5 EFO_0002209 \n", 3280 | "6 None \n", 3281 | "7 None \n", 3282 | "8 None \n", 3283 | "9 EFO_0002169 \n", 3284 | "10 None \n", 3285 | "11 EFO_0002215 \n", 3286 | "12 None \n", 3287 | "13 None \n", 3288 | "14 None " 3289 | ] 3290 | }, 3291 | "execution_count": 35, 3292 | "metadata": {}, 3293 | "output_type": "execute_result" 3294 | } 3295 | ], 3296 | "source": [ 3297 | "from chembl_webresource_client.new_client import new_client\n", 3298 | "import pandas as pd #Use pandas python module to view data as tables\n", 3299 | "\n", 3300 | "cell_line = new_client.cell_line\n", 3301 | "res = cell_line.filter(cell_description__icontains=\"lymphoma\")\n", 3302 | "pd.DataFrame(res)" 3303 | ] 3304 | }, 3305 | { 3306 | "cell_type": "markdown", 3307 | "metadata": {}, 3308 | "source": [ 3309 | "# Utils" 3310 | ] 3311 | }, 3312 | { 3313 | "cell_type": "markdown", 3314 | "metadata": {}, 3315 | "source": [ 3316 | "## Convert SMILES to CTAB" 3317 | ] 3318 | }, 3319 | { 3320 | "cell_type": "code", 3321 | "execution_count": 37, 3322 | "metadata": {}, 3323 | "outputs": [ 3324 | { 3325 | "data": { 3326 | "text/plain": [ 3327 | "'\\n RDKit 2D\\n\\n 13 13 0 0 0 0 0 0 0 0999 V2000\\n -0.9550 -1.3220 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.9528 -0.3220 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.0858 0.1764 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.7792 -0.3254 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.7772 -1.3254 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.6422 -1.8270 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.5092 -1.3288 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.5112 -0.3288 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.6462 0.1728 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.6482 1.1728 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.7832 1.6746 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.5152 1.6710 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.8178 0.1798 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1 2 2 0\\n 2 3 1 0\\n 3 4 1 0\\n 4 5 2 0\\n 5 6 1 0\\n 6 7 2 0\\n 7 8 1 0\\n 8 9 2 0\\n 9 10 1 0\\n 10 11 2 0\\n 10 12 1 0\\n 2 13 1 0\\n 9 4 1 0\\nM END\\n$$$$\\n'" 3328 | ] 3329 | }, 3330 | "execution_count": 37, 3331 | "metadata": {}, 3332 | "output_type": "execute_result" 3333 | } 3334 | ], 3335 | "source": [ 3336 | "from chembl_webresource_client.utils import utils\n", 3337 | "\n", 3338 | "aspirin = utils.smiles2ctab('O=C(Oc1ccccc1C(=O)O)C')\n", 3339 | "aspirin" 3340 | ] 3341 | }, 3342 | { 3343 | "cell_type": "markdown", 3344 | "metadata": {}, 3345 | "source": [ 3346 | "## Compute Maximal Common Substructure" 3347 | ] 3348 | }, 3349 | { 3350 | "cell_type": "code", 3351 | "execution_count": 38, 3352 | "metadata": {}, 3353 | "outputs": [ 3354 | { 3355 | "data": { 3356 | "text/plain": [ 3357 | "'[#6]1(-[#6]):[#6]:[#6](-[#8]-[#6]):[#6](:[#6]:[#6]:1)-[#8]'" 3358 | ] 3359 | }, 3360 | "execution_count": 38, 3361 | "metadata": {}, 3362 | "output_type": "execute_result" 3363 | } 3364 | ], 3365 | "source": [ 3366 | "from chembl_webresource_client.utils import utils\n", 3367 | "\n", 3368 | "smiles = [\"O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C\",\n", 3369 | " \"CC(C)CCCCCC(=O)NCC1=CC(=C(C=C1)O)OC\", \"c1(C=O)cc(OC)c(O)cc1\"]\n", 3370 | "mols = [utils.smiles2ctab(smile) for smile in smiles]\n", 3371 | "sdf = ''.join(mols)\n", 3372 | "result = utils.mcs(sdf)\n", 3373 | "result" 3374 | ] 3375 | }, 3376 | { 3377 | "cell_type": "markdown", 3378 | "metadata": {}, 3379 | "source": [ 3380 | "## Compute various molecular descriptors" 3381 | ] 3382 | }, 3383 | { 3384 | "cell_type": "code", 3385 | "execution_count": 39, 3386 | "metadata": {}, 3387 | "outputs": [ 3388 | { 3389 | "data": { 3390 | "text/plain": [ 3391 | "{'qed': 0.5501217966938848,\n", 3392 | " 'MolWt': 180.15899999999996,\n", 3393 | " 'TPSA': 63.60000000000001,\n", 3394 | " 'HeavyAtomCount': 13,\n", 3395 | " 'NumAromaticRings': 1,\n", 3396 | " 'NumHAcceptors': 3,\n", 3397 | " 'NumHDonors': 1,\n", 3398 | " 'NumRotatableBonds': 2,\n", 3399 | " 'MolLogP': 1.3100999999999998,\n", 3400 | " 'MolecularFormula': 'C9H8O4',\n", 3401 | " 'Ro3Pass': 0,\n", 3402 | " 'NumRo5': 0,\n", 3403 | " 'MonoisotopicMolWt': 180.042258736}" 3404 | ] 3405 | }, 3406 | "execution_count": 39, 3407 | "metadata": {}, 3408 | "output_type": "execute_result" 3409 | } 3410 | ], 3411 | "source": [ 3412 | "from chembl_webresource_client.utils import utils\n", 3413 | "import json\n", 3414 | "\n", 3415 | "aspirin = utils.smiles2ctab('O=C(Oc1ccccc1C(=O)O)C')\n", 3416 | "descs = json.loads(utils.chemblDescriptors(aspirin))[0]\n", 3417 | "descs" 3418 | ] 3419 | }, 3420 | { 3421 | "cell_type": "markdown", 3422 | "metadata": {}, 3423 | "source": [ 3424 | "## Compute structural alerts" 3425 | ] 3426 | }, 3427 | { 3428 | "cell_type": "code", 3429 | "execution_count": 40, 3430 | "metadata": {}, 3431 | "outputs": [ 3432 | { 3433 | "name": "stdout", 3434 | "output_type": "stream", 3435 | "text": [ 3436 | "{'alert_id': 1030, 'alert_name': 'Ester', 'set_name': 'MLSMR', 'smarts': '[#6]-C(=O)O-[#6]'}\n", 3437 | "{'alert_id': 1069, 'alert_name': 'vinyl michael acceptor1', 'set_name': 'MLSMR', 'smarts': '[#6]-[CH1]=C-C(=O)[#6,#7,#8]'}\n" 3438 | ] 3439 | } 3440 | ], 3441 | "source": [ 3442 | "from chembl_webresource_client.utils import utils\n", 3443 | "\n", 3444 | "mol = utils.smiles2ctab(\"O=C(Oc1ccccc1C(=O)O)C\")\n", 3445 | "alerts = json.loads(utils.structuralAlerts(mol))\n", 3446 | "for a in alerts[0]:\n", 3447 | " print(a)" 3448 | ] 3449 | }, 3450 | { 3451 | "cell_type": "markdown", 3452 | "metadata": {}, 3453 | "source": [ 3454 | "## Standardize a molecule" 3455 | ] 3456 | }, 3457 | { 3458 | "cell_type": "code", 3459 | "execution_count": 41, 3460 | "metadata": {}, 3461 | "outputs": [ 3462 | { 3463 | "data": { 3464 | "text/plain": [ 3465 | "[{'standard_molblock': '\\n RDKit 2D\\n\\n 19 17 0 0 0 0 0 0 0 0999 V2000\\n 0.0000 -3.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -5.5170 -1.9538 0.0000 Na 0 0 0 0 0 15 0 0 0 0 0 0\\n -2.9244 -0.4442 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.0602 0.0590 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.0638 1.0590 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.1924 -0.4380 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3282 0.0652 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.5396 -0.4318 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4038 0.0714 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4002 1.0714 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.2644 1.5744 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.2608 2.5744 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.5324 1.5682 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3318 1.0652 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 5.0649 2.8712 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 4.8623 1.8920 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.8683 1.7822 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.4567 2.6936 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 4.1963 3.3666 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3 4 1 0\\n 4 5 2 0\\n 4 6 1 0\\n 6 7 1 0\\n 7 8 2 0\\n 8 9 1 0\\n 9 10 2 0\\n 10 11 1 0\\n 11 12 1 0\\n 10 13 1 0\\n 13 14 2 0\\n 14 7 1 0\\n 15 16 2 0\\n 16 17 1 0\\n 17 18 2 0\\n 18 19 1 0\\n 19 15 1 0\\nM CHG 2 2 1 3 -1\\nM END\\n'}]" 3466 | ] 3467 | }, 3468 | "execution_count": 41, 3469 | "metadata": {}, 3470 | "output_type": "execute_result" 3471 | } 3472 | ], 3473 | "source": [ 3474 | "from chembl_webresource_client.utils import utils\n", 3475 | "mol = utils.smiles2ctab(\"[Na]OC(=O)Cc1ccc(C[NH3+])cc1.c1nnn[n-]1.O\")\n", 3476 | "st = json.loads(utils.standardize(mol))\n", 3477 | "st" 3478 | ] 3479 | }, 3480 | { 3481 | "cell_type": "markdown", 3482 | "metadata": {}, 3483 | "source": [ 3484 | "## Calculate the parent molecule from its SMILES identifier" 3485 | ] 3486 | }, 3487 | { 3488 | "cell_type": "code", 3489 | "execution_count": 42, 3490 | "metadata": {}, 3491 | "outputs": [ 3492 | { 3493 | "data": { 3494 | "text/plain": [ 3495 | "[{'parent_molblock': '\\n RDKit 2D\\n\\n 18 18 0 0 0 0 0 0 0 0999 V2000\\n -5.5170 -1.9538 0.0000 Na 0 0 0 0 0 1 0 0 0 0 0 0\\n -2.9244 -0.4442 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.0602 0.0590 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -2.0638 1.0590 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\\n -1.1924 -0.4380 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3282 0.0652 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.5396 -0.4318 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4038 0.0714 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 1.4002 1.0714 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.2644 1.5744 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 2.2608 2.5744 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 0.5324 1.5682 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n -0.3318 1.0652 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 5.0649 2.8712 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\\n 4.8623 1.8920 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.8683 1.7822 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 3.4567 2.6936 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 4.1963 3.3666 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0\\n 1 2 1 0\\n 2 3 1 0\\n 3 4 2 0\\n 3 5 1 0\\n 5 6 1 0\\n 6 7 2 0\\n 7 8 1 0\\n 8 9 2 0\\n 9 10 1 0\\n 10 11 1 0\\n 9 12 1 0\\n 12 13 2 0\\n 13 6 1 0\\n 14 15 2 0\\n 15 16 1 0\\n 16 17 2 0\\n 17 18 1 0\\n 18 14 1 0\\nM END\\n',\n", 3496 | " 'exclude': False}]" 3497 | ] 3498 | }, 3499 | "execution_count": 42, 3500 | "metadata": {}, 3501 | "output_type": "execute_result" 3502 | } 3503 | ], 3504 | "source": [ 3505 | "from chembl_webresource_client.utils import utils\n", 3506 | "\n", 3507 | "mol = utils.smiles2ctab(\"[Na]OC(=O)Cc1ccc(C[NH3+])cc1.c1nnn[n-]1.[Na]\")\n", 3508 | "par = json.loads(utils.getParent(mol))\n", 3509 | "par" 3510 | ] 3511 | }, 3512 | { 3513 | "cell_type": "code", 3514 | "execution_count": null, 3515 | "metadata": {}, 3516 | "outputs": [], 3517 | "source": [] 3518 | } 3519 | ], 3520 | "metadata": { 3521 | "kernelspec": { 3522 | "display_name": "Python 3", 3523 | "language": "python", 3524 | "name": "python3" 3525 | }, 3526 | "language_info": { 3527 | "codemirror_mode": { 3528 | "name": "ipython", 3529 | "version": 3 3530 | }, 3531 | "file_extension": ".py", 3532 | "mimetype": "text/x-python", 3533 | "name": "python", 3534 | "nbconvert_exporter": "python", 3535 | "pygments_lexer": "ipython3", 3536 | "version": "3.7.3" 3537 | } 3538 | }, 3539 | "nbformat": 4, 3540 | "nbformat_minor": 5 3541 | } 3542 | --------------------------------------------------------------------------------