169 lines (168 with data), 7.1 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.chdir('../')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading customized repurposing dataset...\n",
"Beginning Downloading Pretrained Model...\n",
"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",
"Downloading finished... Beginning to extract zip file...\n",
"Pretrained Models Successfully Downloaded...\n",
"Using pretrained model and making predictions...\n",
"repurposing...\n",
"Drug Target Interaction Prediction Mode...\n",
"in total: 82 drug-target pairs\n",
"encoding drug...\n",
"unique drugs: 81\n",
"encoding protein...\n",
"unique target sequence: 1\n",
"Done.\n",
"predicting...\n",
"---------------\n",
"Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n",
"-------------\n",
"repurposing...\n",
"Drug Target Interaction Prediction Mode...\n",
"in total: 82 drug-target pairs\n",
"encoding drug...\n",
"unique drugs: 81\n",
"encoding protein...\n",
"unique target sequence: 1\n",
"Done.\n",
"predicting...\n",
"---------------\n",
"Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n",
"-------------\n",
"repurposing...\n",
"Drug Target Interaction Prediction Mode...\n",
"in total: 82 drug-target pairs\n",
"encoding drug...\n",
"unique drugs: 81\n",
"encoding protein...\n",
"unique target sequence: 1\n",
"Done.\n",
"predicting...\n",
"---------------\n",
"Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n",
"-------------\n",
"repurposing...\n",
"Drug Target Interaction Prediction Mode...\n",
"in total: 82 drug-target pairs\n",
"encoding drug...\n",
"unique drugs: 81\n",
"encoding protein...\n",
"unique target sequence: 1\n",
"-- 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",
"Done.\n",
"predicting...\n",
"---------------\n",
"Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n",
"-------------\n",
"repurposing...\n",
"Drug Target Interaction Prediction Mode...\n",
"in total: 82 drug-target pairs\n",
"encoding drug...\n",
"unique drugs: 81\n",
"encoding protein...\n",
"unique target sequence: 1\n",
"-- 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",
"Done.\n",
"predicting...\n",
"---------------\n",
"Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n",
"-------------\n",
"models prediction finished...\n",
"aggregating results...\n",
"---------------\n",
"Drug Repurposing Result for SARS-CoV2 3CL Protease\n",
"+------+----------------------+------------------------+---------------+\n",
"| Rank | Drug Name | Target Name | Binding Score |\n",
"+------+----------------------+------------------------+---------------+\n",
"| 1 | Sofosbuvir | SARS-CoV2 3CL Protease | 190.25 |\n",
"| 2 | Daclatasvir | SARS-CoV2 3CL Protease | 214.58 |\n",
"| 3 | Vicriviroc | SARS-CoV2 3CL Protease | 315.70 |\n",
"| 4 | Simeprevir | SARS-CoV2 3CL Protease | 396.53 |\n",
"| 5 | Etravirine | SARS-CoV2 3CL Protease | 409.34 |\n",
"| 6 | Amantadine | SARS-CoV2 3CL Protease | 419.76 |\n",
"| 7 | Letermovir | SARS-CoV2 3CL Protease | 460.28 |\n",
"| 8 | Rilpivirine | SARS-CoV2 3CL Protease | 470.79 |\n",
"| 9 | Darunavir | SARS-CoV2 3CL Protease | 472.24 |\n",
"| 10 | Lopinavir | SARS-CoV2 3CL Protease | 473.01 |\n",
"| 11 | Maraviroc | SARS-CoV2 3CL Protease | 474.86 |\n",
"| 12 | Fosamprenavir | SARS-CoV2 3CL Protease | 487.45 |\n",
"| 13 | Ritonavir | SARS-CoV2 3CL Protease | 492.19 |\n",
"| 14 | Efavirenz | SARS-CoV2 3CL Protease | 513.81 |\n",
"| 15 | Peramivir | SARS-CoV2 3CL Protease | 538.11 |\n",
"| 16 | Amprenavir | SARS-CoV2 3CL Protease | 602.76 |\n",
"| 17 | Telaprevir | SARS-CoV2 3CL Protease | 607.84 |\n",
"| 18 | Grazoprevir | SARS-CoV2 3CL Protease | 632.54 |\n",
"| 19 | Tenofovir | SARS-CoV2 3CL Protease | 637.96 |\n",
"| 20 | Descovy | SARS-CoV2 3CL Protease | 637.96 |\n",
"| 21 | Elvitegravir | SARS-CoV2 3CL Protease | 654.94 |\n",
"| 22 | Atazanavir | SARS-CoV2 3CL Protease | 679.53 |\n",
"| 23 | Nelfinavir | SARS-CoV2 3CL Protease | 727.49 |\n",
"| 24 | Abacavir | SARS-CoV2 3CL Protease | 738.80 |\n",
"| 25 | Tenofovir_disoproxil | SARS-CoV2 3CL Protease | 828.19 |\n",
"| 26 | Delavirdine | SARS-CoV2 3CL Protease | 856.06 |\n",
"| 27 | Tromantadine | SARS-CoV2 3CL Protease | 863.40 |\n",
"| 28 | Saquinavir | SARS-CoV2 3CL Protease | 891.75 |\n",
"| 29 | Dolutegravir | SARS-CoV2 3CL Protease | 920.32 |\n",
"| 30 | Raltegravir | SARS-CoV2 3CL Protease | 938.43 |\n",
"checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n",
"\n"
]
}
],
"source": [
"from DeepPurpose import oneliner\n",
"from DeepPurpose.dataset import *\n",
"\n",
"oneliner.repurpose(*load_SARS_CoV2_Protease_3CL(), *load_antiviral_drugs(no_cid = True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}