Datasets
Models
Downloads: 1
Diff of
/DEMO/case-study-I-Drug-Repurposing-for-3CLPro.ipynb
[000000]
..
[0a9449]
Switch to unified view
| a | b/DEMO/case-study-I-Drug-Repurposing-for-3CLPro.ipynb | ||
|---|---|---|---|
| 1 | { |
||
| 2 | "cells": [ |
||
| 3 | { |
||
| 4 | "cell_type": "code", |
||
| 5 | "execution_count": 1, |
||
| 6 | "metadata": {}, |
||
| 7 | "outputs": [], |
||
| 8 | "source": [ |
||
| 9 | "import os\n", |
||
| 10 | "os.chdir('../')" |
||
| 11 | ] |
||
| 12 | }, |
||
| 13 | { |
||
| 14 | "cell_type": "code", |
||
| 15 | "execution_count": 2, |
||
| 16 | "metadata": {}, |
||
| 17 | "outputs": [ |
||
| 18 | { |
||
| 19 | "name": "stdout", |
||
| 20 | "output_type": "stream", |
||
| 21 | "text": [ |
||
| 22 | "Loading customized repurposing dataset...\n", |
||
| 23 | "Beginning Downloading Pretrained Model...\n", |
||
| 24 | "Note: if you have already download the pretrained model before, please stop the program and set the input parameter 'pretrained_dir' to the path\n", |
||
| 25 | "Downloading finished... Beginning to extract zip file...\n", |
||
| 26 | "Pretrained Models Successfully Downloaded...\n", |
||
| 27 | "Using pretrained model and making predictions...\n", |
||
| 28 | "repurposing...\n", |
||
| 29 | "Drug Target Interaction Prediction Mode...\n", |
||
| 30 | "in total: 82 drug-target pairs\n", |
||
| 31 | "encoding drug...\n", |
||
| 32 | "unique drugs: 81\n", |
||
| 33 | "encoding protein...\n", |
||
| 34 | "unique target sequence: 1\n", |
||
| 35 | "Done.\n", |
||
| 36 | "predicting...\n", |
||
| 37 | "---------------\n", |
||
| 38 | "Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n", |
||
| 39 | "-------------\n", |
||
| 40 | "repurposing...\n", |
||
| 41 | "Drug Target Interaction Prediction Mode...\n", |
||
| 42 | "in total: 82 drug-target pairs\n", |
||
| 43 | "encoding drug...\n", |
||
| 44 | "unique drugs: 81\n", |
||
| 45 | "encoding protein...\n", |
||
| 46 | "unique target sequence: 1\n", |
||
| 47 | "Done.\n", |
||
| 48 | "predicting...\n", |
||
| 49 | "---------------\n", |
||
| 50 | "Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n", |
||
| 51 | "-------------\n", |
||
| 52 | "repurposing...\n", |
||
| 53 | "Drug Target Interaction Prediction Mode...\n", |
||
| 54 | "in total: 82 drug-target pairs\n", |
||
| 55 | "encoding drug...\n", |
||
| 56 | "unique drugs: 81\n", |
||
| 57 | "encoding protein...\n", |
||
| 58 | "unique target sequence: 1\n", |
||
| 59 | "Done.\n", |
||
| 60 | "predicting...\n", |
||
| 61 | "---------------\n", |
||
| 62 | "Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n", |
||
| 63 | "-------------\n", |
||
| 64 | "repurposing...\n", |
||
| 65 | "Drug Target Interaction Prediction Mode...\n", |
||
| 66 | "in total: 82 drug-target pairs\n", |
||
| 67 | "encoding drug...\n", |
||
| 68 | "unique drugs: 81\n", |
||
| 69 | "encoding protein...\n", |
||
| 70 | "unique target sequence: 1\n", |
||
| 71 | "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU.\t\t\t\t Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
||
| 72 | "Done.\n", |
||
| 73 | "predicting...\n", |
||
| 74 | "---------------\n", |
||
| 75 | "Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n", |
||
| 76 | "-------------\n", |
||
| 77 | "repurposing...\n", |
||
| 78 | "Drug Target Interaction Prediction Mode...\n", |
||
| 79 | "in total: 82 drug-target pairs\n", |
||
| 80 | "encoding drug...\n", |
||
| 81 | "unique drugs: 81\n", |
||
| 82 | "encoding protein...\n", |
||
| 83 | "unique target sequence: 1\n", |
||
| 84 | "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU.\t\t\t\t Calculate your time by the unique target sequence #, instead of the entire dataset.\n", |
||
| 85 | "Done.\n", |
||
| 86 | "predicting...\n", |
||
| 87 | "---------------\n", |
||
| 88 | "Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n", |
||
| 89 | "-------------\n", |
||
| 90 | "models prediction finished...\n", |
||
| 91 | "aggregating results...\n", |
||
| 92 | "---------------\n", |
||
| 93 | "Drug Repurposing Result for SARS-CoV2 3CL Protease\n", |
||
| 94 | "+------+----------------------+------------------------+---------------+\n", |
||
| 95 | "| Rank | Drug Name | Target Name | Binding Score |\n", |
||
| 96 | "+------+----------------------+------------------------+---------------+\n", |
||
| 97 | "| 1 | Sofosbuvir | SARS-CoV2 3CL Protease | 190.25 |\n", |
||
| 98 | "| 2 | Daclatasvir | SARS-CoV2 3CL Protease | 214.58 |\n", |
||
| 99 | "| 3 | Vicriviroc | SARS-CoV2 3CL Protease | 315.70 |\n", |
||
| 100 | "| 4 | Simeprevir | SARS-CoV2 3CL Protease | 396.53 |\n", |
||
| 101 | "| 5 | Etravirine | SARS-CoV2 3CL Protease | 409.34 |\n", |
||
| 102 | "| 6 | Amantadine | SARS-CoV2 3CL Protease | 419.76 |\n", |
||
| 103 | "| 7 | Letermovir | SARS-CoV2 3CL Protease | 460.28 |\n", |
||
| 104 | "| 8 | Rilpivirine | SARS-CoV2 3CL Protease | 470.79 |\n", |
||
| 105 | "| 9 | Darunavir | SARS-CoV2 3CL Protease | 472.24 |\n", |
||
| 106 | "| 10 | Lopinavir | SARS-CoV2 3CL Protease | 473.01 |\n", |
||
| 107 | "| 11 | Maraviroc | SARS-CoV2 3CL Protease | 474.86 |\n", |
||
| 108 | "| 12 | Fosamprenavir | SARS-CoV2 3CL Protease | 487.45 |\n", |
||
| 109 | "| 13 | Ritonavir | SARS-CoV2 3CL Protease | 492.19 |\n", |
||
| 110 | "| 14 | Efavirenz | SARS-CoV2 3CL Protease | 513.81 |\n", |
||
| 111 | "| 15 | Peramivir | SARS-CoV2 3CL Protease | 538.11 |\n", |
||
| 112 | "| 16 | Amprenavir | SARS-CoV2 3CL Protease | 602.76 |\n", |
||
| 113 | "| 17 | Telaprevir | SARS-CoV2 3CL Protease | 607.84 |\n", |
||
| 114 | "| 18 | Grazoprevir | SARS-CoV2 3CL Protease | 632.54 |\n", |
||
| 115 | "| 19 | Tenofovir | SARS-CoV2 3CL Protease | 637.96 |\n", |
||
| 116 | "| 20 | Descovy | SARS-CoV2 3CL Protease | 637.96 |\n", |
||
| 117 | "| 21 | Elvitegravir | SARS-CoV2 3CL Protease | 654.94 |\n", |
||
| 118 | "| 22 | Atazanavir | SARS-CoV2 3CL Protease | 679.53 |\n", |
||
| 119 | "| 23 | Nelfinavir | SARS-CoV2 3CL Protease | 727.49 |\n", |
||
| 120 | "| 24 | Abacavir | SARS-CoV2 3CL Protease | 738.80 |\n", |
||
| 121 | "| 25 | Tenofovir_disoproxil | SARS-CoV2 3CL Protease | 828.19 |\n", |
||
| 122 | "| 26 | Delavirdine | SARS-CoV2 3CL Protease | 856.06 |\n", |
||
| 123 | "| 27 | Tromantadine | SARS-CoV2 3CL Protease | 863.40 |\n", |
||
| 124 | "| 28 | Saquinavir | SARS-CoV2 3CL Protease | 891.75 |\n", |
||
| 125 | "| 29 | Dolutegravir | SARS-CoV2 3CL Protease | 920.32 |\n", |
||
| 126 | "| 30 | Raltegravir | SARS-CoV2 3CL Protease | 938.43 |\n", |
||
| 127 | "checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n", |
||
| 128 | "\n" |
||
| 129 | ] |
||
| 130 | } |
||
| 131 | ], |
||
| 132 | "source": [ |
||
| 133 | "from DeepPurpose import oneliner\n", |
||
| 134 | "from DeepPurpose.dataset import *\n", |
||
| 135 | "\n", |
||
| 136 | "oneliner.repurpose(*load_SARS_CoV2_Protease_3CL(), *load_antiviral_drugs(no_cid = True))" |
||
| 137 | ] |
||
| 138 | }, |
||
| 139 | { |
||
| 140 | "cell_type": "code", |
||
| 141 | "execution_count": null, |
||
| 142 | "metadata": {}, |
||
| 143 | "outputs": [], |
||
| 144 | "source": [] |
||
| 145 | } |
||
| 146 | ], |
||
| 147 | "metadata": { |
||
| 148 | "kernelspec": { |
||
| 149 | "display_name": "Python 3", |
||
| 150 | "language": "python", |
||
| 151 | "name": "python3" |
||
| 152 | }, |
||
| 153 | "language_info": { |
||
| 154 | "codemirror_mode": { |
||
| 155 | "name": "ipython", |
||
| 156 | "version": 3 |
||
| 157 | }, |
||
| 158 | "file_extension": ".py", |
||
| 159 | "mimetype": "text/x-python", |
||
| 160 | "name": "python", |
||
| 161 | "nbconvert_exporter": "python", |
||
| 162 | "pygments_lexer": "ipython3", |
||
| 163 | "version": "3.7.7" |
||
| 164 | } |
||
| 165 | }, |
||
| 166 | "nbformat": 4, |
||
| 167 | "nbformat_minor": 4 |
||
| 168 | } |