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b/DEMO/oneliner_repurpose_COVID19_Pretrained.ipynb |
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"os.chdir('../')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import DeepPurpose.oneliner as oneliner\n", |
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"from DeepPurpose import dataset\n", |
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"import time" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"X_repurpose, drug_names, drug_CID = dataset.load_antiviral_drugs('./data')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"metadata": { |
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"scrolled": false |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Loading customized repurposing dataset...\n", |
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"Checking if pretrained directory is valid...\n", |
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"Beginning to load the pretrained models...\n", |
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"Using pretrained model and making predictions...\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
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"-------------\n", |
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"models prediction finished...\n", |
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"aggregating results...\n", |
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"---------------\n", |
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"Drug Repurposing Result for SARS-CoV2 3CL Protease\n", |
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"+------+----------------------+------------------------+---------------+\n", |
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"| Rank | Drug Name | Target Name | Binding Score |\n", |
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"+------+----------------------+------------------------+---------------+\n", |
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"| 1 | Sofosbuvir | SARS-CoV2 3CL Protease | 360.22 |\n", |
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"| 2 | Daclatasvir | SARS-CoV2 3CL Protease | 424.06 |\n", |
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"| 3 | Vicriviroc | SARS-CoV2 3CL Protease | 623.78 |\n", |
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"| 4 | Efavirenz | SARS-CoV2 3CL Protease | 768.33 |\n", |
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"| 5 | Simeprevir | SARS-CoV2 3CL Protease | 781.29 |\n", |
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"| 6 | Etravirine | SARS-CoV2 3CL Protease | 809.88 |\n", |
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"| 7 | Amantadine | SARS-CoV2 3CL Protease | 826.28 |\n", |
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"| 8 | Letermovir | SARS-CoV2 3CL Protease | 891.66 |\n", |
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"| 9 | Rilpivirine | SARS-CoV2 3CL Protease | 929.63 |\n", |
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"| 10 | Ritonavir | SARS-CoV2 3CL Protease | 941.13 |\n", |
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"| 11 | Darunavir | SARS-CoV2 3CL Protease | 944.10 |\n", |
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"| 12 | Maraviroc | SARS-CoV2 3CL Protease | 945.22 |\n", |
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"| 13 | Lopinavir | SARS-CoV2 3CL Protease | 945.71 |\n", |
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"| 14 | Fosamprenavir | SARS-CoV2 3CL Protease | 964.99 |\n", |
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"| 15 | Peramivir | SARS-CoV2 3CL Protease | 1050.64 |\n", |
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"| 16 | Grazoprevir | SARS-CoV2 3CL Protease | 1202.52 |\n", |
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"| 17 | Amprenavir | SARS-CoV2 3CL Protease | 1204.21 |\n", |
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"| 18 | Telaprevir | SARS-CoV2 3CL Protease | 1212.97 |\n", |
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"| 19 | Elvitegravir | SARS-CoV2 3CL Protease | 1220.48 |\n", |
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"| 20 | Tenofovir | SARS-CoV2 3CL Protease | 1250.70 |\n", |
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"| 21 | Descovy | SARS-CoV2 3CL Protease | 1250.70 |\n", |
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"| 22 | Atazanavir | SARS-CoV2 3CL Protease | 1348.65 |\n", |
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"| 23 | Tromantadine | SARS-CoV2 3CL Protease | 1380.71 |\n", |
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"| 24 | Nelfinavir | SARS-CoV2 3CL Protease | 1451.43 |\n", |
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"| 25 | Abacavir | SARS-CoV2 3CL Protease | 1464.89 |\n", |
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"| 26 | Tenofovir_disoproxil | SARS-CoV2 3CL Protease | 1571.85 |\n", |
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"| 27 | Dolutegravir | SARS-CoV2 3CL Protease | 1672.82 |\n", |
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"| 28 | Delavirdine | SARS-CoV2 3CL Protease | 1691.47 |\n", |
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"| 29 | Saquinavir | SARS-CoV2 3CL Protease | 1763.63 |\n", |
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"| 30 | Raltegravir | SARS-CoV2 3CL Protease | 1854.40 |\n", |
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"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
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"\n", |
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"Time lapse:6.697427749633789\n" |
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] |
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} |
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], |
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"source": [ |
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"start = time.time()\n", |
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"target, target_name = dataset.load_SARS_CoV2_Protease_3CL()\n", |
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"oneliner.repurpose(target = target, \n", |
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" target_name = target_name, \n", |
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" X_repurpose = X_repurpose,\n", |
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" drug_names = drug_names,\n", |
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" save_dir = './save_folder',\n", |
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" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
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" agg = 'mean')\n", |
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"end = time.time()\n", |
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"print('Time lapse:' + str(end - start))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"metadata": { |
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"scrolled": false |
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}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Loading customized repurposing dataset...\n", |
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184 |
"Checking if pretrained directory is valid...\n", |
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"Beginning to load the pretrained models...\n", |
|
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"Using pretrained model and making predictions...\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
|
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"predicting...\n", |
|
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"---------------\n", |
|
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198 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
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"-------------\n", |
|
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
|
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"predicting...\n", |
|
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"---------------\n", |
|
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"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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"predicting...\n", |
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"---------------\n", |
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"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
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"-------------\n", |
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"repurposing...\n", |
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"in total: 82 drug-target pairs\n", |
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"encoding drug...\n", |
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"unique drugs: 81\n", |
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"drug encoding finished...\n", |
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"encoding protein...\n", |
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"unique target sequence: 1\n", |
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247 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
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"protein encoding finished...\n", |
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"Done.\n", |
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250 |
"predicting...\n", |
|
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"---------------\n", |
|
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252 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
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"-------------\n", |
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"models prediction finished...\n", |
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255 |
"aggregating results...\n", |
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"---------------\n", |
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"Drug Repurposing Result for SARS-CoV2 3CL Protease\n", |
|
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"+------+----------------------+------------------------+---------------+\n", |
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"| Rank | Drug Name | Target Name | Binding Score |\n", |
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"+------+----------------------+------------------------+---------------+\n", |
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"| 1 | Lopinavir | SARS-CoV2 3CL Protease | 0.30 |\n", |
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"| 2 | Darunavir | SARS-CoV2 3CL Protease | 0.37 |\n", |
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"| 3 | Amprenavir | SARS-CoV2 3CL Protease | 1.31 |\n", |
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"| 4 | Tipranavir | SARS-CoV2 3CL Protease | 1.35 |\n", |
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"| 5 | Baloxavir | SARS-CoV2 3CL Protease | 1.69 |\n", |
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"| 6 | Boceprevir | SARS-CoV2 3CL Protease | 2.06 |\n", |
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"| 7 | Glecaprevir | SARS-CoV2 3CL Protease | 2.22 |\n", |
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"| 8 | Oseltamivir | SARS-CoV2 3CL Protease | 2.56 |\n", |
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"| 9 | Telaprevir | SARS-CoV2 3CL Protease | 2.70 |\n", |
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"| 10 | Nelfinavir | SARS-CoV2 3CL Protease | 3.56 |\n", |
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"| 11 | Maraviroc | SARS-CoV2 3CL Protease | 4.50 |\n", |
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"| 12 | Daclatasvir | SARS-CoV2 3CL Protease | 5.09 |\n", |
|
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"| 13 | Vicriviroc | SARS-CoV2 3CL Protease | 7.62 |\n", |
|
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"| 14 | Etravirine | SARS-CoV2 3CL Protease | 8.80 |\n", |
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"| 15 | Fosamprenavir | SARS-CoV2 3CL Protease | 9.91 |\n", |
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"| 16 | Entecavir | SARS-CoV2 3CL Protease | 10.25 |\n", |
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"| 17 | Atazanavir | SARS-CoV2 3CL Protease | 10.41 |\n", |
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"| 18 | Foscarnet | SARS-CoV2 3CL Protease | 11.34 |\n", |
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"| 19 | Simeprevir | SARS-CoV2 3CL Protease | 11.76 |\n", |
|
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"| 20 | Rilpivirine | SARS-CoV2 3CL Protease | 11.95 |\n", |
|
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"| 21 | Abacavir | SARS-CoV2 3CL Protease | 12.70 |\n", |
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"| 22 | Amantadine | SARS-CoV2 3CL Protease | 13.24 |\n", |
|
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283 |
"| 23 | Pleconaril | SARS-CoV2 3CL Protease | 13.74 |\n", |
|
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"| 24 | Saquinavir | SARS-CoV2 3CL Protease | 19.87 |\n", |
|
|
285 |
"| 25 | Sofosbuvir | SARS-CoV2 3CL Protease | 20.28 |\n", |
|
|
286 |
"| 26 | Delavirdine | SARS-CoV2 3CL Protease | 20.65 |\n", |
|
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287 |
"| 27 | Raltegravir | SARS-CoV2 3CL Protease | 22.45 |\n", |
|
|
288 |
"| 28 | Tenofovir | SARS-CoV2 3CL Protease | 25.22 |\n", |
|
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289 |
"| 29 | Descovy | SARS-CoV2 3CL Protease | 25.22 |\n", |
|
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290 |
"| 30 | Peramivir | SARS-CoV2 3CL Protease | 25.57 |\n", |
|
|
291 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
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292 |
"\n" |
|
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293 |
] |
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294 |
} |
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], |
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296 |
"source": [ |
|
|
297 |
"oneliner.repurpose(target = target, \n", |
|
|
298 |
" target_name = target_name, \n", |
|
|
299 |
" X_repurpose = X_repurpose,\n", |
|
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300 |
" drug_names = drug_names,\n", |
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301 |
" save_dir = './save_folder',\n", |
|
|
302 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
303 |
" agg = 'max_effect')" |
|
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304 |
] |
|
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305 |
}, |
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306 |
{ |
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307 |
"cell_type": "code", |
|
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308 |
"execution_count": 6, |
|
|
309 |
"metadata": { |
|
|
310 |
"scrolled": false |
|
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311 |
}, |
|
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312 |
"outputs": [ |
|
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313 |
{ |
|
|
314 |
"name": "stdout", |
|
|
315 |
"output_type": "stream", |
|
|
316 |
"text": [ |
|
|
317 |
"Loading customized repurposing dataset...\n", |
|
|
318 |
"Checking if pretrained directory is valid...\n", |
|
|
319 |
"Beginning to load the pretrained models...\n", |
|
|
320 |
"Using pretrained model and making predictions...\n", |
|
|
321 |
"repurposing...\n", |
|
|
322 |
"in total: 82 drug-target pairs\n", |
|
|
323 |
"encoding drug...\n", |
|
|
324 |
"unique drugs: 81\n", |
|
|
325 |
"drug encoding finished...\n", |
|
|
326 |
"encoding protein...\n", |
|
|
327 |
"unique target sequence: 1\n", |
|
|
328 |
"protein encoding finished...\n", |
|
|
329 |
"Done.\n", |
|
|
330 |
"predicting...\n", |
|
|
331 |
"---------------\n", |
|
|
332 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
333 |
"-------------\n", |
|
|
334 |
"repurposing...\n", |
|
|
335 |
"in total: 82 drug-target pairs\n", |
|
|
336 |
"encoding drug...\n", |
|
|
337 |
"unique drugs: 81\n", |
|
|
338 |
"drug encoding finished...\n", |
|
|
339 |
"encoding protein...\n", |
|
|
340 |
"unique target sequence: 1\n", |
|
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341 |
"protein encoding finished...\n", |
|
|
342 |
"Done.\n", |
|
|
343 |
"predicting...\n", |
|
|
344 |
"---------------\n", |
|
|
345 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
346 |
"-------------\n", |
|
|
347 |
"repurposing...\n", |
|
|
348 |
"in total: 82 drug-target pairs\n", |
|
|
349 |
"encoding drug...\n", |
|
|
350 |
"unique drugs: 81\n", |
|
|
351 |
"drug encoding finished...\n", |
|
|
352 |
"encoding protein...\n", |
|
|
353 |
"unique target sequence: 1\n", |
|
|
354 |
"protein encoding finished...\n", |
|
|
355 |
"Done.\n", |
|
|
356 |
"predicting...\n", |
|
|
357 |
"---------------\n", |
|
|
358 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
359 |
"-------------\n", |
|
|
360 |
"repurposing...\n", |
|
|
361 |
"in total: 82 drug-target pairs\n", |
|
|
362 |
"encoding drug...\n", |
|
|
363 |
"unique drugs: 81\n", |
|
|
364 |
"drug encoding finished...\n", |
|
|
365 |
"encoding protein...\n", |
|
|
366 |
"unique target sequence: 1\n", |
|
|
367 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
368 |
"protein encoding finished...\n", |
|
|
369 |
"Done.\n", |
|
|
370 |
"predicting...\n", |
|
|
371 |
"---------------\n", |
|
|
372 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
373 |
"-------------\n", |
|
|
374 |
"repurposing...\n", |
|
|
375 |
"in total: 82 drug-target pairs\n", |
|
|
376 |
"encoding drug...\n", |
|
|
377 |
"unique drugs: 81\n", |
|
|
378 |
"drug encoding finished...\n", |
|
|
379 |
"encoding protein...\n", |
|
|
380 |
"unique target sequence: 1\n", |
|
|
381 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
382 |
"protein encoding finished...\n", |
|
|
383 |
"Done.\n", |
|
|
384 |
"predicting...\n", |
|
|
385 |
"---------------\n", |
|
|
386 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
387 |
"-------------\n", |
|
|
388 |
"models prediction finished...\n", |
|
|
389 |
"aggregating results...\n", |
|
|
390 |
"---------------\n", |
|
|
391 |
"Drug Repurposing Result for SARS-CoV2 3CL Protease\n", |
|
|
392 |
"+------+----------------------+------------------------+---------------+\n", |
|
|
393 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
394 |
"+------+----------------------+------------------------+---------------+\n", |
|
|
395 |
"| 1 | Sofosbuvir | SARS-CoV2 3CL Protease | 190.25 |\n", |
|
|
396 |
"| 2 | Daclatasvir | SARS-CoV2 3CL Protease | 214.58 |\n", |
|
|
397 |
"| 3 | Vicriviroc | SARS-CoV2 3CL Protease | 315.70 |\n", |
|
|
398 |
"| 4 | Simeprevir | SARS-CoV2 3CL Protease | 396.53 |\n", |
|
|
399 |
"| 5 | Etravirine | SARS-CoV2 3CL Protease | 409.34 |\n", |
|
|
400 |
"| 6 | Amantadine | SARS-CoV2 3CL Protease | 419.76 |\n", |
|
|
401 |
"| 7 | Letermovir | SARS-CoV2 3CL Protease | 460.28 |\n", |
|
|
402 |
"| 8 | Rilpivirine | SARS-CoV2 3CL Protease | 470.79 |\n", |
|
|
403 |
"| 9 | Darunavir | SARS-CoV2 3CL Protease | 472.24 |\n", |
|
|
404 |
"| 10 | Lopinavir | SARS-CoV2 3CL Protease | 473.01 |\n", |
|
|
405 |
"| 11 | Maraviroc | SARS-CoV2 3CL Protease | 474.86 |\n", |
|
|
406 |
"| 12 | Fosamprenavir | SARS-CoV2 3CL Protease | 487.45 |\n", |
|
|
407 |
"| 13 | Ritonavir | SARS-CoV2 3CL Protease | 492.19 |\n", |
|
|
408 |
"| 14 | Efavirenz | SARS-CoV2 3CL Protease | 513.81 |\n", |
|
|
409 |
"| 15 | Peramivir | SARS-CoV2 3CL Protease | 538.11 |\n", |
|
|
410 |
"| 16 | Amprenavir | SARS-CoV2 3CL Protease | 602.76 |\n", |
|
|
411 |
"| 17 | Telaprevir | SARS-CoV2 3CL Protease | 607.84 |\n", |
|
|
412 |
"| 18 | Grazoprevir | SARS-CoV2 3CL Protease | 632.54 |\n", |
|
|
413 |
"| 19 | Tenofovir | SARS-CoV2 3CL Protease | 637.96 |\n", |
|
|
414 |
"| 20 | Descovy | SARS-CoV2 3CL Protease | 637.96 |\n", |
|
|
415 |
"| 21 | Elvitegravir | SARS-CoV2 3CL Protease | 654.94 |\n", |
|
|
416 |
"| 22 | Atazanavir | SARS-CoV2 3CL Protease | 679.53 |\n", |
|
|
417 |
"| 23 | Nelfinavir | SARS-CoV2 3CL Protease | 727.49 |\n", |
|
|
418 |
"| 24 | Abacavir | SARS-CoV2 3CL Protease | 738.80 |\n", |
|
|
419 |
"| 25 | Tenofovir_disoproxil | SARS-CoV2 3CL Protease | 828.19 |\n", |
|
|
420 |
"| 26 | Delavirdine | SARS-CoV2 3CL Protease | 856.06 |\n", |
|
|
421 |
"| 27 | Tromantadine | SARS-CoV2 3CL Protease | 863.40 |\n", |
|
|
422 |
"| 28 | Saquinavir | SARS-CoV2 3CL Protease | 891.75 |\n", |
|
|
423 |
"| 29 | Dolutegravir | SARS-CoV2 3CL Protease | 920.32 |\n", |
|
|
424 |
"| 30 | Raltegravir | SARS-CoV2 3CL Protease | 938.42 |\n", |
|
|
425 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
426 |
"\n" |
|
|
427 |
] |
|
|
428 |
} |
|
|
429 |
], |
|
|
430 |
"source": [ |
|
|
431 |
"oneliner.repurpose(target = target, \n", |
|
|
432 |
" target_name = target_name, \n", |
|
|
433 |
" X_repurpose = X_repurpose,\n", |
|
|
434 |
" drug_names = drug_names,\n", |
|
|
435 |
" save_dir = './save_folder',\n", |
|
|
436 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
437 |
" agg = 'agg_mean_max')" |
|
|
438 |
] |
|
|
439 |
}, |
|
|
440 |
{ |
|
|
441 |
"cell_type": "code", |
|
|
442 |
"execution_count": 7, |
|
|
443 |
"metadata": { |
|
|
444 |
"scrolled": false |
|
|
445 |
}, |
|
|
446 |
"outputs": [ |
|
|
447 |
{ |
|
|
448 |
"name": "stdout", |
|
|
449 |
"output_type": "stream", |
|
|
450 |
"text": [ |
|
|
451 |
"Loading customized repurposing dataset...\n", |
|
|
452 |
"Checking if pretrained directory is valid...\n", |
|
|
453 |
"Beginning to load the pretrained models...\n", |
|
|
454 |
"Using pretrained model and making predictions...\n", |
|
|
455 |
"repurposing...\n", |
|
|
456 |
"in total: 82 drug-target pairs\n", |
|
|
457 |
"encoding drug...\n", |
|
|
458 |
"unique drugs: 81\n", |
|
|
459 |
"drug encoding finished...\n", |
|
|
460 |
"encoding protein...\n", |
|
|
461 |
"unique target sequence: 1\n", |
|
|
462 |
"protein encoding finished...\n", |
|
|
463 |
"Done.\n", |
|
|
464 |
"predicting...\n", |
|
|
465 |
"---------------\n", |
|
|
466 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
467 |
"-------------\n", |
|
|
468 |
"repurposing...\n", |
|
|
469 |
"in total: 82 drug-target pairs\n", |
|
|
470 |
"encoding drug...\n", |
|
|
471 |
"unique drugs: 81\n", |
|
|
472 |
"drug encoding finished...\n", |
|
|
473 |
"encoding protein...\n", |
|
|
474 |
"unique target sequence: 1\n", |
|
|
475 |
"protein encoding finished...\n", |
|
|
476 |
"Done.\n", |
|
|
477 |
"predicting...\n", |
|
|
478 |
"---------------\n", |
|
|
479 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
480 |
"-------------\n", |
|
|
481 |
"repurposing...\n", |
|
|
482 |
"in total: 82 drug-target pairs\n", |
|
|
483 |
"encoding drug...\n", |
|
|
484 |
"unique drugs: 81\n", |
|
|
485 |
"drug encoding finished...\n", |
|
|
486 |
"encoding protein...\n", |
|
|
487 |
"unique target sequence: 1\n", |
|
|
488 |
"protein encoding finished...\n", |
|
|
489 |
"Done.\n", |
|
|
490 |
"predicting...\n", |
|
|
491 |
"---------------\n", |
|
|
492 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
493 |
"-------------\n", |
|
|
494 |
"repurposing...\n", |
|
|
495 |
"in total: 82 drug-target pairs\n", |
|
|
496 |
"encoding drug...\n", |
|
|
497 |
"unique drugs: 81\n", |
|
|
498 |
"drug encoding finished...\n", |
|
|
499 |
"encoding protein...\n", |
|
|
500 |
"unique target sequence: 1\n", |
|
|
501 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
502 |
"protein encoding finished...\n", |
|
|
503 |
"Done.\n", |
|
|
504 |
"predicting...\n", |
|
|
505 |
"---------------\n", |
|
|
506 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
507 |
"-------------\n", |
|
|
508 |
"repurposing...\n", |
|
|
509 |
"in total: 82 drug-target pairs\n", |
|
|
510 |
"encoding drug...\n", |
|
|
511 |
"unique drugs: 81\n", |
|
|
512 |
"drug encoding finished...\n", |
|
|
513 |
"encoding protein...\n", |
|
|
514 |
"unique target sequence: 1\n", |
|
|
515 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
516 |
"protein encoding finished...\n", |
|
|
517 |
"Done.\n", |
|
|
518 |
"predicting...\n", |
|
|
519 |
"---------------\n", |
|
|
520 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
521 |
"-------------\n", |
|
|
522 |
"models prediction finished...\n", |
|
|
523 |
"aggregating results...\n", |
|
|
524 |
"---------------\n", |
|
|
525 |
"Drug Repurposing Result for RNA_polymerase_SARS_CoV2\n", |
|
|
526 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
527 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
528 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
529 |
"| 1 | Daclatasvir | RNA_polymerase_SARS_CoV2 | 380.69 |\n", |
|
|
530 |
"| 2 | Vicriviroc | RNA_polymerase_SARS_CoV2 | 404.62 |\n", |
|
|
531 |
"| 3 | Simeprevir | RNA_polymerase_SARS_CoV2 | 408.03 |\n", |
|
|
532 |
"| 4 | Sofosbuvir | RNA_polymerase_SARS_CoV2 | 647.43 |\n", |
|
|
533 |
"| 5 | Etravirine | RNA_polymerase_SARS_CoV2 | 735.35 |\n", |
|
|
534 |
"| 6 | Atazanavir | RNA_polymerase_SARS_CoV2 | 770.73 |\n", |
|
|
535 |
"| 7 | Rilpivirine | RNA_polymerase_SARS_CoV2 | 906.63 |\n", |
|
|
536 |
"| 8 | Maraviroc | RNA_polymerase_SARS_CoV2 | 911.97 |\n", |
|
|
537 |
"| 9 | Letermovir | RNA_polymerase_SARS_CoV2 | 932.10 |\n", |
|
|
538 |
"| 10 | Lopinavir | RNA_polymerase_SARS_CoV2 | 936.25 |\n", |
|
|
539 |
"| 11 | Darunavir | RNA_polymerase_SARS_CoV2 | 936.39 |\n", |
|
|
540 |
"| 12 | Fosamprenavir | RNA_polymerase_SARS_CoV2 | 945.02 |\n", |
|
|
541 |
"| 13 | Peramivir | RNA_polymerase_SARS_CoV2 | 964.85 |\n", |
|
|
542 |
"| 14 | Telaprevir | RNA_polymerase_SARS_CoV2 | 1123.34 |\n", |
|
|
543 |
"| 15 | Amprenavir | RNA_polymerase_SARS_CoV2 | 1202.55 |\n", |
|
|
544 |
"| 16 | Grazoprevir | RNA_polymerase_SARS_CoV2 | 1236.20 |\n", |
|
|
545 |
"| 17 | Nelfinavir | RNA_polymerase_SARS_CoV2 | 1252.00 |\n", |
|
|
546 |
"| 18 | Boceprevir | RNA_polymerase_SARS_CoV2 | 1391.99 |\n", |
|
|
547 |
"| 19 | Raltegravir | RNA_polymerase_SARS_CoV2 | 1552.78 |\n", |
|
|
548 |
"| 20 | Abacavir | RNA_polymerase_SARS_CoV2 | 1660.44 |\n", |
|
|
549 |
"| 21 | Dolutegravir | RNA_polymerase_SARS_CoV2 | 1718.55 |\n", |
|
|
550 |
"| 22 | Delavirdine | RNA_polymerase_SARS_CoV2 | 1746.95 |\n", |
|
|
551 |
"| 23 | Doravirine | RNA_polymerase_SARS_CoV2 | 1763.03 |\n", |
|
|
552 |
"| 24 | Elvitegravir | RNA_polymerase_SARS_CoV2 | 1821.02 |\n", |
|
|
553 |
"| 25 | Saquinavir | RNA_polymerase_SARS_CoV2 | 1829.28 |\n", |
|
|
554 |
"| 26 | Enfuvirtide | RNA_polymerase_SARS_CoV2 | 2177.03 |\n", |
|
|
555 |
"| 27 | Pleconaril | RNA_polymerase_SARS_CoV2 | 2266.15 |\n", |
|
|
556 |
"| 28 | Glecaprevir | RNA_polymerase_SARS_CoV2 | 2306.74 |\n", |
|
|
557 |
"| 29 | Amantadine | RNA_polymerase_SARS_CoV2 | 2434.83 |\n", |
|
|
558 |
"| 30 | Efavirenz | RNA_polymerase_SARS_CoV2 | 2617.99 |\n", |
|
|
559 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
560 |
"\n" |
|
|
561 |
] |
|
|
562 |
} |
|
|
563 |
], |
|
|
564 |
"source": [ |
|
|
565 |
"target, target_name = dataset.load_SARS_CoV2_RNA_polymerase()\n", |
|
|
566 |
"oneliner.repurpose(target = target, \n", |
|
|
567 |
" target_name = target_name, \n", |
|
|
568 |
" X_repurpose = X_repurpose,\n", |
|
|
569 |
" drug_names = drug_names,\n", |
|
|
570 |
" save_dir = './save_folder',\n", |
|
|
571 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
572 |
" agg = 'mean')" |
|
|
573 |
] |
|
|
574 |
}, |
|
|
575 |
{ |
|
|
576 |
"cell_type": "code", |
|
|
577 |
"execution_count": 8, |
|
|
578 |
"metadata": { |
|
|
579 |
"scrolled": false |
|
|
580 |
}, |
|
|
581 |
"outputs": [ |
|
|
582 |
{ |
|
|
583 |
"name": "stdout", |
|
|
584 |
"output_type": "stream", |
|
|
585 |
"text": [ |
|
|
586 |
"Loading customized repurposing dataset...\n", |
|
|
587 |
"Checking if pretrained directory is valid...\n", |
|
|
588 |
"Beginning to load the pretrained models...\n", |
|
|
589 |
"Using pretrained model and making predictions...\n", |
|
|
590 |
"repurposing...\n", |
|
|
591 |
"in total: 82 drug-target pairs\n", |
|
|
592 |
"encoding drug...\n", |
|
|
593 |
"unique drugs: 81\n", |
|
|
594 |
"drug encoding finished...\n", |
|
|
595 |
"encoding protein...\n", |
|
|
596 |
"unique target sequence: 1\n", |
|
|
597 |
"protein encoding finished...\n", |
|
|
598 |
"Done.\n", |
|
|
599 |
"predicting...\n", |
|
|
600 |
"---------------\n", |
|
|
601 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
602 |
"-------------\n", |
|
|
603 |
"repurposing...\n", |
|
|
604 |
"in total: 82 drug-target pairs\n", |
|
|
605 |
"encoding drug...\n", |
|
|
606 |
"unique drugs: 81\n", |
|
|
607 |
"drug encoding finished...\n", |
|
|
608 |
"encoding protein...\n", |
|
|
609 |
"unique target sequence: 1\n", |
|
|
610 |
"protein encoding finished...\n", |
|
|
611 |
"Done.\n", |
|
|
612 |
"predicting...\n", |
|
|
613 |
"---------------\n", |
|
|
614 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
615 |
"-------------\n", |
|
|
616 |
"repurposing...\n", |
|
|
617 |
"in total: 82 drug-target pairs\n", |
|
|
618 |
"encoding drug...\n", |
|
|
619 |
"unique drugs: 81\n", |
|
|
620 |
"drug encoding finished...\n", |
|
|
621 |
"encoding protein...\n", |
|
|
622 |
"unique target sequence: 1\n", |
|
|
623 |
"protein encoding finished...\n", |
|
|
624 |
"Done.\n", |
|
|
625 |
"predicting...\n", |
|
|
626 |
"---------------\n", |
|
|
627 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
628 |
"-------------\n", |
|
|
629 |
"repurposing...\n", |
|
|
630 |
"in total: 82 drug-target pairs\n", |
|
|
631 |
"encoding drug...\n", |
|
|
632 |
"unique drugs: 81\n", |
|
|
633 |
"drug encoding finished...\n", |
|
|
634 |
"encoding protein...\n", |
|
|
635 |
"unique target sequence: 1\n", |
|
|
636 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
637 |
"protein encoding finished...\n", |
|
|
638 |
"Done.\n", |
|
|
639 |
"predicting...\n", |
|
|
640 |
"---------------\n", |
|
|
641 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
642 |
"-------------\n", |
|
|
643 |
"repurposing...\n", |
|
|
644 |
"in total: 82 drug-target pairs\n", |
|
|
645 |
"encoding drug...\n", |
|
|
646 |
"unique drugs: 81\n", |
|
|
647 |
"drug encoding finished...\n", |
|
|
648 |
"encoding protein...\n", |
|
|
649 |
"unique target sequence: 1\n", |
|
|
650 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
651 |
"protein encoding finished...\n", |
|
|
652 |
"Done.\n", |
|
|
653 |
"predicting...\n", |
|
|
654 |
"---------------\n", |
|
|
655 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
656 |
"-------------\n", |
|
|
657 |
"models prediction finished...\n", |
|
|
658 |
"aggregating results...\n", |
|
|
659 |
"---------------\n", |
|
|
660 |
"Drug Repurposing Result for RNA_polymerase_SARS_CoV2\n", |
|
|
661 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
662 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
663 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
664 |
"| 1 | Lopinavir | RNA_polymerase_SARS_CoV2 | 0.28 |\n", |
|
|
665 |
"| 2 | Darunavir | RNA_polymerase_SARS_CoV2 | 0.36 |\n", |
|
|
666 |
"| 3 | Amprenavir | RNA_polymerase_SARS_CoV2 | 1.22 |\n", |
|
|
667 |
"| 4 | Tipranavir | RNA_polymerase_SARS_CoV2 | 5.41 |\n", |
|
|
668 |
"| 5 | Sofosbuvir | RNA_polymerase_SARS_CoV2 | 6.37 |\n", |
|
|
669 |
"| 6 | Daclatasvir | RNA_polymerase_SARS_CoV2 | 6.66 |\n", |
|
|
670 |
"| 7 | Baloxavir | RNA_polymerase_SARS_CoV2 | 7.39 |\n", |
|
|
671 |
"| 8 | Pleconaril | RNA_polymerase_SARS_CoV2 | 7.54 |\n", |
|
|
672 |
"| 9 | Boceprevir | RNA_polymerase_SARS_CoV2 | 8.10 |\n", |
|
|
673 |
"| 10 | Vicriviroc | RNA_polymerase_SARS_CoV2 | 8.32 |\n", |
|
|
674 |
"| 11 | Fosamprenavir | RNA_polymerase_SARS_CoV2 | 8.46 |\n", |
|
|
675 |
"| 12 | Tenofovir | RNA_polymerase_SARS_CoV2 | 10.18 |\n", |
|
|
676 |
"| 13 | Descovy | RNA_polymerase_SARS_CoV2 | 10.18 |\n", |
|
|
677 |
"| 14 | Foscarnet | RNA_polymerase_SARS_CoV2 | 10.84 |\n", |
|
|
678 |
"| 15 | Nelfinavir | RNA_polymerase_SARS_CoV2 | 11.13 |\n", |
|
|
679 |
"| 16 | Oseltamivir | RNA_polymerase_SARS_CoV2 | 11.73 |\n", |
|
|
680 |
"| 17 | Maraviroc | RNA_polymerase_SARS_CoV2 | 11.84 |\n", |
|
|
681 |
"| 18 | Glecaprevir | RNA_polymerase_SARS_CoV2 | 11.87 |\n", |
|
|
682 |
"| 19 | Amantadine | RNA_polymerase_SARS_CoV2 | 12.28 |\n", |
|
|
683 |
"| 20 | Telaprevir | RNA_polymerase_SARS_CoV2 | 12.56 |\n", |
|
|
684 |
"| 21 | Arbidol | RNA_polymerase_SARS_CoV2 | 14.80 |\n", |
|
|
685 |
"| 22 | Remdesivir | RNA_polymerase_SARS_CoV2 | 18.93 |\n", |
|
|
686 |
"| 23 | Letermovir | RNA_polymerase_SARS_CoV2 | 20.34 |\n", |
|
|
687 |
"| 24 | Abacavir | RNA_polymerase_SARS_CoV2 | 24.28 |\n", |
|
|
688 |
"| 25 | Saquinavir | RNA_polymerase_SARS_CoV2 | 25.48 |\n", |
|
|
689 |
"| 26 | Rimantadine | RNA_polymerase_SARS_CoV2 | 37.71 |\n", |
|
|
690 |
"| 27 | Rilpivirine | RNA_polymerase_SARS_CoV2 | 38.50 |\n", |
|
|
691 |
"| 28 | Delavirdine | RNA_polymerase_SARS_CoV2 | 40.78 |\n", |
|
|
692 |
"| 29 | Ritonavir | RNA_polymerase_SARS_CoV2 | 43.73 |\n", |
|
|
693 |
"| 30 | Loviride | RNA_polymerase_SARS_CoV2 | 63.94 |\n", |
|
|
694 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
695 |
"\n" |
|
|
696 |
] |
|
|
697 |
} |
|
|
698 |
], |
|
|
699 |
"source": [ |
|
|
700 |
"oneliner.repurpose(target = target, \n", |
|
|
701 |
" target_name = target_name, \n", |
|
|
702 |
" X_repurpose = X_repurpose,\n", |
|
|
703 |
" drug_names = drug_names,\n", |
|
|
704 |
" save_dir = './save_folder',\n", |
|
|
705 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
706 |
" agg = 'max_effect')" |
|
|
707 |
] |
|
|
708 |
}, |
|
|
709 |
{ |
|
|
710 |
"cell_type": "code", |
|
|
711 |
"execution_count": 9, |
|
|
712 |
"metadata": {}, |
|
|
713 |
"outputs": [ |
|
|
714 |
{ |
|
|
715 |
"name": "stdout", |
|
|
716 |
"output_type": "stream", |
|
|
717 |
"text": [ |
|
|
718 |
"Loading customized repurposing dataset...\n", |
|
|
719 |
"Checking if pretrained directory is valid...\n", |
|
|
720 |
"Beginning to load the pretrained models...\n", |
|
|
721 |
"Using pretrained model and making predictions...\n", |
|
|
722 |
"repurposing...\n", |
|
|
723 |
"in total: 82 drug-target pairs\n", |
|
|
724 |
"encoding drug...\n", |
|
|
725 |
"unique drugs: 81\n", |
|
|
726 |
"drug encoding finished...\n", |
|
|
727 |
"encoding protein...\n", |
|
|
728 |
"unique target sequence: 1\n", |
|
|
729 |
"protein encoding finished...\n", |
|
|
730 |
"Done.\n", |
|
|
731 |
"predicting...\n", |
|
|
732 |
"---------------\n", |
|
|
733 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
734 |
"-------------\n", |
|
|
735 |
"repurposing...\n", |
|
|
736 |
"in total: 82 drug-target pairs\n", |
|
|
737 |
"encoding drug...\n", |
|
|
738 |
"unique drugs: 81\n", |
|
|
739 |
"drug encoding finished...\n", |
|
|
740 |
"encoding protein...\n", |
|
|
741 |
"unique target sequence: 1\n", |
|
|
742 |
"protein encoding finished...\n", |
|
|
743 |
"Done.\n", |
|
|
744 |
"predicting...\n", |
|
|
745 |
"---------------\n", |
|
|
746 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
747 |
"-------------\n", |
|
|
748 |
"repurposing...\n", |
|
|
749 |
"in total: 82 drug-target pairs\n", |
|
|
750 |
"encoding drug...\n", |
|
|
751 |
"unique drugs: 81\n", |
|
|
752 |
"drug encoding finished...\n", |
|
|
753 |
"encoding protein...\n", |
|
|
754 |
"unique target sequence: 1\n", |
|
|
755 |
"protein encoding finished...\n", |
|
|
756 |
"Done.\n", |
|
|
757 |
"predicting...\n", |
|
|
758 |
"---------------\n", |
|
|
759 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
760 |
"-------------\n", |
|
|
761 |
"repurposing...\n", |
|
|
762 |
"in total: 82 drug-target pairs\n", |
|
|
763 |
"encoding drug...\n", |
|
|
764 |
"unique drugs: 81\n", |
|
|
765 |
"drug encoding finished...\n", |
|
|
766 |
"encoding protein...\n", |
|
|
767 |
"unique target sequence: 1\n", |
|
|
768 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
769 |
"protein encoding finished...\n", |
|
|
770 |
"Done.\n", |
|
|
771 |
"predicting...\n", |
|
|
772 |
"---------------\n", |
|
|
773 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
774 |
"-------------\n", |
|
|
775 |
"repurposing...\n", |
|
|
776 |
"in total: 82 drug-target pairs\n", |
|
|
777 |
"encoding drug...\n", |
|
|
778 |
"unique drugs: 81\n", |
|
|
779 |
"drug encoding finished...\n", |
|
|
780 |
"encoding protein...\n", |
|
|
781 |
"unique target sequence: 1\n", |
|
|
782 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
783 |
"protein encoding finished...\n", |
|
|
784 |
"Done.\n", |
|
|
785 |
"predicting...\n", |
|
|
786 |
"---------------\n", |
|
|
787 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
788 |
"-------------\n", |
|
|
789 |
"models prediction finished...\n", |
|
|
790 |
"aggregating results...\n", |
|
|
791 |
"---------------\n", |
|
|
792 |
"Drug Repurposing Result for RNA_polymerase_SARS_CoV2\n", |
|
|
793 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
794 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
795 |
"+------+----------------------+--------------------------+---------------+\n", |
|
|
796 |
"| 1 | Daclatasvir | RNA_polymerase_SARS_CoV2 | 193.68 |\n", |
|
|
797 |
"| 2 | Vicriviroc | RNA_polymerase_SARS_CoV2 | 206.47 |\n", |
|
|
798 |
"| 3 | Simeprevir | RNA_polymerase_SARS_CoV2 | 247.13 |\n", |
|
|
799 |
"| 4 | Sofosbuvir | RNA_polymerase_SARS_CoV2 | 326.90 |\n", |
|
|
800 |
"| 5 | Etravirine | RNA_polymerase_SARS_CoV2 | 420.95 |\n", |
|
|
801 |
"| 6 | Atazanavir | RNA_polymerase_SARS_CoV2 | 422.32 |\n", |
|
|
802 |
"| 7 | Maraviroc | RNA_polymerase_SARS_CoV2 | 461.91 |\n", |
|
|
803 |
"| 8 | Lopinavir | RNA_polymerase_SARS_CoV2 | 468.27 |\n", |
|
|
804 |
"| 9 | Darunavir | RNA_polymerase_SARS_CoV2 | 468.37 |\n", |
|
|
805 |
"| 10 | Rilpivirine | RNA_polymerase_SARS_CoV2 | 472.57 |\n", |
|
|
806 |
"| 11 | Letermovir | RNA_polymerase_SARS_CoV2 | 476.22 |\n", |
|
|
807 |
"| 12 | Fosamprenavir | RNA_polymerase_SARS_CoV2 | 476.74 |\n", |
|
|
808 |
"| 13 | Peramivir | RNA_polymerase_SARS_CoV2 | 515.97 |\n", |
|
|
809 |
"| 14 | Telaprevir | RNA_polymerase_SARS_CoV2 | 567.95 |\n", |
|
|
810 |
"| 15 | Amprenavir | RNA_polymerase_SARS_CoV2 | 601.88 |\n", |
|
|
811 |
"| 16 | Nelfinavir | RNA_polymerase_SARS_CoV2 | 631.56 |\n", |
|
|
812 |
"| 17 | Grazoprevir | RNA_polymerase_SARS_CoV2 | 657.71 |\n", |
|
|
813 |
"| 18 | Boceprevir | RNA_polymerase_SARS_CoV2 | 700.05 |\n", |
|
|
814 |
"| 19 | Abacavir | RNA_polymerase_SARS_CoV2 | 842.36 |\n", |
|
|
815 |
"| 20 | Raltegravir | RNA_polymerase_SARS_CoV2 | 870.85 |\n", |
|
|
816 |
"| 21 | Delavirdine | RNA_polymerase_SARS_CoV2 | 893.87 |\n", |
|
|
817 |
"| 22 | Saquinavir | RNA_polymerase_SARS_CoV2 | 927.38 |\n", |
|
|
818 |
"| 23 | Elvitegravir | RNA_polymerase_SARS_CoV2 | 983.21 |\n", |
|
|
819 |
"| 24 | Doravirine | RNA_polymerase_SARS_CoV2 | 1034.73 |\n", |
|
|
820 |
"| 25 | Dolutegravir | RNA_polymerase_SARS_CoV2 | 1096.64 |\n", |
|
|
821 |
"| 26 | Pleconaril | RNA_polymerase_SARS_CoV2 | 1136.85 |\n", |
|
|
822 |
"| 27 | Glecaprevir | RNA_polymerase_SARS_CoV2 | 1159.31 |\n", |
|
|
823 |
"| 28 | Enfuvirtide | RNA_polymerase_SARS_CoV2 | 1212.25 |\n", |
|
|
824 |
"| 29 | Amantadine | RNA_polymerase_SARS_CoV2 | 1223.55 |\n", |
|
|
825 |
"| 30 | Ritonavir | RNA_polymerase_SARS_CoV2 | 1395.41 |\n", |
|
|
826 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
827 |
"\n" |
|
|
828 |
] |
|
|
829 |
} |
|
|
830 |
], |
|
|
831 |
"source": [ |
|
|
832 |
"oneliner.repurpose(target = target, \n", |
|
|
833 |
" target_name = target_name, \n", |
|
|
834 |
" X_repurpose = X_repurpose,\n", |
|
|
835 |
" drug_names = drug_names,\n", |
|
|
836 |
" save_dir = './save_folder',\n", |
|
|
837 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
838 |
" agg = 'agg_mean_max')" |
|
|
839 |
] |
|
|
840 |
}, |
|
|
841 |
{ |
|
|
842 |
"cell_type": "code", |
|
|
843 |
"execution_count": 10, |
|
|
844 |
"metadata": { |
|
|
845 |
"scrolled": false |
|
|
846 |
}, |
|
|
847 |
"outputs": [ |
|
|
848 |
{ |
|
|
849 |
"name": "stdout", |
|
|
850 |
"output_type": "stream", |
|
|
851 |
"text": [ |
|
|
852 |
"Loading customized repurposing dataset...\n", |
|
|
853 |
"Checking if pretrained directory is valid...\n", |
|
|
854 |
"Beginning to load the pretrained models...\n", |
|
|
855 |
"Using pretrained model and making predictions...\n", |
|
|
856 |
"repurposing...\n", |
|
|
857 |
"in total: 82 drug-target pairs\n", |
|
|
858 |
"encoding drug...\n", |
|
|
859 |
"unique drugs: 81\n", |
|
|
860 |
"drug encoding finished...\n", |
|
|
861 |
"encoding protein...\n", |
|
|
862 |
"unique target sequence: 1\n", |
|
|
863 |
"protein encoding finished...\n", |
|
|
864 |
"Done.\n", |
|
|
865 |
"predicting...\n", |
|
|
866 |
"---------------\n", |
|
|
867 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
868 |
"-------------\n", |
|
|
869 |
"repurposing...\n", |
|
|
870 |
"in total: 82 drug-target pairs\n", |
|
|
871 |
"encoding drug...\n", |
|
|
872 |
"unique drugs: 81\n", |
|
|
873 |
"drug encoding finished...\n", |
|
|
874 |
"encoding protein...\n", |
|
|
875 |
"unique target sequence: 1\n", |
|
|
876 |
"protein encoding finished...\n", |
|
|
877 |
"Done.\n", |
|
|
878 |
"predicting...\n", |
|
|
879 |
"---------------\n", |
|
|
880 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
881 |
"-------------\n", |
|
|
882 |
"repurposing...\n", |
|
|
883 |
"in total: 82 drug-target pairs\n", |
|
|
884 |
"encoding drug...\n", |
|
|
885 |
"unique drugs: 81\n", |
|
|
886 |
"drug encoding finished...\n", |
|
|
887 |
"encoding protein...\n", |
|
|
888 |
"unique target sequence: 1\n", |
|
|
889 |
"protein encoding finished...\n", |
|
|
890 |
"Done.\n", |
|
|
891 |
"predicting...\n", |
|
|
892 |
"---------------\n", |
|
|
893 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
894 |
"-------------\n", |
|
|
895 |
"repurposing...\n", |
|
|
896 |
"in total: 82 drug-target pairs\n", |
|
|
897 |
"encoding drug...\n", |
|
|
898 |
"unique drugs: 81\n", |
|
|
899 |
"drug encoding finished...\n", |
|
|
900 |
"encoding protein...\n", |
|
|
901 |
"unique target sequence: 1\n", |
|
|
902 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
903 |
"protein encoding finished...\n", |
|
|
904 |
"Done.\n", |
|
|
905 |
"predicting...\n", |
|
|
906 |
"---------------\n", |
|
|
907 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
908 |
"-------------\n", |
|
|
909 |
"repurposing...\n", |
|
|
910 |
"in total: 82 drug-target pairs\n", |
|
|
911 |
"encoding drug...\n", |
|
|
912 |
"unique drugs: 81\n", |
|
|
913 |
"drug encoding finished...\n", |
|
|
914 |
"encoding protein...\n", |
|
|
915 |
"unique target sequence: 1\n", |
|
|
916 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
917 |
"protein encoding finished...\n", |
|
|
918 |
"Done.\n", |
|
|
919 |
"predicting...\n", |
|
|
920 |
"---------------\n", |
|
|
921 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
922 |
"-------------\n", |
|
|
923 |
"models prediction finished...\n", |
|
|
924 |
"aggregating results...\n", |
|
|
925 |
"---------------\n", |
|
|
926 |
"Drug Repurposing Result for SARS_CoV2_Helicase\n", |
|
|
927 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
928 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
929 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
930 |
"| 1 | Daclatasvir | SARS_CoV2_Helicase | 422.51 |\n", |
|
|
931 |
"| 2 | Simeprevir | SARS_CoV2_Helicase | 436.10 |\n", |
|
|
932 |
"| 3 | Sofosbuvir | SARS_CoV2_Helicase | 460.44 |\n", |
|
|
933 |
"| 4 | Vicriviroc | SARS_CoV2_Helicase | 624.43 |\n", |
|
|
934 |
"| 5 | Etravirine | SARS_CoV2_Helicase | 749.41 |\n", |
|
|
935 |
"| 6 | Atazanavir | SARS_CoV2_Helicase | 822.11 |\n", |
|
|
936 |
"| 7 | Rilpivirine | SARS_CoV2_Helicase | 896.30 |\n", |
|
|
937 |
"| 8 | Letermovir | SARS_CoV2_Helicase | 904.84 |\n", |
|
|
938 |
"| 9 | Grazoprevir | SARS_CoV2_Helicase | 944.09 |\n", |
|
|
939 |
"| 10 | Maraviroc | SARS_CoV2_Helicase | 958.09 |\n", |
|
|
940 |
"| 11 | Lopinavir | SARS_CoV2_Helicase | 959.09 |\n", |
|
|
941 |
"| 12 | Darunavir | SARS_CoV2_Helicase | 960.77 |\n", |
|
|
942 |
"| 13 | Peramivir | SARS_CoV2_Helicase | 971.53 |\n", |
|
|
943 |
"| 14 | Fosamprenavir | SARS_CoV2_Helicase | 982.04 |\n", |
|
|
944 |
"| 15 | Tenofovir_disoproxil | SARS_CoV2_Helicase | 1025.72 |\n", |
|
|
945 |
"| 16 | Amantadine | SARS_CoV2_Helicase | 1067.12 |\n", |
|
|
946 |
"| 17 | Efavirenz | SARS_CoV2_Helicase | 1116.72 |\n", |
|
|
947 |
"| 18 | Telaprevir | SARS_CoV2_Helicase | 1188.83 |\n", |
|
|
948 |
"| 19 | Amprenavir | SARS_CoV2_Helicase | 1229.83 |\n", |
|
|
949 |
"| 20 | Elvitegravir | SARS_CoV2_Helicase | 1338.24 |\n", |
|
|
950 |
"| 21 | Nelfinavir | SARS_CoV2_Helicase | 1339.03 |\n", |
|
|
951 |
"| 22 | Tenofovir | SARS_CoV2_Helicase | 1370.97 |\n", |
|
|
952 |
"| 23 | Descovy | SARS_CoV2_Helicase | 1370.97 |\n", |
|
|
953 |
"| 24 | Ritonavir | SARS_CoV2_Helicase | 1405.82 |\n", |
|
|
954 |
"| 25 | Doravirine | SARS_CoV2_Helicase | 1477.56 |\n", |
|
|
955 |
"| 26 | Abacavir | SARS_CoV2_Helicase | 1498.16 |\n", |
|
|
956 |
"| 27 | Boceprevir | SARS_CoV2_Helicase | 1757.84 |\n", |
|
|
957 |
"| 28 | Dolutegravir | SARS_CoV2_Helicase | 1764.69 |\n", |
|
|
958 |
"| 29 | Pleconaril | SARS_CoV2_Helicase | 1787.40 |\n", |
|
|
959 |
"| 30 | Delavirdine | SARS_CoV2_Helicase | 1796.55 |\n", |
|
|
960 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
961 |
"\n" |
|
|
962 |
] |
|
|
963 |
} |
|
|
964 |
], |
|
|
965 |
"source": [ |
|
|
966 |
"target, target_name = dataset.load_SARS_CoV2_Helicase()\n", |
|
|
967 |
"oneliner.repurpose(target = target, \n", |
|
|
968 |
" target_name = target_name, \n", |
|
|
969 |
" X_repurpose = X_repurpose,\n", |
|
|
970 |
" drug_names = drug_names,\n", |
|
|
971 |
" save_dir = './save_folder',\n", |
|
|
972 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
973 |
" agg = 'mean')" |
|
|
974 |
] |
|
|
975 |
}, |
|
|
976 |
{ |
|
|
977 |
"cell_type": "code", |
|
|
978 |
"execution_count": 11, |
|
|
979 |
"metadata": { |
|
|
980 |
"scrolled": false |
|
|
981 |
}, |
|
|
982 |
"outputs": [ |
|
|
983 |
{ |
|
|
984 |
"name": "stdout", |
|
|
985 |
"output_type": "stream", |
|
|
986 |
"text": [ |
|
|
987 |
"Loading customized repurposing dataset...\n", |
|
|
988 |
"Checking if pretrained directory is valid...\n", |
|
|
989 |
"Beginning to load the pretrained models...\n", |
|
|
990 |
"Using pretrained model and making predictions...\n", |
|
|
991 |
"repurposing...\n", |
|
|
992 |
"in total: 82 drug-target pairs\n", |
|
|
993 |
"encoding drug...\n", |
|
|
994 |
"unique drugs: 81\n", |
|
|
995 |
"drug encoding finished...\n", |
|
|
996 |
"encoding protein...\n", |
|
|
997 |
"unique target sequence: 1\n", |
|
|
998 |
"protein encoding finished...\n", |
|
|
999 |
"Done.\n", |
|
|
1000 |
"predicting...\n", |
|
|
1001 |
"---------------\n", |
|
|
1002 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1003 |
"-------------\n", |
|
|
1004 |
"repurposing...\n", |
|
|
1005 |
"in total: 82 drug-target pairs\n", |
|
|
1006 |
"encoding drug...\n", |
|
|
1007 |
"unique drugs: 81\n", |
|
|
1008 |
"drug encoding finished...\n", |
|
|
1009 |
"encoding protein...\n", |
|
|
1010 |
"unique target sequence: 1\n", |
|
|
1011 |
"protein encoding finished...\n", |
|
|
1012 |
"Done.\n", |
|
|
1013 |
"predicting...\n", |
|
|
1014 |
"---------------\n", |
|
|
1015 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1016 |
"-------------\n", |
|
|
1017 |
"repurposing...\n", |
|
|
1018 |
"in total: 82 drug-target pairs\n", |
|
|
1019 |
"encoding drug...\n", |
|
|
1020 |
"unique drugs: 81\n", |
|
|
1021 |
"drug encoding finished...\n", |
|
|
1022 |
"encoding protein...\n", |
|
|
1023 |
"unique target sequence: 1\n", |
|
|
1024 |
"protein encoding finished...\n", |
|
|
1025 |
"Done.\n", |
|
|
1026 |
"predicting...\n", |
|
|
1027 |
"---------------\n", |
|
|
1028 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1029 |
"-------------\n", |
|
|
1030 |
"repurposing...\n", |
|
|
1031 |
"in total: 82 drug-target pairs\n", |
|
|
1032 |
"encoding drug...\n", |
|
|
1033 |
"unique drugs: 81\n", |
|
|
1034 |
"drug encoding finished...\n", |
|
|
1035 |
"encoding protein...\n", |
|
|
1036 |
"unique target sequence: 1\n", |
|
|
1037 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1038 |
"protein encoding finished...\n", |
|
|
1039 |
"Done.\n", |
|
|
1040 |
"predicting...\n", |
|
|
1041 |
"---------------\n", |
|
|
1042 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1043 |
"-------------\n", |
|
|
1044 |
"repurposing...\n", |
|
|
1045 |
"in total: 82 drug-target pairs\n", |
|
|
1046 |
"encoding drug...\n", |
|
|
1047 |
"unique drugs: 81\n", |
|
|
1048 |
"drug encoding finished...\n", |
|
|
1049 |
"encoding protein...\n", |
|
|
1050 |
"unique target sequence: 1\n", |
|
|
1051 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1052 |
"protein encoding finished...\n", |
|
|
1053 |
"Done.\n", |
|
|
1054 |
"predicting...\n", |
|
|
1055 |
"---------------\n", |
|
|
1056 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1057 |
"-------------\n", |
|
|
1058 |
"models prediction finished...\n", |
|
|
1059 |
"aggregating results...\n", |
|
|
1060 |
"---------------\n", |
|
|
1061 |
"Drug Repurposing Result for SARS_CoV2_Helicase\n", |
|
|
1062 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
1063 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1064 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
1065 |
"| 1 | Lopinavir | SARS_CoV2_Helicase | 0.26 |\n", |
|
|
1066 |
"| 2 | Darunavir | SARS_CoV2_Helicase | 0.33 |\n", |
|
|
1067 |
"| 3 | Amprenavir | SARS_CoV2_Helicase | 0.96 |\n", |
|
|
1068 |
"| 4 | Tipranavir | SARS_CoV2_Helicase | 2.84 |\n", |
|
|
1069 |
"| 5 | Baloxavir | SARS_CoV2_Helicase | 4.24 |\n", |
|
|
1070 |
"| 6 | Boceprevir | SARS_CoV2_Helicase | 4.34 |\n", |
|
|
1071 |
"| 7 | Vicriviroc | SARS_CoV2_Helicase | 5.51 |\n", |
|
|
1072 |
"| 8 | Fosamprenavir | SARS_CoV2_Helicase | 5.58 |\n", |
|
|
1073 |
"| 9 | Oseltamivir | SARS_CoV2_Helicase | 5.73 |\n", |
|
|
1074 |
"| 10 | Glecaprevir | SARS_CoV2_Helicase | 5.78 |\n", |
|
|
1075 |
"| 11 | Telaprevir | SARS_CoV2_Helicase | 6.22 |\n", |
|
|
1076 |
"| 12 | Daclatasvir | SARS_CoV2_Helicase | 6.50 |\n", |
|
|
1077 |
"| 13 | Nelfinavir | SARS_CoV2_Helicase | 8.26 |\n", |
|
|
1078 |
"| 14 | Amantadine | SARS_CoV2_Helicase | 10.22 |\n", |
|
|
1079 |
"| 15 | Pleconaril | SARS_CoV2_Helicase | 11.02 |\n", |
|
|
1080 |
"| 16 | Maraviroc | SARS_CoV2_Helicase | 11.37 |\n", |
|
|
1081 |
"| 17 | Foscarnet | SARS_CoV2_Helicase | 11.44 |\n", |
|
|
1082 |
"| 18 | Sofosbuvir | SARS_CoV2_Helicase | 12.00 |\n", |
|
|
1083 |
"| 19 | Abacavir | SARS_CoV2_Helicase | 15.96 |\n", |
|
|
1084 |
"| 20 | Tenofovir | SARS_CoV2_Helicase | 18.68 |\n", |
|
|
1085 |
"| 21 | Descovy | SARS_CoV2_Helicase | 18.68 |\n", |
|
|
1086 |
"| 22 | Arbidol | SARS_CoV2_Helicase | 18.93 |\n", |
|
|
1087 |
"| 23 | Letermovir | SARS_CoV2_Helicase | 20.55 |\n", |
|
|
1088 |
"| 24 | Ritonavir | SARS_CoV2_Helicase | 26.00 |\n", |
|
|
1089 |
"| 25 | Rimantadine | SARS_CoV2_Helicase | 26.93 |\n", |
|
|
1090 |
"| 26 | Remdesivir | SARS_CoV2_Helicase | 27.36 |\n", |
|
|
1091 |
"| 27 | Atazanavir | SARS_CoV2_Helicase | 29.60 |\n", |
|
|
1092 |
"| 28 | Saquinavir | SARS_CoV2_Helicase | 29.99 |\n", |
|
|
1093 |
"| 29 | Simeprevir | SARS_CoV2_Helicase | 32.97 |\n", |
|
|
1094 |
"| 30 | Etravirine | SARS_CoV2_Helicase | 34.18 |\n", |
|
|
1095 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1096 |
"\n" |
|
|
1097 |
] |
|
|
1098 |
} |
|
|
1099 |
], |
|
|
1100 |
"source": [ |
|
|
1101 |
"oneliner.repurpose(target = target, \n", |
|
|
1102 |
" target_name = target_name, \n", |
|
|
1103 |
" X_repurpose = X_repurpose,\n", |
|
|
1104 |
" drug_names = drug_names,\n", |
|
|
1105 |
" save_dir = './save_folder',\n", |
|
|
1106 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1107 |
" agg = 'max_effect')" |
|
|
1108 |
] |
|
|
1109 |
}, |
|
|
1110 |
{ |
|
|
1111 |
"cell_type": "code", |
|
|
1112 |
"execution_count": 12, |
|
|
1113 |
"metadata": {}, |
|
|
1114 |
"outputs": [ |
|
|
1115 |
{ |
|
|
1116 |
"name": "stdout", |
|
|
1117 |
"output_type": "stream", |
|
|
1118 |
"text": [ |
|
|
1119 |
"Loading customized repurposing dataset...\n", |
|
|
1120 |
"Checking if pretrained directory is valid...\n", |
|
|
1121 |
"Beginning to load the pretrained models...\n", |
|
|
1122 |
"Using pretrained model and making predictions...\n", |
|
|
1123 |
"repurposing...\n", |
|
|
1124 |
"in total: 82 drug-target pairs\n", |
|
|
1125 |
"encoding drug...\n", |
|
|
1126 |
"unique drugs: 81\n", |
|
|
1127 |
"drug encoding finished...\n", |
|
|
1128 |
"encoding protein...\n", |
|
|
1129 |
"unique target sequence: 1\n", |
|
|
1130 |
"protein encoding finished...\n", |
|
|
1131 |
"Done.\n", |
|
|
1132 |
"predicting...\n", |
|
|
1133 |
"---------------\n", |
|
|
1134 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1135 |
"-------------\n", |
|
|
1136 |
"repurposing...\n", |
|
|
1137 |
"in total: 82 drug-target pairs\n", |
|
|
1138 |
"encoding drug...\n", |
|
|
1139 |
"unique drugs: 81\n", |
|
|
1140 |
"drug encoding finished...\n", |
|
|
1141 |
"encoding protein...\n", |
|
|
1142 |
"unique target sequence: 1\n", |
|
|
1143 |
"protein encoding finished...\n", |
|
|
1144 |
"Done.\n", |
|
|
1145 |
"predicting...\n", |
|
|
1146 |
"---------------\n", |
|
|
1147 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1148 |
"-------------\n", |
|
|
1149 |
"repurposing...\n", |
|
|
1150 |
"in total: 82 drug-target pairs\n", |
|
|
1151 |
"encoding drug...\n", |
|
|
1152 |
"unique drugs: 81\n", |
|
|
1153 |
"drug encoding finished...\n", |
|
|
1154 |
"encoding protein...\n", |
|
|
1155 |
"unique target sequence: 1\n", |
|
|
1156 |
"protein encoding finished...\n", |
|
|
1157 |
"Done.\n", |
|
|
1158 |
"predicting...\n", |
|
|
1159 |
"---------------\n", |
|
|
1160 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1161 |
"-------------\n", |
|
|
1162 |
"repurposing...\n", |
|
|
1163 |
"in total: 82 drug-target pairs\n", |
|
|
1164 |
"encoding drug...\n", |
|
|
1165 |
"unique drugs: 81\n", |
|
|
1166 |
"drug encoding finished...\n", |
|
|
1167 |
"encoding protein...\n", |
|
|
1168 |
"unique target sequence: 1\n", |
|
|
1169 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1170 |
"protein encoding finished...\n", |
|
|
1171 |
"Done.\n", |
|
|
1172 |
"predicting...\n", |
|
|
1173 |
"---------------\n", |
|
|
1174 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1175 |
"-------------\n", |
|
|
1176 |
"repurposing...\n", |
|
|
1177 |
"in total: 82 drug-target pairs\n", |
|
|
1178 |
"encoding drug...\n", |
|
|
1179 |
"unique drugs: 81\n", |
|
|
1180 |
"drug encoding finished...\n", |
|
|
1181 |
"encoding protein...\n", |
|
|
1182 |
"unique target sequence: 1\n", |
|
|
1183 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1184 |
"protein encoding finished...\n", |
|
|
1185 |
"Done.\n", |
|
|
1186 |
"predicting...\n", |
|
|
1187 |
"---------------\n", |
|
|
1188 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1189 |
"-------------\n", |
|
|
1190 |
"models prediction finished...\n", |
|
|
1191 |
"aggregating results...\n", |
|
|
1192 |
"---------------\n", |
|
|
1193 |
"Drug Repurposing Result for SARS_CoV2_Helicase\n", |
|
|
1194 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
1195 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1196 |
"+------+----------------------+--------------------+---------------+\n", |
|
|
1197 |
"| 1 | Daclatasvir | SARS_CoV2_Helicase | 214.51 |\n", |
|
|
1198 |
"| 2 | Simeprevir | SARS_CoV2_Helicase | 234.54 |\n", |
|
|
1199 |
"| 3 | Sofosbuvir | SARS_CoV2_Helicase | 236.22 |\n", |
|
|
1200 |
"| 4 | Vicriviroc | SARS_CoV2_Helicase | 314.97 |\n", |
|
|
1201 |
"| 5 | Etravirine | SARS_CoV2_Helicase | 391.80 |\n", |
|
|
1202 |
"| 6 | Atazanavir | SARS_CoV2_Helicase | 425.85 |\n", |
|
|
1203 |
"| 7 | Letermovir | SARS_CoV2_Helicase | 462.70 |\n", |
|
|
1204 |
"| 8 | Rilpivirine | SARS_CoV2_Helicase | 474.99 |\n", |
|
|
1205 |
"| 9 | Lopinavir | SARS_CoV2_Helicase | 479.67 |\n", |
|
|
1206 |
"| 10 | Darunavir | SARS_CoV2_Helicase | 480.55 |\n", |
|
|
1207 |
"| 11 | Maraviroc | SARS_CoV2_Helicase | 484.73 |\n", |
|
|
1208 |
"| 12 | Fosamprenavir | SARS_CoV2_Helicase | 493.81 |\n", |
|
|
1209 |
"| 13 | Peramivir | SARS_CoV2_Helicase | 516.79 |\n", |
|
|
1210 |
"| 14 | Grazoprevir | SARS_CoV2_Helicase | 525.42 |\n", |
|
|
1211 |
"| 15 | Amantadine | SARS_CoV2_Helicase | 538.67 |\n", |
|
|
1212 |
"| 16 | Telaprevir | SARS_CoV2_Helicase | 597.52 |\n", |
|
|
1213 |
"| 17 | Amprenavir | SARS_CoV2_Helicase | 615.40 |\n", |
|
|
1214 |
"| 18 | Tenofovir_disoproxil | SARS_CoV2_Helicase | 620.33 |\n", |
|
|
1215 |
"| 19 | Nelfinavir | SARS_CoV2_Helicase | 673.65 |\n", |
|
|
1216 |
"| 20 | Efavirenz | SARS_CoV2_Helicase | 689.13 |\n", |
|
|
1217 |
"| 21 | Tenofovir | SARS_CoV2_Helicase | 694.82 |\n", |
|
|
1218 |
"| 22 | Descovy | SARS_CoV2_Helicase | 694.82 |\n", |
|
|
1219 |
"| 23 | Ritonavir | SARS_CoV2_Helicase | 715.91 |\n", |
|
|
1220 |
"| 24 | Elvitegravir | SARS_CoV2_Helicase | 729.73 |\n", |
|
|
1221 |
"| 25 | Abacavir | SARS_CoV2_Helicase | 757.06 |\n", |
|
|
1222 |
"| 26 | Doravirine | SARS_CoV2_Helicase | 800.64 |\n", |
|
|
1223 |
"| 27 | Boceprevir | SARS_CoV2_Helicase | 881.09 |\n", |
|
|
1224 |
"| 28 | Pleconaril | SARS_CoV2_Helicase | 899.21 |\n", |
|
|
1225 |
"| 29 | Delavirdine | SARS_CoV2_Helicase | 924.90 |\n", |
|
|
1226 |
"| 30 | Raltegravir | SARS_CoV2_Helicase | 934.50 |\n", |
|
|
1227 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1228 |
"\n" |
|
|
1229 |
] |
|
|
1230 |
} |
|
|
1231 |
], |
|
|
1232 |
"source": [ |
|
|
1233 |
"oneliner.repurpose(target = target, \n", |
|
|
1234 |
" target_name = target_name, \n", |
|
|
1235 |
" X_repurpose = X_repurpose,\n", |
|
|
1236 |
" drug_names = drug_names,\n", |
|
|
1237 |
" save_dir = './save_folder',\n", |
|
|
1238 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1239 |
" agg = 'agg_mean_max')" |
|
|
1240 |
] |
|
|
1241 |
}, |
|
|
1242 |
{ |
|
|
1243 |
"cell_type": "code", |
|
|
1244 |
"execution_count": 13, |
|
|
1245 |
"metadata": { |
|
|
1246 |
"scrolled": false |
|
|
1247 |
}, |
|
|
1248 |
"outputs": [ |
|
|
1249 |
{ |
|
|
1250 |
"name": "stdout", |
|
|
1251 |
"output_type": "stream", |
|
|
1252 |
"text": [ |
|
|
1253 |
"Loading customized repurposing dataset...\n", |
|
|
1254 |
"Checking if pretrained directory is valid...\n", |
|
|
1255 |
"Beginning to load the pretrained models...\n", |
|
|
1256 |
"Using pretrained model and making predictions...\n", |
|
|
1257 |
"repurposing...\n", |
|
|
1258 |
"in total: 82 drug-target pairs\n", |
|
|
1259 |
"encoding drug...\n", |
|
|
1260 |
"unique drugs: 81\n", |
|
|
1261 |
"drug encoding finished...\n", |
|
|
1262 |
"encoding protein...\n", |
|
|
1263 |
"unique target sequence: 1\n", |
|
|
1264 |
"protein encoding finished...\n", |
|
|
1265 |
"Done.\n", |
|
|
1266 |
"predicting...\n", |
|
|
1267 |
"---------------\n", |
|
|
1268 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1269 |
"-------------\n", |
|
|
1270 |
"repurposing...\n", |
|
|
1271 |
"in total: 82 drug-target pairs\n", |
|
|
1272 |
"encoding drug...\n", |
|
|
1273 |
"unique drugs: 81\n", |
|
|
1274 |
"drug encoding finished...\n", |
|
|
1275 |
"encoding protein...\n", |
|
|
1276 |
"unique target sequence: 1\n", |
|
|
1277 |
"protein encoding finished...\n", |
|
|
1278 |
"Done.\n", |
|
|
1279 |
"predicting...\n", |
|
|
1280 |
"---------------\n", |
|
|
1281 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1282 |
"-------------\n", |
|
|
1283 |
"repurposing...\n", |
|
|
1284 |
"in total: 82 drug-target pairs\n", |
|
|
1285 |
"encoding drug...\n", |
|
|
1286 |
"unique drugs: 81\n", |
|
|
1287 |
"drug encoding finished...\n", |
|
|
1288 |
"encoding protein...\n", |
|
|
1289 |
"unique target sequence: 1\n", |
|
|
1290 |
"protein encoding finished...\n", |
|
|
1291 |
"Done.\n", |
|
|
1292 |
"predicting...\n", |
|
|
1293 |
"---------------\n", |
|
|
1294 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1295 |
"-------------\n", |
|
|
1296 |
"repurposing...\n", |
|
|
1297 |
"in total: 82 drug-target pairs\n", |
|
|
1298 |
"encoding drug...\n", |
|
|
1299 |
"unique drugs: 81\n", |
|
|
1300 |
"drug encoding finished...\n", |
|
|
1301 |
"encoding protein...\n", |
|
|
1302 |
"unique target sequence: 1\n", |
|
|
1303 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1304 |
"protein encoding finished...\n", |
|
|
1305 |
"Done.\n", |
|
|
1306 |
"predicting...\n", |
|
|
1307 |
"---------------\n", |
|
|
1308 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1309 |
"-------------\n", |
|
|
1310 |
"repurposing...\n", |
|
|
1311 |
"in total: 82 drug-target pairs\n", |
|
|
1312 |
"encoding drug...\n", |
|
|
1313 |
"unique drugs: 81\n", |
|
|
1314 |
"drug encoding finished...\n", |
|
|
1315 |
"encoding protein...\n", |
|
|
1316 |
"unique target sequence: 1\n", |
|
|
1317 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1318 |
"protein encoding finished...\n", |
|
|
1319 |
"Done.\n", |
|
|
1320 |
"predicting...\n", |
|
|
1321 |
"---------------\n", |
|
|
1322 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1323 |
"-------------\n", |
|
|
1324 |
"models prediction finished...\n", |
|
|
1325 |
"aggregating results...\n", |
|
|
1326 |
"---------------\n", |
|
|
1327 |
"Drug Repurposing Result for SARS_CoV2_3to5_exonuclease\n", |
|
|
1328 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1329 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1330 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1331 |
"| 1 | Sofosbuvir | SARS_CoV2_3to5_exonuclease | 331.42 |\n", |
|
|
1332 |
"| 2 | Simeprevir | SARS_CoV2_3to5_exonuclease | 357.64 |\n", |
|
|
1333 |
"| 3 | Daclatasvir | SARS_CoV2_3to5_exonuclease | 391.05 |\n", |
|
|
1334 |
"| 4 | Vicriviroc | SARS_CoV2_3to5_exonuclease | 511.83 |\n", |
|
|
1335 |
"| 5 | Atazanavir | SARS_CoV2_3to5_exonuclease | 669.99 |\n", |
|
|
1336 |
"| 6 | Etravirine | SARS_CoV2_3to5_exonuclease | 717.46 |\n", |
|
|
1337 |
"| 7 | Tenofovir_disoproxil | SARS_CoV2_3to5_exonuclease | 733.86 |\n", |
|
|
1338 |
"| 8 | Efavirenz | SARS_CoV2_3to5_exonuclease | 767.65 |\n", |
|
|
1339 |
"| 9 | Grazoprevir | SARS_CoV2_3to5_exonuclease | 820.81 |\n", |
|
|
1340 |
"| 10 | Rilpivirine | SARS_CoV2_3to5_exonuclease | 859.15 |\n", |
|
|
1341 |
"| 11 | Letermovir | SARS_CoV2_3to5_exonuclease | 865.38 |\n", |
|
|
1342 |
"| 12 | Peramivir | SARS_CoV2_3to5_exonuclease | 877.47 |\n", |
|
|
1343 |
"| 13 | Lopinavir | SARS_CoV2_3to5_exonuclease | 925.16 |\n", |
|
|
1344 |
"| 14 | Darunavir | SARS_CoV2_3to5_exonuclease | 930.19 |\n", |
|
|
1345 |
"| 15 | Maraviroc | SARS_CoV2_3to5_exonuclease | 933.72 |\n", |
|
|
1346 |
"| 16 | Fosamprenavir | SARS_CoV2_3to5_exonuclease | 952.93 |\n", |
|
|
1347 |
"| 17 | Elvitegravir | SARS_CoV2_3to5_exonuclease | 971.27 |\n", |
|
|
1348 |
"| 18 | Amantadine | SARS_CoV2_3to5_exonuclease | 977.99 |\n", |
|
|
1349 |
"| 19 | Telaprevir | SARS_CoV2_3to5_exonuclease | 1103.15 |\n", |
|
|
1350 |
"| 20 | Tenofovir | SARS_CoV2_3to5_exonuclease | 1111.73 |\n", |
|
|
1351 |
"| 21 | Descovy | SARS_CoV2_3to5_exonuclease | 1111.73 |\n", |
|
|
1352 |
"| 22 | Boceprevir | SARS_CoV2_3to5_exonuclease | 1137.67 |\n", |
|
|
1353 |
"| 23 | Nelfinavir | SARS_CoV2_3to5_exonuclease | 1189.33 |\n", |
|
|
1354 |
"| 24 | Amprenavir | SARS_CoV2_3to5_exonuclease | 1190.66 |\n", |
|
|
1355 |
"| 25 | Doravirine | SARS_CoV2_3to5_exonuclease | 1240.21 |\n", |
|
|
1356 |
"| 26 | Ritonavir | SARS_CoV2_3to5_exonuclease | 1310.21 |\n", |
|
|
1357 |
"| 27 | Abacavir | SARS_CoV2_3to5_exonuclease | 1424.62 |\n", |
|
|
1358 |
"| 28 | Raltegravir | SARS_CoV2_3to5_exonuclease | 1515.33 |\n", |
|
|
1359 |
"| 29 | Dolutegravir | SARS_CoV2_3to5_exonuclease | 1593.56 |\n", |
|
|
1360 |
"| 30 | Pleconaril | SARS_CoV2_3to5_exonuclease | 1645.66 |\n", |
|
|
1361 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1362 |
"\n" |
|
|
1363 |
] |
|
|
1364 |
} |
|
|
1365 |
], |
|
|
1366 |
"source": [ |
|
|
1367 |
"target, target_name = dataset.load_SARS_CoV2_3to5_exonuclease()\n", |
|
|
1368 |
"oneliner.repurpose(target = target, \n", |
|
|
1369 |
" target_name = target_name, \n", |
|
|
1370 |
" X_repurpose = X_repurpose,\n", |
|
|
1371 |
" drug_names = drug_names,\n", |
|
|
1372 |
" save_dir = './save_folder',\n", |
|
|
1373 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1374 |
" agg = 'mean')" |
|
|
1375 |
] |
|
|
1376 |
}, |
|
|
1377 |
{ |
|
|
1378 |
"cell_type": "code", |
|
|
1379 |
"execution_count": 14, |
|
|
1380 |
"metadata": { |
|
|
1381 |
"scrolled": false |
|
|
1382 |
}, |
|
|
1383 |
"outputs": [ |
|
|
1384 |
{ |
|
|
1385 |
"name": "stdout", |
|
|
1386 |
"output_type": "stream", |
|
|
1387 |
"text": [ |
|
|
1388 |
"Loading customized repurposing dataset...\n", |
|
|
1389 |
"Checking if pretrained directory is valid...\n", |
|
|
1390 |
"Beginning to load the pretrained models...\n", |
|
|
1391 |
"Using pretrained model and making predictions...\n", |
|
|
1392 |
"repurposing...\n", |
|
|
1393 |
"in total: 82 drug-target pairs\n", |
|
|
1394 |
"encoding drug...\n", |
|
|
1395 |
"unique drugs: 81\n", |
|
|
1396 |
"drug encoding finished...\n", |
|
|
1397 |
"encoding protein...\n", |
|
|
1398 |
"unique target sequence: 1\n", |
|
|
1399 |
"protein encoding finished...\n", |
|
|
1400 |
"Done.\n", |
|
|
1401 |
"predicting...\n", |
|
|
1402 |
"---------------\n", |
|
|
1403 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1404 |
"-------------\n", |
|
|
1405 |
"repurposing...\n", |
|
|
1406 |
"in total: 82 drug-target pairs\n", |
|
|
1407 |
"encoding drug...\n", |
|
|
1408 |
"unique drugs: 81\n", |
|
|
1409 |
"drug encoding finished...\n", |
|
|
1410 |
"encoding protein...\n", |
|
|
1411 |
"unique target sequence: 1\n", |
|
|
1412 |
"protein encoding finished...\n", |
|
|
1413 |
"Done.\n", |
|
|
1414 |
"predicting...\n", |
|
|
1415 |
"---------------\n", |
|
|
1416 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1417 |
"-------------\n", |
|
|
1418 |
"repurposing...\n", |
|
|
1419 |
"in total: 82 drug-target pairs\n", |
|
|
1420 |
"encoding drug...\n", |
|
|
1421 |
"unique drugs: 81\n", |
|
|
1422 |
"drug encoding finished...\n", |
|
|
1423 |
"encoding protein...\n", |
|
|
1424 |
"unique target sequence: 1\n", |
|
|
1425 |
"protein encoding finished...\n", |
|
|
1426 |
"Done.\n", |
|
|
1427 |
"predicting...\n", |
|
|
1428 |
"---------------\n", |
|
|
1429 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1430 |
"-------------\n", |
|
|
1431 |
"repurposing...\n", |
|
|
1432 |
"in total: 82 drug-target pairs\n", |
|
|
1433 |
"encoding drug...\n", |
|
|
1434 |
"unique drugs: 81\n", |
|
|
1435 |
"drug encoding finished...\n", |
|
|
1436 |
"encoding protein...\n", |
|
|
1437 |
"unique target sequence: 1\n", |
|
|
1438 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1439 |
"protein encoding finished...\n", |
|
|
1440 |
"Done.\n", |
|
|
1441 |
"predicting...\n", |
|
|
1442 |
"---------------\n", |
|
|
1443 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1444 |
"-------------\n", |
|
|
1445 |
"repurposing...\n", |
|
|
1446 |
"in total: 82 drug-target pairs\n", |
|
|
1447 |
"encoding drug...\n", |
|
|
1448 |
"unique drugs: 81\n", |
|
|
1449 |
"drug encoding finished...\n", |
|
|
1450 |
"encoding protein...\n", |
|
|
1451 |
"unique target sequence: 1\n", |
|
|
1452 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1453 |
"protein encoding finished...\n", |
|
|
1454 |
"Done.\n", |
|
|
1455 |
"predicting...\n", |
|
|
1456 |
"---------------\n", |
|
|
1457 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1458 |
"-------------\n", |
|
|
1459 |
"models prediction finished...\n", |
|
|
1460 |
"aggregating results...\n", |
|
|
1461 |
"---------------\n", |
|
|
1462 |
"Drug Repurposing Result for SARS_CoV2_3to5_exonuclease\n", |
|
|
1463 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1464 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1465 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1466 |
"| 1 | Lopinavir | SARS_CoV2_3to5_exonuclease | 0.21 |\n", |
|
|
1467 |
"| 2 | Darunavir | SARS_CoV2_3to5_exonuclease | 0.26 |\n", |
|
|
1468 |
"| 3 | Amprenavir | SARS_CoV2_3to5_exonuclease | 0.77 |\n", |
|
|
1469 |
"| 4 | Tipranavir | SARS_CoV2_3to5_exonuclease | 1.98 |\n", |
|
|
1470 |
"| 5 | Baloxavir | SARS_CoV2_3to5_exonuclease | 2.76 |\n", |
|
|
1471 |
"| 6 | Boceprevir | SARS_CoV2_3to5_exonuclease | 3.35 |\n", |
|
|
1472 |
"| 7 | Glecaprevir | SARS_CoV2_3to5_exonuclease | 3.63 |\n", |
|
|
1473 |
"| 8 | Oseltamivir | SARS_CoV2_3to5_exonuclease | 4.12 |\n", |
|
|
1474 |
"| 9 | Telaprevir | SARS_CoV2_3to5_exonuclease | 4.44 |\n", |
|
|
1475 |
"| 10 | Nelfinavir | SARS_CoV2_3to5_exonuclease | 5.15 |\n", |
|
|
1476 |
"| 11 | Daclatasvir | SARS_CoV2_3to5_exonuclease | 5.31 |\n", |
|
|
1477 |
"| 12 | Vicriviroc | SARS_CoV2_3to5_exonuclease | 5.57 |\n", |
|
|
1478 |
"| 13 | Fosamprenavir | SARS_CoV2_3to5_exonuclease | 5.64 |\n", |
|
|
1479 |
"| 14 | Maraviroc | SARS_CoV2_3to5_exonuclease | 7.09 |\n", |
|
|
1480 |
"| 15 | Amantadine | SARS_CoV2_3to5_exonuclease | 8.80 |\n", |
|
|
1481 |
"| 16 | Etravirine | SARS_CoV2_3to5_exonuclease | 10.17 |\n", |
|
|
1482 |
"| 17 | Foscarnet | SARS_CoV2_3to5_exonuclease | 11.20 |\n", |
|
|
1483 |
"| 18 | Entecavir | SARS_CoV2_3to5_exonuclease | 13.15 |\n", |
|
|
1484 |
"| 19 | Rilpivirine | SARS_CoV2_3to5_exonuclease | 14.35 |\n", |
|
|
1485 |
"| 20 | Atazanavir | SARS_CoV2_3to5_exonuclease | 14.42 |\n", |
|
|
1486 |
"| 21 | Simeprevir | SARS_CoV2_3to5_exonuclease | 14.67 |\n", |
|
|
1487 |
"| 22 | Sofosbuvir | SARS_CoV2_3to5_exonuclease | 15.18 |\n", |
|
|
1488 |
"| 23 | Pleconaril | SARS_CoV2_3to5_exonuclease | 15.20 |\n", |
|
|
1489 |
"| 24 | Abacavir | SARS_CoV2_3to5_exonuclease | 16.05 |\n", |
|
|
1490 |
"| 25 | Arbidol | SARS_CoV2_3to5_exonuclease | 18.36 |\n", |
|
|
1491 |
"| 26 | Saquinavir | SARS_CoV2_3to5_exonuclease | 19.92 |\n", |
|
|
1492 |
"| 27 | Tenofovir | SARS_CoV2_3to5_exonuclease | 20.45 |\n", |
|
|
1493 |
"| 28 | Descovy | SARS_CoV2_3to5_exonuclease | 20.45 |\n", |
|
|
1494 |
"| 29 | Ritonavir | SARS_CoV2_3to5_exonuclease | 26.42 |\n", |
|
|
1495 |
"| 30 | Letermovir | SARS_CoV2_3to5_exonuclease | 26.89 |\n", |
|
|
1496 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1497 |
"\n" |
|
|
1498 |
] |
|
|
1499 |
} |
|
|
1500 |
], |
|
|
1501 |
"source": [ |
|
|
1502 |
"oneliner.repurpose(target = target, \n", |
|
|
1503 |
" target_name = target_name, \n", |
|
|
1504 |
" X_repurpose = X_repurpose,\n", |
|
|
1505 |
" drug_names = drug_names,\n", |
|
|
1506 |
" save_dir = './save_folder',\n", |
|
|
1507 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1508 |
" agg = 'max_effect')" |
|
|
1509 |
] |
|
|
1510 |
}, |
|
|
1511 |
{ |
|
|
1512 |
"cell_type": "code", |
|
|
1513 |
"execution_count": 15, |
|
|
1514 |
"metadata": {}, |
|
|
1515 |
"outputs": [ |
|
|
1516 |
{ |
|
|
1517 |
"name": "stdout", |
|
|
1518 |
"output_type": "stream", |
|
|
1519 |
"text": [ |
|
|
1520 |
"Loading customized repurposing dataset...\n", |
|
|
1521 |
"Checking if pretrained directory is valid...\n", |
|
|
1522 |
"Beginning to load the pretrained models...\n", |
|
|
1523 |
"Using pretrained model and making predictions...\n", |
|
|
1524 |
"repurposing...\n", |
|
|
1525 |
"in total: 82 drug-target pairs\n", |
|
|
1526 |
"encoding drug...\n", |
|
|
1527 |
"unique drugs: 81\n", |
|
|
1528 |
"drug encoding finished...\n", |
|
|
1529 |
"encoding protein...\n", |
|
|
1530 |
"unique target sequence: 1\n", |
|
|
1531 |
"protein encoding finished...\n", |
|
|
1532 |
"Done.\n", |
|
|
1533 |
"predicting...\n", |
|
|
1534 |
"---------------\n", |
|
|
1535 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1536 |
"-------------\n", |
|
|
1537 |
"repurposing...\n", |
|
|
1538 |
"in total: 82 drug-target pairs\n", |
|
|
1539 |
"encoding drug...\n", |
|
|
1540 |
"unique drugs: 81\n", |
|
|
1541 |
"drug encoding finished...\n", |
|
|
1542 |
"encoding protein...\n", |
|
|
1543 |
"unique target sequence: 1\n", |
|
|
1544 |
"protein encoding finished...\n", |
|
|
1545 |
"Done.\n", |
|
|
1546 |
"predicting...\n", |
|
|
1547 |
"---------------\n", |
|
|
1548 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1549 |
"-------------\n", |
|
|
1550 |
"repurposing...\n", |
|
|
1551 |
"in total: 82 drug-target pairs\n", |
|
|
1552 |
"encoding drug...\n", |
|
|
1553 |
"unique drugs: 81\n", |
|
|
1554 |
"drug encoding finished...\n", |
|
|
1555 |
"encoding protein...\n", |
|
|
1556 |
"unique target sequence: 1\n", |
|
|
1557 |
"protein encoding finished...\n", |
|
|
1558 |
"Done.\n", |
|
|
1559 |
"predicting...\n", |
|
|
1560 |
"---------------\n", |
|
|
1561 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1562 |
"-------------\n", |
|
|
1563 |
"repurposing...\n", |
|
|
1564 |
"in total: 82 drug-target pairs\n", |
|
|
1565 |
"encoding drug...\n", |
|
|
1566 |
"unique drugs: 81\n", |
|
|
1567 |
"drug encoding finished...\n", |
|
|
1568 |
"encoding protein...\n", |
|
|
1569 |
"unique target sequence: 1\n", |
|
|
1570 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1571 |
"protein encoding finished...\n", |
|
|
1572 |
"Done.\n", |
|
|
1573 |
"predicting...\n", |
|
|
1574 |
"---------------\n", |
|
|
1575 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1576 |
"-------------\n", |
|
|
1577 |
"repurposing...\n", |
|
|
1578 |
"in total: 82 drug-target pairs\n", |
|
|
1579 |
"encoding drug...\n", |
|
|
1580 |
"unique drugs: 81\n", |
|
|
1581 |
"drug encoding finished...\n", |
|
|
1582 |
"encoding protein...\n", |
|
|
1583 |
"unique target sequence: 1\n", |
|
|
1584 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1585 |
"protein encoding finished...\n", |
|
|
1586 |
"Done.\n", |
|
|
1587 |
"predicting...\n", |
|
|
1588 |
"---------------\n", |
|
|
1589 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1590 |
"-------------\n", |
|
|
1591 |
"models prediction finished...\n", |
|
|
1592 |
"aggregating results...\n", |
|
|
1593 |
"---------------\n", |
|
|
1594 |
"Drug Repurposing Result for SARS_CoV2_3to5_exonuclease\n", |
|
|
1595 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1596 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1597 |
"+------+----------------------+----------------------------+---------------+\n", |
|
|
1598 |
"| 1 | Sofosbuvir | SARS_CoV2_3to5_exonuclease | 173.30 |\n", |
|
|
1599 |
"| 2 | Simeprevir | SARS_CoV2_3to5_exonuclease | 186.16 |\n", |
|
|
1600 |
"| 3 | Daclatasvir | SARS_CoV2_3to5_exonuclease | 198.18 |\n", |
|
|
1601 |
"| 4 | Vicriviroc | SARS_CoV2_3to5_exonuclease | 258.70 |\n", |
|
|
1602 |
"| 5 | Atazanavir | SARS_CoV2_3to5_exonuclease | 342.21 |\n", |
|
|
1603 |
"| 6 | Etravirine | SARS_CoV2_3to5_exonuclease | 363.81 |\n", |
|
|
1604 |
"| 7 | Tenofovir_disoproxil | SARS_CoV2_3to5_exonuclease | 430.66 |\n", |
|
|
1605 |
"| 8 | Rilpivirine | SARS_CoV2_3to5_exonuclease | 436.75 |\n", |
|
|
1606 |
"| 9 | Letermovir | SARS_CoV2_3to5_exonuclease | 446.13 |\n", |
|
|
1607 |
"| 10 | Peramivir | SARS_CoV2_3to5_exonuclease | 456.39 |\n", |
|
|
1608 |
"| 11 | Lopinavir | SARS_CoV2_3to5_exonuclease | 462.68 |\n", |
|
|
1609 |
"| 12 | Grazoprevir | SARS_CoV2_3to5_exonuclease | 463.52 |\n", |
|
|
1610 |
"| 13 | Darunavir | SARS_CoV2_3to5_exonuclease | 465.23 |\n", |
|
|
1611 |
"| 14 | Maraviroc | SARS_CoV2_3to5_exonuclease | 470.40 |\n", |
|
|
1612 |
"| 15 | Fosamprenavir | SARS_CoV2_3to5_exonuclease | 479.29 |\n", |
|
|
1613 |
"| 16 | Amantadine | SARS_CoV2_3to5_exonuclease | 493.40 |\n", |
|
|
1614 |
"| 17 | Efavirenz | SARS_CoV2_3to5_exonuclease | 511.76 |\n", |
|
|
1615 |
"| 18 | Elvitegravir | SARS_CoV2_3to5_exonuclease | 546.67 |\n", |
|
|
1616 |
"| 19 | Telaprevir | SARS_CoV2_3to5_exonuclease | 553.80 |\n", |
|
|
1617 |
"| 20 | Tenofovir | SARS_CoV2_3to5_exonuclease | 566.09 |\n", |
|
|
1618 |
"| 21 | Descovy | SARS_CoV2_3to5_exonuclease | 566.09 |\n", |
|
|
1619 |
"| 22 | Boceprevir | SARS_CoV2_3to5_exonuclease | 570.51 |\n", |
|
|
1620 |
"| 23 | Amprenavir | SARS_CoV2_3to5_exonuclease | 595.71 |\n", |
|
|
1621 |
"| 24 | Nelfinavir | SARS_CoV2_3to5_exonuclease | 597.24 |\n", |
|
|
1622 |
"| 25 | Doravirine | SARS_CoV2_3to5_exonuclease | 650.27 |\n", |
|
|
1623 |
"| 26 | Ritonavir | SARS_CoV2_3to5_exonuclease | 668.31 |\n", |
|
|
1624 |
"| 27 | Abacavir | SARS_CoV2_3to5_exonuclease | 720.33 |\n", |
|
|
1625 |
"| 28 | Raltegravir | SARS_CoV2_3to5_exonuclease | 771.92 |\n", |
|
|
1626 |
"| 29 | Pleconaril | SARS_CoV2_3to5_exonuclease | 830.43 |\n", |
|
|
1627 |
"| 30 | Delavirdine | SARS_CoV2_3to5_exonuclease | 864.18 |\n", |
|
|
1628 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1629 |
"\n" |
|
|
1630 |
] |
|
|
1631 |
} |
|
|
1632 |
], |
|
|
1633 |
"source": [ |
|
|
1634 |
"oneliner.repurpose(target = target, \n", |
|
|
1635 |
" target_name = target_name, \n", |
|
|
1636 |
" X_repurpose = X_repurpose,\n", |
|
|
1637 |
" drug_names = drug_names,\n", |
|
|
1638 |
" save_dir = './save_folder',\n", |
|
|
1639 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1640 |
" agg = 'agg_mean_max')" |
|
|
1641 |
] |
|
|
1642 |
}, |
|
|
1643 |
{ |
|
|
1644 |
"cell_type": "code", |
|
|
1645 |
"execution_count": 16, |
|
|
1646 |
"metadata": { |
|
|
1647 |
"scrolled": false |
|
|
1648 |
}, |
|
|
1649 |
"outputs": [ |
|
|
1650 |
{ |
|
|
1651 |
"name": "stdout", |
|
|
1652 |
"output_type": "stream", |
|
|
1653 |
"text": [ |
|
|
1654 |
"Loading customized repurposing dataset...\n", |
|
|
1655 |
"Checking if pretrained directory is valid...\n", |
|
|
1656 |
"Beginning to load the pretrained models...\n", |
|
|
1657 |
"Using pretrained model and making predictions...\n", |
|
|
1658 |
"repurposing...\n", |
|
|
1659 |
"in total: 82 drug-target pairs\n", |
|
|
1660 |
"encoding drug...\n", |
|
|
1661 |
"unique drugs: 81\n", |
|
|
1662 |
"drug encoding finished...\n", |
|
|
1663 |
"encoding protein...\n", |
|
|
1664 |
"unique target sequence: 1\n", |
|
|
1665 |
"protein encoding finished...\n", |
|
|
1666 |
"Done.\n", |
|
|
1667 |
"predicting...\n", |
|
|
1668 |
"---------------\n", |
|
|
1669 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1670 |
"-------------\n", |
|
|
1671 |
"repurposing...\n", |
|
|
1672 |
"in total: 82 drug-target pairs\n", |
|
|
1673 |
"encoding drug...\n", |
|
|
1674 |
"unique drugs: 81\n", |
|
|
1675 |
"drug encoding finished...\n", |
|
|
1676 |
"encoding protein...\n", |
|
|
1677 |
"unique target sequence: 1\n", |
|
|
1678 |
"protein encoding finished...\n", |
|
|
1679 |
"Done.\n", |
|
|
1680 |
"predicting...\n", |
|
|
1681 |
"---------------\n", |
|
|
1682 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1683 |
"-------------\n", |
|
|
1684 |
"repurposing...\n", |
|
|
1685 |
"in total: 82 drug-target pairs\n", |
|
|
1686 |
"encoding drug...\n", |
|
|
1687 |
"unique drugs: 81\n", |
|
|
1688 |
"drug encoding finished...\n", |
|
|
1689 |
"encoding protein...\n", |
|
|
1690 |
"unique target sequence: 1\n", |
|
|
1691 |
"protein encoding finished...\n", |
|
|
1692 |
"Done.\n", |
|
|
1693 |
"predicting...\n", |
|
|
1694 |
"---------------\n", |
|
|
1695 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1696 |
"-------------\n", |
|
|
1697 |
"repurposing...\n", |
|
|
1698 |
"in total: 82 drug-target pairs\n", |
|
|
1699 |
"encoding drug...\n", |
|
|
1700 |
"unique drugs: 81\n", |
|
|
1701 |
"drug encoding finished...\n", |
|
|
1702 |
"encoding protein...\n", |
|
|
1703 |
"unique target sequence: 1\n", |
|
|
1704 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1705 |
"protein encoding finished...\n", |
|
|
1706 |
"Done.\n", |
|
|
1707 |
"predicting...\n", |
|
|
1708 |
"---------------\n", |
|
|
1709 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1710 |
"-------------\n", |
|
|
1711 |
"repurposing...\n", |
|
|
1712 |
"in total: 82 drug-target pairs\n", |
|
|
1713 |
"encoding drug...\n", |
|
|
1714 |
"unique drugs: 81\n", |
|
|
1715 |
"drug encoding finished...\n", |
|
|
1716 |
"encoding protein...\n", |
|
|
1717 |
"unique target sequence: 1\n", |
|
|
1718 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1719 |
"protein encoding finished...\n", |
|
|
1720 |
"Done.\n", |
|
|
1721 |
"predicting...\n", |
|
|
1722 |
"---------------\n", |
|
|
1723 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1724 |
"-------------\n", |
|
|
1725 |
"models prediction finished...\n", |
|
|
1726 |
"aggregating results...\n", |
|
|
1727 |
"---------------\n", |
|
|
1728 |
"Drug Repurposing Result for SARS_CoV2_endoRNAse\n", |
|
|
1729 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1730 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1731 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1732 |
"| 1 | Daclatasvir | SARS_CoV2_endoRNAse | 425.21 |\n", |
|
|
1733 |
"| 2 | Simeprevir | SARS_CoV2_endoRNAse | 464.11 |\n", |
|
|
1734 |
"| 3 | Sofosbuvir | SARS_CoV2_endoRNAse | 561.39 |\n", |
|
|
1735 |
"| 4 | Vicriviroc | SARS_CoV2_endoRNAse | 794.73 |\n", |
|
|
1736 |
"| 5 | Etravirine | SARS_CoV2_endoRNAse | 795.08 |\n", |
|
|
1737 |
"| 6 | Atazanavir | SARS_CoV2_endoRNAse | 811.34 |\n", |
|
|
1738 |
"| 7 | Rilpivirine | SARS_CoV2_endoRNAse | 869.46 |\n", |
|
|
1739 |
"| 8 | Letermovir | SARS_CoV2_endoRNAse | 879.67 |\n", |
|
|
1740 |
"| 9 | Maraviroc | SARS_CoV2_endoRNAse | 915.73 |\n", |
|
|
1741 |
"| 10 | Darunavir | SARS_CoV2_endoRNAse | 919.07 |\n", |
|
|
1742 |
"| 11 | Lopinavir | SARS_CoV2_endoRNAse | 919.69 |\n", |
|
|
1743 |
"| 12 | Peramivir | SARS_CoV2_endoRNAse | 939.63 |\n", |
|
|
1744 |
"| 13 | Fosamprenavir | SARS_CoV2_endoRNAse | 941.33 |\n", |
|
|
1745 |
"| 14 | Grazoprevir | SARS_CoV2_endoRNAse | 1118.11 |\n", |
|
|
1746 |
"| 15 | Telaprevir | SARS_CoV2_endoRNAse | 1142.64 |\n", |
|
|
1747 |
"| 16 | Amprenavir | SARS_CoV2_endoRNAse | 1176.82 |\n", |
|
|
1748 |
"| 17 | Amantadine | SARS_CoV2_endoRNAse | 1190.22 |\n", |
|
|
1749 |
"| 18 | Nelfinavir | SARS_CoV2_endoRNAse | 1289.00 |\n", |
|
|
1750 |
"| 19 | Elvitegravir | SARS_CoV2_endoRNAse | 1517.18 |\n", |
|
|
1751 |
"| 20 | Doravirine | SARS_CoV2_endoRNAse | 1574.81 |\n", |
|
|
1752 |
"| 21 | Boceprevir | SARS_CoV2_endoRNAse | 1595.57 |\n", |
|
|
1753 |
"| 22 | Raltegravir | SARS_CoV2_endoRNAse | 1661.85 |\n", |
|
|
1754 |
"| 23 | Tenofovir_disoproxil | SARS_CoV2_endoRNAse | 1707.86 |\n", |
|
|
1755 |
"| 24 | Delavirdine | SARS_CoV2_endoRNAse | 1775.90 |\n", |
|
|
1756 |
"| 25 | Abacavir | SARS_CoV2_endoRNAse | 1809.39 |\n", |
|
|
1757 |
"| 26 | Saquinavir | SARS_CoV2_endoRNAse | 1812.37 |\n", |
|
|
1758 |
"| 27 | Dolutegravir | SARS_CoV2_endoRNAse | 1855.91 |\n", |
|
|
1759 |
"| 28 | Ritonavir | SARS_CoV2_endoRNAse | 1902.92 |\n", |
|
|
1760 |
"| 29 | Glecaprevir | SARS_CoV2_endoRNAse | 2152.12 |\n", |
|
|
1761 |
"| 30 | Pleconaril | SARS_CoV2_endoRNAse | 2189.36 |\n", |
|
|
1762 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1763 |
"\n" |
|
|
1764 |
] |
|
|
1765 |
} |
|
|
1766 |
], |
|
|
1767 |
"source": [ |
|
|
1768 |
"target, target_name = dataset.load_SARS_CoV2_endoRNAse()\n", |
|
|
1769 |
"oneliner.repurpose(target = target, \n", |
|
|
1770 |
" target_name = target_name, \n", |
|
|
1771 |
" X_repurpose = X_repurpose,\n", |
|
|
1772 |
" drug_names = drug_names,\n", |
|
|
1773 |
" save_dir = './save_folder',\n", |
|
|
1774 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1775 |
" agg = 'mean')" |
|
|
1776 |
] |
|
|
1777 |
}, |
|
|
1778 |
{ |
|
|
1779 |
"cell_type": "code", |
|
|
1780 |
"execution_count": 17, |
|
|
1781 |
"metadata": { |
|
|
1782 |
"scrolled": false |
|
|
1783 |
}, |
|
|
1784 |
"outputs": [ |
|
|
1785 |
{ |
|
|
1786 |
"name": "stdout", |
|
|
1787 |
"output_type": "stream", |
|
|
1788 |
"text": [ |
|
|
1789 |
"Loading customized repurposing dataset...\n", |
|
|
1790 |
"Checking if pretrained directory is valid...\n", |
|
|
1791 |
"Beginning to load the pretrained models...\n", |
|
|
1792 |
"Using pretrained model and making predictions...\n", |
|
|
1793 |
"repurposing...\n", |
|
|
1794 |
"in total: 82 drug-target pairs\n", |
|
|
1795 |
"encoding drug...\n", |
|
|
1796 |
"unique drugs: 81\n", |
|
|
1797 |
"drug encoding finished...\n", |
|
|
1798 |
"encoding protein...\n", |
|
|
1799 |
"unique target sequence: 1\n", |
|
|
1800 |
"protein encoding finished...\n", |
|
|
1801 |
"Done.\n", |
|
|
1802 |
"predicting...\n", |
|
|
1803 |
"---------------\n", |
|
|
1804 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1805 |
"-------------\n", |
|
|
1806 |
"repurposing...\n", |
|
|
1807 |
"in total: 82 drug-target pairs\n", |
|
|
1808 |
"encoding drug...\n", |
|
|
1809 |
"unique drugs: 81\n", |
|
|
1810 |
"drug encoding finished...\n", |
|
|
1811 |
"encoding protein...\n", |
|
|
1812 |
"unique target sequence: 1\n", |
|
|
1813 |
"protein encoding finished...\n", |
|
|
1814 |
"Done.\n", |
|
|
1815 |
"predicting...\n", |
|
|
1816 |
"---------------\n", |
|
|
1817 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1818 |
"-------------\n", |
|
|
1819 |
"repurposing...\n", |
|
|
1820 |
"in total: 82 drug-target pairs\n", |
|
|
1821 |
"encoding drug...\n", |
|
|
1822 |
"unique drugs: 81\n", |
|
|
1823 |
"drug encoding finished...\n", |
|
|
1824 |
"encoding protein...\n", |
|
|
1825 |
"unique target sequence: 1\n", |
|
|
1826 |
"protein encoding finished...\n", |
|
|
1827 |
"Done.\n", |
|
|
1828 |
"predicting...\n", |
|
|
1829 |
"---------------\n", |
|
|
1830 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1831 |
"-------------\n", |
|
|
1832 |
"repurposing...\n", |
|
|
1833 |
"in total: 82 drug-target pairs\n", |
|
|
1834 |
"encoding drug...\n", |
|
|
1835 |
"unique drugs: 81\n", |
|
|
1836 |
"drug encoding finished...\n", |
|
|
1837 |
"encoding protein...\n", |
|
|
1838 |
"unique target sequence: 1\n", |
|
|
1839 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1840 |
"protein encoding finished...\n", |
|
|
1841 |
"Done.\n", |
|
|
1842 |
"predicting...\n", |
|
|
1843 |
"---------------\n", |
|
|
1844 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1845 |
"-------------\n", |
|
|
1846 |
"repurposing...\n", |
|
|
1847 |
"in total: 82 drug-target pairs\n", |
|
|
1848 |
"encoding drug...\n", |
|
|
1849 |
"unique drugs: 81\n", |
|
|
1850 |
"drug encoding finished...\n", |
|
|
1851 |
"encoding protein...\n", |
|
|
1852 |
"unique target sequence: 1\n", |
|
|
1853 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1854 |
"protein encoding finished...\n", |
|
|
1855 |
"Done.\n", |
|
|
1856 |
"predicting...\n", |
|
|
1857 |
"---------------\n", |
|
|
1858 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1859 |
"-------------\n", |
|
|
1860 |
"models prediction finished...\n", |
|
|
1861 |
"aggregating results...\n", |
|
|
1862 |
"---------------\n", |
|
|
1863 |
"Drug Repurposing Result for SARS_CoV2_endoRNAse\n", |
|
|
1864 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1865 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1866 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1867 |
"| 1 | Lopinavir | SARS_CoV2_endoRNAse | 0.18 |\n", |
|
|
1868 |
"| 2 | Darunavir | SARS_CoV2_endoRNAse | 0.23 |\n", |
|
|
1869 |
"| 3 | Amprenavir | SARS_CoV2_endoRNAse | 0.80 |\n", |
|
|
1870 |
"| 4 | Tipranavir | SARS_CoV2_endoRNAse | 2.65 |\n", |
|
|
1871 |
"| 5 | Baloxavir | SARS_CoV2_endoRNAse | 3.88 |\n", |
|
|
1872 |
"| 6 | Boceprevir | SARS_CoV2_endoRNAse | 3.96 |\n", |
|
|
1873 |
"| 7 | Daclatasvir | SARS_CoV2_endoRNAse | 4.57 |\n", |
|
|
1874 |
"| 8 | Oseltamivir | SARS_CoV2_endoRNAse | 5.11 |\n", |
|
|
1875 |
"| 9 | Vicriviroc | SARS_CoV2_endoRNAse | 5.27 |\n", |
|
|
1876 |
"| 10 | Glecaprevir | SARS_CoV2_endoRNAse | 5.33 |\n", |
|
|
1877 |
"| 11 | Fosamprenavir | SARS_CoV2_endoRNAse | 5.34 |\n", |
|
|
1878 |
"| 12 | Telaprevir | SARS_CoV2_endoRNAse | 5.64 |\n", |
|
|
1879 |
"| 13 | Nelfinavir | SARS_CoV2_endoRNAse | 7.91 |\n", |
|
|
1880 |
"| 14 | Amantadine | SARS_CoV2_endoRNAse | 7.91 |\n", |
|
|
1881 |
"| 15 | Foscarnet | SARS_CoV2_endoRNAse | 10.88 |\n", |
|
|
1882 |
"| 16 | Maraviroc | SARS_CoV2_endoRNAse | 12.47 |\n", |
|
|
1883 |
"| 17 | Pleconaril | SARS_CoV2_endoRNAse | 13.01 |\n", |
|
|
1884 |
"| 18 | Abacavir | SARS_CoV2_endoRNAse | 15.48 |\n", |
|
|
1885 |
"| 19 | Sofosbuvir | SARS_CoV2_endoRNAse | 19.61 |\n", |
|
|
1886 |
"| 20 | Rimantadine | SARS_CoV2_endoRNAse | 25.77 |\n", |
|
|
1887 |
"| 21 | Arbidol | SARS_CoV2_endoRNAse | 26.51 |\n", |
|
|
1888 |
"| 22 | Tenofovir | SARS_CoV2_endoRNAse | 29.80 |\n", |
|
|
1889 |
"| 23 | Descovy | SARS_CoV2_endoRNAse | 29.80 |\n", |
|
|
1890 |
"| 24 | Atazanavir | SARS_CoV2_endoRNAse | 32.23 |\n", |
|
|
1891 |
"| 25 | Letermovir | SARS_CoV2_endoRNAse | 32.71 |\n", |
|
|
1892 |
"| 26 | Ritonavir | SARS_CoV2_endoRNAse | 35.84 |\n", |
|
|
1893 |
"| 27 | Simeprevir | SARS_CoV2_endoRNAse | 36.19 |\n", |
|
|
1894 |
"| 28 | Saquinavir | SARS_CoV2_endoRNAse | 37.63 |\n", |
|
|
1895 |
"| 29 | Remdesivir | SARS_CoV2_endoRNAse | 38.42 |\n", |
|
|
1896 |
"| 30 | Etravirine | SARS_CoV2_endoRNAse | 40.88 |\n", |
|
|
1897 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
1898 |
"\n" |
|
|
1899 |
] |
|
|
1900 |
} |
|
|
1901 |
], |
|
|
1902 |
"source": [ |
|
|
1903 |
"oneliner.repurpose(target = target, \n", |
|
|
1904 |
" target_name = target_name, \n", |
|
|
1905 |
" X_repurpose = X_repurpose,\n", |
|
|
1906 |
" drug_names = drug_names,\n", |
|
|
1907 |
" save_dir = './save_folder',\n", |
|
|
1908 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
1909 |
" agg = 'max_effect')" |
|
|
1910 |
] |
|
|
1911 |
}, |
|
|
1912 |
{ |
|
|
1913 |
"cell_type": "code", |
|
|
1914 |
"execution_count": 18, |
|
|
1915 |
"metadata": {}, |
|
|
1916 |
"outputs": [ |
|
|
1917 |
{ |
|
|
1918 |
"name": "stdout", |
|
|
1919 |
"output_type": "stream", |
|
|
1920 |
"text": [ |
|
|
1921 |
"Loading customized repurposing dataset...\n", |
|
|
1922 |
"Checking if pretrained directory is valid...\n", |
|
|
1923 |
"Beginning to load the pretrained models...\n", |
|
|
1924 |
"Using pretrained model and making predictions...\n", |
|
|
1925 |
"repurposing...\n", |
|
|
1926 |
"in total: 82 drug-target pairs\n", |
|
|
1927 |
"encoding drug...\n", |
|
|
1928 |
"unique drugs: 81\n", |
|
|
1929 |
"drug encoding finished...\n", |
|
|
1930 |
"encoding protein...\n", |
|
|
1931 |
"unique target sequence: 1\n", |
|
|
1932 |
"protein encoding finished...\n", |
|
|
1933 |
"Done.\n", |
|
|
1934 |
"predicting...\n", |
|
|
1935 |
"---------------\n", |
|
|
1936 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
1937 |
"-------------\n", |
|
|
1938 |
"repurposing...\n", |
|
|
1939 |
"in total: 82 drug-target pairs\n", |
|
|
1940 |
"encoding drug...\n", |
|
|
1941 |
"unique drugs: 81\n", |
|
|
1942 |
"drug encoding finished...\n", |
|
|
1943 |
"encoding protein...\n", |
|
|
1944 |
"unique target sequence: 1\n", |
|
|
1945 |
"protein encoding finished...\n", |
|
|
1946 |
"Done.\n", |
|
|
1947 |
"predicting...\n", |
|
|
1948 |
"---------------\n", |
|
|
1949 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
1950 |
"-------------\n", |
|
|
1951 |
"repurposing...\n", |
|
|
1952 |
"in total: 82 drug-target pairs\n", |
|
|
1953 |
"encoding drug...\n", |
|
|
1954 |
"unique drugs: 81\n", |
|
|
1955 |
"drug encoding finished...\n", |
|
|
1956 |
"encoding protein...\n", |
|
|
1957 |
"unique target sequence: 1\n", |
|
|
1958 |
"protein encoding finished...\n", |
|
|
1959 |
"Done.\n", |
|
|
1960 |
"predicting...\n", |
|
|
1961 |
"---------------\n", |
|
|
1962 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
1963 |
"-------------\n", |
|
|
1964 |
"repurposing...\n", |
|
|
1965 |
"in total: 82 drug-target pairs\n", |
|
|
1966 |
"encoding drug...\n", |
|
|
1967 |
"unique drugs: 81\n", |
|
|
1968 |
"drug encoding finished...\n", |
|
|
1969 |
"encoding protein...\n", |
|
|
1970 |
"unique target sequence: 1\n", |
|
|
1971 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1972 |
"protein encoding finished...\n", |
|
|
1973 |
"Done.\n", |
|
|
1974 |
"predicting...\n", |
|
|
1975 |
"---------------\n", |
|
|
1976 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
1977 |
"-------------\n", |
|
|
1978 |
"repurposing...\n", |
|
|
1979 |
"in total: 82 drug-target pairs\n", |
|
|
1980 |
"encoding drug...\n", |
|
|
1981 |
"unique drugs: 81\n", |
|
|
1982 |
"drug encoding finished...\n", |
|
|
1983 |
"encoding protein...\n", |
|
|
1984 |
"unique target sequence: 1\n", |
|
|
1985 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
1986 |
"protein encoding finished...\n", |
|
|
1987 |
"Done.\n", |
|
|
1988 |
"predicting...\n", |
|
|
1989 |
"---------------\n", |
|
|
1990 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
1991 |
"-------------\n", |
|
|
1992 |
"models prediction finished...\n", |
|
|
1993 |
"aggregating results...\n", |
|
|
1994 |
"---------------\n", |
|
|
1995 |
"Drug Repurposing Result for SARS_CoV2_endoRNAse\n", |
|
|
1996 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1997 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
1998 |
"+------+----------------------+---------------------+---------------+\n", |
|
|
1999 |
"| 1 | Daclatasvir | SARS_CoV2_endoRNAse | 214.89 |\n", |
|
|
2000 |
"| 2 | Simeprevir | SARS_CoV2_endoRNAse | 250.15 |\n", |
|
|
2001 |
"| 3 | Sofosbuvir | SARS_CoV2_endoRNAse | 290.50 |\n", |
|
|
2002 |
"| 4 | Vicriviroc | SARS_CoV2_endoRNAse | 400.00 |\n", |
|
|
2003 |
"| 5 | Etravirine | SARS_CoV2_endoRNAse | 417.98 |\n", |
|
|
2004 |
"| 6 | Atazanavir | SARS_CoV2_endoRNAse | 421.78 |\n", |
|
|
2005 |
"| 7 | Letermovir | SARS_CoV2_endoRNAse | 456.19 |\n", |
|
|
2006 |
"| 8 | Darunavir | SARS_CoV2_endoRNAse | 459.65 |\n", |
|
|
2007 |
"| 9 | Lopinavir | SARS_CoV2_endoRNAse | 459.94 |\n", |
|
|
2008 |
"| 10 | Maraviroc | SARS_CoV2_endoRNAse | 464.10 |\n", |
|
|
2009 |
"| 11 | Rilpivirine | SARS_CoV2_endoRNAse | 468.50 |\n", |
|
|
2010 |
"| 12 | Fosamprenavir | SARS_CoV2_endoRNAse | 473.33 |\n", |
|
|
2011 |
"| 13 | Peramivir | SARS_CoV2_endoRNAse | 512.31 |\n", |
|
|
2012 |
"| 14 | Telaprevir | SARS_CoV2_endoRNAse | 574.14 |\n", |
|
|
2013 |
"| 15 | Amprenavir | SARS_CoV2_endoRNAse | 588.81 |\n", |
|
|
2014 |
"| 16 | Amantadine | SARS_CoV2_endoRNAse | 599.07 |\n", |
|
|
2015 |
"| 17 | Grazoprevir | SARS_CoV2_endoRNAse | 628.48 |\n", |
|
|
2016 |
"| 18 | Nelfinavir | SARS_CoV2_endoRNAse | 648.46 |\n", |
|
|
2017 |
"| 19 | Boceprevir | SARS_CoV2_endoRNAse | 799.77 |\n", |
|
|
2018 |
"| 20 | Elvitegravir | SARS_CoV2_endoRNAse | 817.42 |\n", |
|
|
2019 |
"| 21 | Doravirine | SARS_CoV2_endoRNAse | 853.32 |\n", |
|
|
2020 |
"| 22 | Raltegravir | SARS_CoV2_endoRNAse | 872.32 |\n", |
|
|
2021 |
"| 23 | Abacavir | SARS_CoV2_endoRNAse | 912.43 |\n", |
|
|
2022 |
"| 24 | Delavirdine | SARS_CoV2_endoRNAse | 920.16 |\n", |
|
|
2023 |
"| 25 | Saquinavir | SARS_CoV2_endoRNAse | 925.00 |\n", |
|
|
2024 |
"| 26 | Tenofovir_disoproxil | SARS_CoV2_endoRNAse | 969.32 |\n", |
|
|
2025 |
"| 27 | Ritonavir | SARS_CoV2_endoRNAse | 969.38 |\n", |
|
|
2026 |
"| 28 | Glecaprevir | SARS_CoV2_endoRNAse | 1078.72 |\n", |
|
|
2027 |
"| 29 | Pleconaril | SARS_CoV2_endoRNAse | 1101.19 |\n", |
|
|
2028 |
"| 30 | Tenofovir | SARS_CoV2_endoRNAse | 1266.07 |\n", |
|
|
2029 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
2030 |
"\n" |
|
|
2031 |
] |
|
|
2032 |
} |
|
|
2033 |
], |
|
|
2034 |
"source": [ |
|
|
2035 |
"oneliner.repurpose(target = target, \n", |
|
|
2036 |
" target_name = target_name, \n", |
|
|
2037 |
" X_repurpose = X_repurpose,\n", |
|
|
2038 |
" drug_names = drug_names,\n", |
|
|
2039 |
" save_dir = './save_folder',\n", |
|
|
2040 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
2041 |
" agg = 'agg_mean_max')" |
|
|
2042 |
] |
|
|
2043 |
}, |
|
|
2044 |
{ |
|
|
2045 |
"cell_type": "code", |
|
|
2046 |
"execution_count": 19, |
|
|
2047 |
"metadata": { |
|
|
2048 |
"scrolled": false |
|
|
2049 |
}, |
|
|
2050 |
"outputs": [ |
|
|
2051 |
{ |
|
|
2052 |
"name": "stdout", |
|
|
2053 |
"output_type": "stream", |
|
|
2054 |
"text": [ |
|
|
2055 |
"Loading customized repurposing dataset...\n", |
|
|
2056 |
"Checking if pretrained directory is valid...\n", |
|
|
2057 |
"Beginning to load the pretrained models...\n", |
|
|
2058 |
"Using pretrained model and making predictions...\n", |
|
|
2059 |
"repurposing...\n", |
|
|
2060 |
"in total: 82 drug-target pairs\n", |
|
|
2061 |
"encoding drug...\n", |
|
|
2062 |
"unique drugs: 81\n", |
|
|
2063 |
"drug encoding finished...\n", |
|
|
2064 |
"encoding protein...\n", |
|
|
2065 |
"unique target sequence: 1\n", |
|
|
2066 |
"protein encoding finished...\n", |
|
|
2067 |
"Done.\n", |
|
|
2068 |
"predicting...\n", |
|
|
2069 |
"---------------\n", |
|
|
2070 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
2071 |
"-------------\n", |
|
|
2072 |
"repurposing...\n", |
|
|
2073 |
"in total: 82 drug-target pairs\n", |
|
|
2074 |
"encoding drug...\n", |
|
|
2075 |
"unique drugs: 81\n", |
|
|
2076 |
"drug encoding finished...\n", |
|
|
2077 |
"encoding protein...\n", |
|
|
2078 |
"unique target sequence: 1\n", |
|
|
2079 |
"protein encoding finished...\n", |
|
|
2080 |
"Done.\n", |
|
|
2081 |
"predicting...\n", |
|
|
2082 |
"---------------\n", |
|
|
2083 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
2084 |
"-------------\n", |
|
|
2085 |
"repurposing...\n", |
|
|
2086 |
"in total: 82 drug-target pairs\n", |
|
|
2087 |
"encoding drug...\n", |
|
|
2088 |
"unique drugs: 81\n", |
|
|
2089 |
"drug encoding finished...\n", |
|
|
2090 |
"encoding protein...\n", |
|
|
2091 |
"unique target sequence: 1\n", |
|
|
2092 |
"protein encoding finished...\n", |
|
|
2093 |
"Done.\n", |
|
|
2094 |
"predicting...\n", |
|
|
2095 |
"---------------\n", |
|
|
2096 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
2097 |
"-------------\n", |
|
|
2098 |
"repurposing...\n", |
|
|
2099 |
"in total: 82 drug-target pairs\n", |
|
|
2100 |
"encoding drug...\n", |
|
|
2101 |
"unique drugs: 81\n", |
|
|
2102 |
"drug encoding finished...\n", |
|
|
2103 |
"encoding protein...\n", |
|
|
2104 |
"unique target sequence: 1\n", |
|
|
2105 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2106 |
"protein encoding finished...\n", |
|
|
2107 |
"Done.\n", |
|
|
2108 |
"predicting...\n", |
|
|
2109 |
"---------------\n", |
|
|
2110 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
2111 |
"-------------\n", |
|
|
2112 |
"repurposing...\n", |
|
|
2113 |
"in total: 82 drug-target pairs\n", |
|
|
2114 |
"encoding drug...\n", |
|
|
2115 |
"unique drugs: 81\n", |
|
|
2116 |
"drug encoding finished...\n", |
|
|
2117 |
"encoding protein...\n", |
|
|
2118 |
"unique target sequence: 1\n", |
|
|
2119 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2120 |
"protein encoding finished...\n", |
|
|
2121 |
"Done.\n", |
|
|
2122 |
"predicting...\n", |
|
|
2123 |
"---------------\n", |
|
|
2124 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
2125 |
"-------------\n", |
|
|
2126 |
"models prediction finished...\n", |
|
|
2127 |
"aggregating results...\n", |
|
|
2128 |
"---------------\n", |
|
|
2129 |
"Drug Repurposing Result for SARS_CoV2_2_O_ribose_methyltransferase\n", |
|
|
2130 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2131 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
2132 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2133 |
"| 1 | Sofosbuvir | SARS_CoV2_2_O_ribose_methyltransferase | 364.51 |\n", |
|
|
2134 |
"| 2 | Daclatasvir | SARS_CoV2_2_O_ribose_methyltransferase | 424.59 |\n", |
|
|
2135 |
"| 3 | Simeprevir | SARS_CoV2_2_O_ribose_methyltransferase | 512.20 |\n", |
|
|
2136 |
"| 4 | Vicriviroc | SARS_CoV2_2_O_ribose_methyltransferase | 739.15 |\n", |
|
|
2137 |
"| 5 | Etravirine | SARS_CoV2_2_O_ribose_methyltransferase | 776.94 |\n", |
|
|
2138 |
"| 6 | Atazanavir | SARS_CoV2_2_O_ribose_methyltransferase | 835.38 |\n", |
|
|
2139 |
"| 7 | Amantadine | SARS_CoV2_2_O_ribose_methyltransferase | 849.80 |\n", |
|
|
2140 |
"| 8 | Rilpivirine | SARS_CoV2_2_O_ribose_methyltransferase | 882.95 |\n", |
|
|
2141 |
"| 9 | Letermovir | SARS_CoV2_2_O_ribose_methyltransferase | 892.38 |\n", |
|
|
2142 |
"| 10 | Ritonavir | SARS_CoV2_2_O_ribose_methyltransferase | 916.95 |\n", |
|
|
2143 |
"| 11 | Lopinavir | SARS_CoV2_2_O_ribose_methyltransferase | 953.41 |\n", |
|
|
2144 |
"| 12 | Maraviroc | SARS_CoV2_2_O_ribose_methyltransferase | 956.00 |\n", |
|
|
2145 |
"| 13 | Darunavir | SARS_CoV2_2_O_ribose_methyltransferase | 956.72 |\n", |
|
|
2146 |
"| 14 | Peramivir | SARS_CoV2_2_O_ribose_methyltransferase | 968.37 |\n", |
|
|
2147 |
"| 15 | Grazoprevir | SARS_CoV2_2_O_ribose_methyltransferase | 976.05 |\n", |
|
|
2148 |
"| 16 | Fosamprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 977.57 |\n", |
|
|
2149 |
"| 17 | Efavirenz | SARS_CoV2_2_O_ribose_methyltransferase | 1075.01 |\n", |
|
|
2150 |
"| 18 | Telaprevir | SARS_CoV2_2_O_ribose_methyltransferase | 1136.33 |\n", |
|
|
2151 |
"| 19 | Elvitegravir | SARS_CoV2_2_O_ribose_methyltransferase | 1188.12 |\n", |
|
|
2152 |
"| 20 | Tenofovir | SARS_CoV2_2_O_ribose_methyltransferase | 1200.92 |\n", |
|
|
2153 |
"| 21 | Descovy | SARS_CoV2_2_O_ribose_methyltransferase | 1200.92 |\n", |
|
|
2154 |
"| 22 | Amprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 1222.05 |\n", |
|
|
2155 |
"| 23 | Nelfinavir | SARS_CoV2_2_O_ribose_methyltransferase | 1346.06 |\n", |
|
|
2156 |
"| 24 | Tenofovir_disoproxil | SARS_CoV2_2_O_ribose_methyltransferase | 1352.00 |\n", |
|
|
2157 |
"| 25 | Tromantadine | SARS_CoV2_2_O_ribose_methyltransferase | 1362.92 |\n", |
|
|
2158 |
"| 26 | Doravirine | SARS_CoV2_2_O_ribose_methyltransferase | 1508.91 |\n", |
|
|
2159 |
"| 27 | Dolutegravir | SARS_CoV2_2_O_ribose_methyltransferase | 1547.19 |\n", |
|
|
2160 |
"| 28 | Abacavir | SARS_CoV2_2_O_ribose_methyltransferase | 1614.96 |\n", |
|
|
2161 |
"| 29 | Delavirdine | SARS_CoV2_2_O_ribose_methyltransferase | 1699.89 |\n", |
|
|
2162 |
"| 30 | Saquinavir | SARS_CoV2_2_O_ribose_methyltransferase | 1766.76 |\n", |
|
|
2163 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
2164 |
"\n" |
|
|
2165 |
] |
|
|
2166 |
} |
|
|
2167 |
], |
|
|
2168 |
"source": [ |
|
|
2169 |
"target, target_name = dataset.load_SARS_CoV2_2_O_ribose_methyltransferase()\n", |
|
|
2170 |
"oneliner.repurpose(target = target, \n", |
|
|
2171 |
" target_name = target_name, \n", |
|
|
2172 |
" X_repurpose = X_repurpose,\n", |
|
|
2173 |
" drug_names = drug_names,\n", |
|
|
2174 |
" save_dir = './save_folder',\n", |
|
|
2175 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
2176 |
" agg = 'mean')" |
|
|
2177 |
] |
|
|
2178 |
}, |
|
|
2179 |
{ |
|
|
2180 |
"cell_type": "code", |
|
|
2181 |
"execution_count": 20, |
|
|
2182 |
"metadata": { |
|
|
2183 |
"scrolled": false |
|
|
2184 |
}, |
|
|
2185 |
"outputs": [ |
|
|
2186 |
{ |
|
|
2187 |
"name": "stdout", |
|
|
2188 |
"output_type": "stream", |
|
|
2189 |
"text": [ |
|
|
2190 |
"Loading customized repurposing dataset...\n", |
|
|
2191 |
"Checking if pretrained directory is valid...\n", |
|
|
2192 |
"Beginning to load the pretrained models...\n", |
|
|
2193 |
"Using pretrained model and making predictions...\n", |
|
|
2194 |
"repurposing...\n", |
|
|
2195 |
"in total: 82 drug-target pairs\n", |
|
|
2196 |
"encoding drug...\n", |
|
|
2197 |
"unique drugs: 81\n", |
|
|
2198 |
"drug encoding finished...\n", |
|
|
2199 |
"encoding protein...\n", |
|
|
2200 |
"unique target sequence: 1\n", |
|
|
2201 |
"protein encoding finished...\n", |
|
|
2202 |
"Done.\n", |
|
|
2203 |
"predicting...\n", |
|
|
2204 |
"---------------\n", |
|
|
2205 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
2206 |
"-------------\n", |
|
|
2207 |
"repurposing...\n", |
|
|
2208 |
"in total: 82 drug-target pairs\n", |
|
|
2209 |
"encoding drug...\n", |
|
|
2210 |
"unique drugs: 81\n", |
|
|
2211 |
"drug encoding finished...\n", |
|
|
2212 |
"encoding protein...\n", |
|
|
2213 |
"unique target sequence: 1\n", |
|
|
2214 |
"protein encoding finished...\n", |
|
|
2215 |
"Done.\n", |
|
|
2216 |
"predicting...\n", |
|
|
2217 |
"---------------\n", |
|
|
2218 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
2219 |
"-------------\n", |
|
|
2220 |
"repurposing...\n", |
|
|
2221 |
"in total: 82 drug-target pairs\n", |
|
|
2222 |
"encoding drug...\n", |
|
|
2223 |
"unique drugs: 81\n", |
|
|
2224 |
"drug encoding finished...\n", |
|
|
2225 |
"encoding protein...\n", |
|
|
2226 |
"unique target sequence: 1\n", |
|
|
2227 |
"protein encoding finished...\n", |
|
|
2228 |
"Done.\n", |
|
|
2229 |
"predicting...\n", |
|
|
2230 |
"---------------\n", |
|
|
2231 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
2232 |
"-------------\n", |
|
|
2233 |
"repurposing...\n", |
|
|
2234 |
"in total: 82 drug-target pairs\n", |
|
|
2235 |
"encoding drug...\n", |
|
|
2236 |
"unique drugs: 81\n", |
|
|
2237 |
"drug encoding finished...\n", |
|
|
2238 |
"encoding protein...\n", |
|
|
2239 |
"unique target sequence: 1\n", |
|
|
2240 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2241 |
"protein encoding finished...\n", |
|
|
2242 |
"Done.\n", |
|
|
2243 |
"predicting...\n", |
|
|
2244 |
"---------------\n", |
|
|
2245 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
2246 |
"-------------\n", |
|
|
2247 |
"repurposing...\n", |
|
|
2248 |
"in total: 82 drug-target pairs\n", |
|
|
2249 |
"encoding drug...\n", |
|
|
2250 |
"unique drugs: 81\n", |
|
|
2251 |
"drug encoding finished...\n", |
|
|
2252 |
"encoding protein...\n", |
|
|
2253 |
"unique target sequence: 1\n", |
|
|
2254 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2255 |
"protein encoding finished...\n", |
|
|
2256 |
"Done.\n", |
|
|
2257 |
"predicting...\n", |
|
|
2258 |
"---------------\n", |
|
|
2259 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
2260 |
"-------------\n", |
|
|
2261 |
"models prediction finished...\n", |
|
|
2262 |
"aggregating results...\n", |
|
|
2263 |
"---------------\n", |
|
|
2264 |
"Drug Repurposing Result for SARS_CoV2_2_O_ribose_methyltransferase\n", |
|
|
2265 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2266 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
2267 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2268 |
"| 1 | Lopinavir | SARS_CoV2_2_O_ribose_methyltransferase | 0.24 |\n", |
|
|
2269 |
"| 2 | Darunavir | SARS_CoV2_2_O_ribose_methyltransferase | 0.31 |\n", |
|
|
2270 |
"| 3 | Amprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 0.92 |\n", |
|
|
2271 |
"| 4 | Tipranavir | SARS_CoV2_2_O_ribose_methyltransferase | 1.46 |\n", |
|
|
2272 |
"| 5 | Baloxavir | SARS_CoV2_2_O_ribose_methyltransferase | 1.87 |\n", |
|
|
2273 |
"| 6 | Boceprevir | SARS_CoV2_2_O_ribose_methyltransferase | 2.23 |\n", |
|
|
2274 |
"| 7 | Glecaprevir | SARS_CoV2_2_O_ribose_methyltransferase | 2.48 |\n", |
|
|
2275 |
"| 8 | Oseltamivir | SARS_CoV2_2_O_ribose_methyltransferase | 2.83 |\n", |
|
|
2276 |
"| 9 | Telaprevir | SARS_CoV2_2_O_ribose_methyltransferase | 3.00 |\n", |
|
|
2277 |
"| 10 | Nelfinavir | SARS_CoV2_2_O_ribose_methyltransferase | 3.35 |\n", |
|
|
2278 |
"| 11 | Maraviroc | SARS_CoV2_2_O_ribose_methyltransferase | 4.97 |\n", |
|
|
2279 |
"| 12 | Daclatasvir | SARS_CoV2_2_O_ribose_methyltransferase | 5.68 |\n", |
|
|
2280 |
"| 13 | Vicriviroc | SARS_CoV2_2_O_ribose_methyltransferase | 6.15 |\n", |
|
|
2281 |
"| 14 | Fosamprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 6.23 |\n", |
|
|
2282 |
"| 15 | Amantadine | SARS_CoV2_2_O_ribose_methyltransferase | 9.76 |\n", |
|
|
2283 |
"| 16 | Etravirine | SARS_CoV2_2_O_ribose_methyltransferase | 10.07 |\n", |
|
|
2284 |
"| 17 | Foscarnet | SARS_CoV2_2_O_ribose_methyltransferase | 11.51 |\n", |
|
|
2285 |
"| 18 | Atazanavir | SARS_CoV2_2_O_ribose_methyltransferase | 11.70 |\n", |
|
|
2286 |
"| 19 | Entecavir | SARS_CoV2_2_O_ribose_methyltransferase | 11.73 |\n", |
|
|
2287 |
"| 20 | Pleconaril | SARS_CoV2_2_O_ribose_methyltransferase | 11.91 |\n", |
|
|
2288 |
"| 21 | Simeprevir | SARS_CoV2_2_O_ribose_methyltransferase | 13.29 |\n", |
|
|
2289 |
"| 22 | Rilpivirine | SARS_CoV2_2_O_ribose_methyltransferase | 13.73 |\n", |
|
|
2290 |
"| 23 | Abacavir | SARS_CoV2_2_O_ribose_methyltransferase | 15.62 |\n", |
|
|
2291 |
"| 24 | Sofosbuvir | SARS_CoV2_2_O_ribose_methyltransferase | 18.38 |\n", |
|
|
2292 |
"| 25 | Saquinavir | SARS_CoV2_2_O_ribose_methyltransferase | 19.33 |\n", |
|
|
2293 |
"| 26 | Delavirdine | SARS_CoV2_2_O_ribose_methyltransferase | 20.24 |\n", |
|
|
2294 |
"| 27 | Arbidol | SARS_CoV2_2_O_ribose_methyltransferase | 20.65 |\n", |
|
|
2295 |
"| 28 | Peramivir | SARS_CoV2_2_O_ribose_methyltransferase | 24.92 |\n", |
|
|
2296 |
"| 29 | Raltegravir | SARS_CoV2_2_O_ribose_methyltransferase | 25.47 |\n", |
|
|
2297 |
"| 30 | Tenofovir | SARS_CoV2_2_O_ribose_methyltransferase | 25.94 |\n", |
|
|
2298 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
2299 |
"\n" |
|
|
2300 |
] |
|
|
2301 |
} |
|
|
2302 |
], |
|
|
2303 |
"source": [ |
|
|
2304 |
"oneliner.repurpose(target = target, \n", |
|
|
2305 |
" target_name = target_name, \n", |
|
|
2306 |
" X_repurpose = X_repurpose,\n", |
|
|
2307 |
" drug_names = drug_names,\n", |
|
|
2308 |
" save_dir = './save_folder',\n", |
|
|
2309 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
2310 |
" agg = 'max_effect')" |
|
|
2311 |
] |
|
|
2312 |
}, |
|
|
2313 |
{ |
|
|
2314 |
"cell_type": "code", |
|
|
2315 |
"execution_count": 21, |
|
|
2316 |
"metadata": { |
|
|
2317 |
"scrolled": false |
|
|
2318 |
}, |
|
|
2319 |
"outputs": [ |
|
|
2320 |
{ |
|
|
2321 |
"name": "stdout", |
|
|
2322 |
"output_type": "stream", |
|
|
2323 |
"text": [ |
|
|
2324 |
"Loading customized repurposing dataset...\n", |
|
|
2325 |
"Checking if pretrained directory is valid...\n", |
|
|
2326 |
"Beginning to load the pretrained models...\n", |
|
|
2327 |
"Using pretrained model and making predictions...\n", |
|
|
2328 |
"repurposing...\n", |
|
|
2329 |
"in total: 82 drug-target pairs\n", |
|
|
2330 |
"encoding drug...\n", |
|
|
2331 |
"unique drugs: 81\n", |
|
|
2332 |
"drug encoding finished...\n", |
|
|
2333 |
"encoding protein...\n", |
|
|
2334 |
"unique target sequence: 1\n", |
|
|
2335 |
"protein encoding finished...\n", |
|
|
2336 |
"Done.\n", |
|
|
2337 |
"predicting...\n", |
|
|
2338 |
"---------------\n", |
|
|
2339 |
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
|
|
2340 |
"-------------\n", |
|
|
2341 |
"repurposing...\n", |
|
|
2342 |
"in total: 82 drug-target pairs\n", |
|
|
2343 |
"encoding drug...\n", |
|
|
2344 |
"unique drugs: 81\n", |
|
|
2345 |
"drug encoding finished...\n", |
|
|
2346 |
"encoding protein...\n", |
|
|
2347 |
"unique target sequence: 1\n", |
|
|
2348 |
"protein encoding finished...\n", |
|
|
2349 |
"Done.\n", |
|
|
2350 |
"predicting...\n", |
|
|
2351 |
"---------------\n", |
|
|
2352 |
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
|
|
2353 |
"-------------\n", |
|
|
2354 |
"repurposing...\n", |
|
|
2355 |
"in total: 82 drug-target pairs\n", |
|
|
2356 |
"encoding drug...\n", |
|
|
2357 |
"unique drugs: 81\n", |
|
|
2358 |
"drug encoding finished...\n", |
|
|
2359 |
"encoding protein...\n", |
|
|
2360 |
"unique target sequence: 1\n", |
|
|
2361 |
"protein encoding finished...\n", |
|
|
2362 |
"Done.\n", |
|
|
2363 |
"predicting...\n", |
|
|
2364 |
"---------------\n", |
|
|
2365 |
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
|
|
2366 |
"-------------\n", |
|
|
2367 |
"repurposing...\n", |
|
|
2368 |
"in total: 82 drug-target pairs\n", |
|
|
2369 |
"encoding drug...\n", |
|
|
2370 |
"unique drugs: 81\n", |
|
|
2371 |
"drug encoding finished...\n", |
|
|
2372 |
"encoding protein...\n", |
|
|
2373 |
"unique target sequence: 1\n", |
|
|
2374 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2375 |
"protein encoding finished...\n", |
|
|
2376 |
"Done.\n", |
|
|
2377 |
"predicting...\n", |
|
|
2378 |
"---------------\n", |
|
|
2379 |
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
|
|
2380 |
"-------------\n", |
|
|
2381 |
"repurposing...\n", |
|
|
2382 |
"in total: 82 drug-target pairs\n", |
|
|
2383 |
"encoding drug...\n", |
|
|
2384 |
"unique drugs: 81\n", |
|
|
2385 |
"drug encoding finished...\n", |
|
|
2386 |
"encoding protein...\n", |
|
|
2387 |
"unique target sequence: 1\n", |
|
|
2388 |
"-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
|
|
2389 |
"protein encoding finished...\n", |
|
|
2390 |
"Done.\n", |
|
|
2391 |
"predicting...\n", |
|
|
2392 |
"---------------\n", |
|
|
2393 |
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
|
|
2394 |
"-------------\n", |
|
|
2395 |
"models prediction finished...\n", |
|
|
2396 |
"aggregating results...\n", |
|
|
2397 |
"---------------\n", |
|
|
2398 |
"Drug Repurposing Result for SARS_CoV2_2_O_ribose_methyltransferase\n", |
|
|
2399 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2400 |
"| Rank | Drug Name | Target Name | Binding Score |\n", |
|
|
2401 |
"+------+----------------------+----------------------------------------+---------------+\n", |
|
|
2402 |
"| 1 | Sofosbuvir | SARS_CoV2_2_O_ribose_methyltransferase | 191.45 |\n", |
|
|
2403 |
"| 2 | Daclatasvir | SARS_CoV2_2_O_ribose_methyltransferase | 215.14 |\n", |
|
|
2404 |
"| 3 | Simeprevir | SARS_CoV2_2_O_ribose_methyltransferase | 262.75 |\n", |
|
|
2405 |
"| 4 | Vicriviroc | SARS_CoV2_2_O_ribose_methyltransferase | 372.65 |\n", |
|
|
2406 |
"| 5 | Etravirine | SARS_CoV2_2_O_ribose_methyltransferase | 393.50 |\n", |
|
|
2407 |
"| 6 | Atazanavir | SARS_CoV2_2_O_ribose_methyltransferase | 423.54 |\n", |
|
|
2408 |
"| 7 | Amantadine | SARS_CoV2_2_O_ribose_methyltransferase | 429.78 |\n", |
|
|
2409 |
"| 8 | Rilpivirine | SARS_CoV2_2_O_ribose_methyltransferase | 448.34 |\n", |
|
|
2410 |
"| 9 | Letermovir | SARS_CoV2_2_O_ribose_methyltransferase | 462.16 |\n", |
|
|
2411 |
"| 10 | Lopinavir | SARS_CoV2_2_O_ribose_methyltransferase | 476.83 |\n", |
|
|
2412 |
"| 11 | Darunavir | SARS_CoV2_2_O_ribose_methyltransferase | 478.52 |\n", |
|
|
2413 |
"| 12 | Ritonavir | SARS_CoV2_2_O_ribose_methyltransferase | 479.50 |\n", |
|
|
2414 |
"| 13 | Maraviroc | SARS_CoV2_2_O_ribose_methyltransferase | 480.49 |\n", |
|
|
2415 |
"| 14 | Fosamprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 491.90 |\n", |
|
|
2416 |
"| 15 | Peramivir | SARS_CoV2_2_O_ribose_methyltransferase | 496.64 |\n", |
|
|
2417 |
"| 16 | Grazoprevir | SARS_CoV2_2_O_ribose_methyltransferase | 523.70 |\n", |
|
|
2418 |
"| 17 | Telaprevir | SARS_CoV2_2_O_ribose_methyltransferase | 569.67 |\n", |
|
|
2419 |
"| 18 | Amprenavir | SARS_CoV2_2_O_ribose_methyltransferase | 611.48 |\n", |
|
|
2420 |
"| 19 | Tenofovir | SARS_CoV2_2_O_ribose_methyltransferase | 613.43 |\n", |
|
|
2421 |
"| 20 | Descovy | SARS_CoV2_2_O_ribose_methyltransferase | 613.43 |\n", |
|
|
2422 |
"| 21 | Elvitegravir | SARS_CoV2_2_O_ribose_methyltransferase | 639.14 |\n", |
|
|
2423 |
"| 22 | Efavirenz | SARS_CoV2_2_O_ribose_methyltransferase | 669.05 |\n", |
|
|
2424 |
"| 23 | Nelfinavir | SARS_CoV2_2_O_ribose_methyltransferase | 674.70 |\n", |
|
|
2425 |
"| 24 | Tenofovir_disoproxil | SARS_CoV2_2_O_ribose_methyltransferase | 719.19 |\n", |
|
|
2426 |
"| 25 | Doravirine | SARS_CoV2_2_O_ribose_methyltransferase | 778.37 |\n", |
|
|
2427 |
"| 26 | Abacavir | SARS_CoV2_2_O_ribose_methyltransferase | 815.29 |\n", |
|
|
2428 |
"| 27 | Delavirdine | SARS_CoV2_2_O_ribose_methyltransferase | 860.06 |\n", |
|
|
2429 |
"| 28 | Dolutegravir | SARS_CoV2_2_O_ribose_methyltransferase | 867.61 |\n", |
|
|
2430 |
"| 29 | Saquinavir | SARS_CoV2_2_O_ribose_methyltransferase | 893.04 |\n", |
|
|
2431 |
"| 30 | Tromantadine | SARS_CoV2_2_O_ribose_methyltransferase | 899.18 |\n", |
|
|
2432 |
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
|
|
2433 |
"\n" |
|
|
2434 |
] |
|
|
2435 |
} |
|
|
2436 |
], |
|
|
2437 |
"source": [ |
|
|
2438 |
"oneliner.repurpose(target = target, \n", |
|
|
2439 |
" target_name = target_name, \n", |
|
|
2440 |
" X_repurpose = X_repurpose,\n", |
|
|
2441 |
" drug_names = drug_names,\n", |
|
|
2442 |
" save_dir = './save_folder',\n", |
|
|
2443 |
" pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/',\n", |
|
|
2444 |
" agg = 'agg_mean_max')" |
|
|
2445 |
] |
|
|
2446 |
} |
|
|
2447 |
], |
|
|
2448 |
"metadata": { |
|
|
2449 |
"kernelspec": { |
|
|
2450 |
"display_name": "Python 3", |
|
|
2451 |
"language": "python", |
|
|
2452 |
"name": "python3" |
|
|
2453 |
}, |
|
|
2454 |
"language_info": { |
|
|
2455 |
"codemirror_mode": { |
|
|
2456 |
"name": "ipython", |
|
|
2457 |
"version": 3 |
|
|
2458 |
}, |
|
|
2459 |
"file_extension": ".py", |
|
|
2460 |
"mimetype": "text/x-python", |
|
|
2461 |
"name": "python", |
|
|
2462 |
"nbconvert_exporter": "python", |
|
|
2463 |
"pygments_lexer": "ipython3", |
|
|
2464 |
"version": "3.7.7" |
|
|
2465 |
} |
|
|
2466 |
}, |
|
|
2467 |
"nbformat": 4, |
|
|
2468 |
"nbformat_minor": 4 |
|
|
2469 |
} |