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  1. .gitattributes +11 -35
  2. README.md +2 -3
  3. ReVa2.spec +50 -0
  4. ReVa3.spec +0 -0
  5. ReVa_version.rc +6 -0
  6. asset/data/PubChem_compound_text_adjuvant.csv +0 -0
  7. asset/data/PubChem_compound_text_adjuvant_records.sdf.gz +0 -0
  8. asset/data/b cell receptor homo sapiens.csv +0 -0
  9. asset/data/b cell receptor homo sapiens.fasta +0 -0
  10. asset/data/b_receptor_v2.csv +3 -0
  11. asset/data/t cell receptor homo sapiens.csv +0 -0
  12. asset/data/t cell receptor homo sapiens.fasta +0 -0
  13. asset/data/t_receptor_v2.csv +3 -0
  14. asset/img/kaede_kayano.ico +0 -0
  15. asset/img/kaede_kayano.jpg +0 -0
  16. asset/img/linux.png +0 -0
  17. asset/json/header.json +9 -0
  18. asset/label/BPepTree_label.json +1 -0
  19. asset/label/BPepTree_label_quantum.json +1 -0
  20. asset/label/TPepTree_label.json +1 -0
  21. asset/label/TPepTree_label_quantum.json +1 -0
  22. asset/label/allergenicity_label_mapping.json +1 -0
  23. asset/label/allergenicity_label_mapping_quantum.json +1 -0
  24. asset/label/antigenicity_label_mapping.json +1 -0
  25. asset/label/antigenicity_label_mapping_quantum.json +1 -0
  26. asset/label/toxin_label_mapping.json +1 -0
  27. asset/label/toxin_label_mapping_quantum.json +1 -0
  28. asset/model/BPepDL.h5 +0 -0
  29. asset/model/BPepDL_label.json +1 -0
  30. asset/model/BPepTree.joblib +3 -0
  31. asset/model/BPepTree.pkl +3 -0
  32. asset/model/Linear_Regression_Model.pkl +0 -0
  33. asset/model/Quantum Model/B_vqc_model +0 -0
  34. asset/model/Quantum Model/T_vqc_model +0 -0
  35. asset/model/Quantum Model/VQR_quantum regression-based scoring function +0 -0
  36. asset/model/Quantum Model/allergen_vqc_model +0 -0
  37. asset/model/Quantum Model/antigen_vqc_model +0 -0
  38. asset/model/Quantum Model/test.ipynb +252 -0
  39. asset/model/Quantum Model/toxin_vqc_model +0 -0
  40. asset/model/TPepDL.h5 +0 -0
  41. asset/model/TPepDL_label.json +1 -0
  42. asset/model/TPepTree.joblib +3 -0
  43. asset/model/TPepTree.pkl +3 -0
  44. asset/model/allerginicity.h5 +3 -0
  45. asset/model/antigenicity.h5 +3 -0
  46. asset/model/toxin.h5 +0 -0
  47. datas.py +34 -0
  48. datas.txt +76 -0
  49. library.txt +14 -0
  50. manage.py +10 -0
.gitattributes CHANGED
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+ # Auto detect text files and perform LF normalization
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+ * text=auto
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README.md CHANGED
@@ -1,3 +1,2 @@
1
- ---
2
- license: mit
3
- ---
 
1
+ # ReVa_AI_For_Vaccine_Design_In_Silico
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+
 
ReVa2.spec ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # -*- mode: python ; coding: utf-8 -*-
2
+
3
+
4
+ block_cipher = None
5
+
6
+
7
+ a = Analysis(
8
+ ['ReVa2', 'copy.spec'],
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+ pathex=[],
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+ binaries=[],
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+ datas=[],
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+ hiddenimports=[],
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+ hookspath=[],
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+ excludes=[],
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+ win_no_prefer_redirects=False,
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+ win_private_assemblies=False,
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+ cipher=block_cipher,
20
+ noarchive=False,
21
+ )
22
+ pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
23
+
24
+ exe = EXE(
25
+ pyz,
26
+ a.scripts,
27
+ [],
28
+ exclude_binaries=True,
29
+ name='ReVa2',
30
+ debug=False,
31
+ bootloader_ignore_signals=False,
32
+ strip=False,
33
+ upx=True,
34
+ console=True,
35
+ disable_windowed_traceback=False,
36
+ argv_emulation=False,
37
+ target_arch=None,
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+ codesign_identity=None,
39
+ entitlements_file=None,
40
+ )
41
+ coll = COLLECT(
42
+ exe,
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+ a.binaries,
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+ a.zipfiles,
45
+ a.datas,
46
+ strip=False,
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+ upx=True,
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+ upx_exclude=[],
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+ name='ReVa2',
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+ )
ReVa3.spec ADDED
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+ PRODUCTVERSION 0,0,0,1
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+ FILEDESCRIPTION "ReVa 0.0.0.1 Alpha Version"
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+ FILEOS VOS__WINDOWS32
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+ FILETYPE VFT_APP
asset/data/PubChem_compound_text_adjuvant.csv ADDED
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asset/json/header.json ADDED
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+ {
2
+ "res1" : ["Amino Acid", "Predictions", "Probabilities"],
3
+ "res2" : ["Peptide", "Label"],
4
+ "res3" : ["Peptide", "Allergenicity", "Toxin", "Antigenicity", "Hydrophobicity", "Kolaskar Antigenicity", "Tangonkar Antigenicity", "Emini Surface Accessibility", "Similarity"],
5
+ "res4" : ["Peptide", "Allergenicity", "Toxin", "Antigenicity"],
6
+ "physicochemical" : ["Peptide", "Instability", "Aliphatic", "GRAVY", "Extinction", "Half Life(Mamalia)", "Formula","C", "H", "N", "O", "S", "Theoretical pI", "mol weight"],
7
+ "classical_docking_ff" : ["Ligand", "Receptor", "Receptor id", "Attractive", "Repulsive", "VDW LJ Force", "Coulomb Energy","Force Field"],
8
+ "classical_docking_ff_adjuvant" : ["Ligand", "Receptor", "Receptor id","Adjuvant CID","Adjuvant IsoSMILES", "Attractive", "Repulsive", "VDW LJ Force", "Coulomb Energy","Force Field"]
9
+ }
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+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
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+ "outputs": [
8
+ {
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+ "name": "stderr",
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+ "output_type": "stream",
11
+ "text": [
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+ "C:\\Users\\herutriana44\\AppData\\Local\\Temp\\ipykernel_7508\\4153759581.py:4: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed no earlier than 3 months after the release date. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
13
+ " from qiskit.algorithms.optimizers import COBYLA\n"
14
+ ]
15
+ }
16
+ ],
17
+ "source": [
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+ "\n",
19
+ "import pickle\n",
20
+ "from sklearn.metrics import accuracy_score, roc_auc_score\n",
21
+ "import pandas as pd\n",
22
+ "from qiskit.algorithms.optimizers import COBYLA\n",
23
+ "from qiskit.circuit.library import TwoLocal, ZZFeatureMap\n",
24
+ "from qiskit.utils import algorithm_globals\n",
25
+ "from qiskit_machine_learning.algorithms import VQC\n",
26
+ "from qiskit_machine_learning.datasets import ad_hoc_data\n",
27
+ "import numpy as np\n",
28
+ "from sklearn.preprocessing import label_binarize\n",
29
+ "from sklearn.model_selection import train_test_split\n",
30
+ "import os\n",
31
+ "import qiskit\n",
32
+ "from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Estimator, Session\n",
33
+ "\n",
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+ "algorithm_globals.random_seed = 42\n"
35
+ ]
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+ },
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+ {
38
+ "cell_type": "code",
39
+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
43
+ "IBM_API = \"d2f89283f9e773299d233cd7f60acf9bbe14813dbf8ce91a5e565c4836b1bce22b77879962c09def698596101c0b294f7300118d952c4ad85281f7d2e2931e7a\"\n",
44
+ "# provider = IBMProvider(token=IBM_API)\n",
45
+ "# backend = provider.get_backend(\"ibmq_qasm_simulator\")\n",
46
+ "# quantum_instance = qiskit.utils.QuantumInstance(backend, shots=1024)"
47
+ ]
48
+ },
49
+ {
50
+ "cell_type": "code",
51
+ "execution_count": 10,
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+ "metadata": {},
53
+ "outputs": [],
54
+ "source": [
55
+ "service = QiskitRuntimeService(token=IBM_API, channel=\"ibm_quantum\")\n",
56
+ "backend = service.backend(\"ibm_brisbane\")\n",
57
+ "estimator, sampler = Estimator(session=Session(service=service, backend=backend)), Sampler(session=Session(service=service, backend=backend))"
58
+ ]
59
+ },
60
+ {
61
+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
66
+ "template_name_model = \"allergen_vqc_model\"\n",
67
+ "vqc = VQC.load(template_name_model)\n",
68
+ "vqc.neural_network.sampler = sampler\n",
69
+ "# vqc = VQC(feature_map=vqc.feature_map, ansatz=vqc.ansatz, optimizer=vqc.optimizer, sampler=sampler).load(template_name_model)"
70
+ ]
71
+ },
72
+ {
73
+ "cell_type": "code",
74
+ "execution_count": 5,
75
+ "metadata": {},
76
+ "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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+ " »\n",
82
+ "q_0: »\n",
83
+ " »\n",
84
+ "q_1: »\n",
85
+ " »\n",
86
+ "« ┌──────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
87
+ "«q_0: ┤0 ├\n",
88
+ "« │ TwoLocal(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7],θ[8],θ[9],θ[10],θ[11],θ[12],θ[13],θ[14],θ[15]) │\n",
89
+ "«q_1: ┤1 ├\n",
90
+ "« └──────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
91
+ ]
92
+ }
93
+ ],
94
+ "source": [
95
+ "print(vqc.ansatz)"
96
+ ]
97
+ },
98
+ {
99
+ "cell_type": "code",
100
+ "execution_count": 6,
101
+ "metadata": {},
102
+ "outputs": [
103
+ {
104
+ "name": "stdout",
105
+ "output_type": "stream",
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+ "text": [
107
+ " ┌──────────────────────────┐\n",
108
+ "q_0: ┤0 ├\n",
109
+ " │ ZZFeatureMap(x[0],x[1]) │\n",
110
+ "q_1: ┤1 ├\n",
111
+ " └──────────────────────────┘\n"
112
+ ]
113
+ }
114
+ ],
115
+ "source": [
116
+ "print(vqc.feature_map)"
117
+ ]
118
+ },
119
+ {
120
+ "cell_type": "code",
121
+ "execution_count": 7,
122
+ "metadata": {},
123
+ "outputs": [
124
+ {
125
+ "name": "stdout",
126
+ "output_type": "stream",
127
+ "text": [
128
+ " ┌──────────────────────────┐»\n",
129
+ "q_0: ┤0 ├»\n",
130
+ " │ ZZFeatureMap(x[0],x[1]) │»\n",
131
+ "q_1: ┤1 ├»\n",
132
+ " └──────────────────────────┘»\n",
133
+ "« ┌──────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
134
+ "«q_0: ┤0 ├\n",
135
+ "« │ TwoLocal(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7],θ[8],θ[9],θ[10],θ[11],θ[12],θ[13],θ[14],θ[15]) │\n",
136
+ "«q_1: ┤1 ├\n",
137
+ "« └──────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
138
+ ]
139
+ }
140
+ ],
141
+ "source": [
142
+ "print(vqc.circuit)"
143
+ ]
144
+ },
145
+ {
146
+ "cell_type": "code",
147
+ "execution_count": 8,
148
+ "metadata": {},
149
+ "outputs": [
150
+ {
151
+ "data": {
152
+ "text/plain": [
153
+ "array(1)"
154
+ ]
155
+ },
156
+ "execution_count": 8,
157
+ "metadata": {},
158
+ "output_type": "execute_result"
159
+ }
160
+ ],
161
+ "source": [
162
+ "a = vqc.predict([[0.21412, 0.5125214124]])\n",
163
+ "a"
164
+ ]
165
+ },
166
+ {
167
+ "cell_type": "code",
168
+ "execution_count": 9,
169
+ "metadata": {},
170
+ "outputs": [
171
+ {
172
+ "ename": "NameError",
173
+ "evalue": "name 'circuit' is not defined",
174
+ "output_type": "error",
175
+ "traceback": [
176
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
177
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
178
+ "\u001b[1;32me:\\Users\\herutriana44\\Documents\\BioDataset\\ReVa(CLI)\\asset\\model\\Quantum Model\\test.ipynb Cell 9\u001b[0m line \u001b[0;36m3\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Users/herutriana44/Documents/BioDataset/ReVa%28CLI%29/asset/model/Quantum%20Model/test.ipynb#X11sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mwith\u001b[39;00m Session(service\u001b[39m=\u001b[39mservice, backend\u001b[39m=\u001b[39mbackend) \u001b[39mas\u001b[39;00m session:\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Users/herutriana44/Documents/BioDataset/ReVa%28CLI%29/asset/model/Quantum%20Model/test.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m estimator \u001b[39m=\u001b[39m Estimator(session\u001b[39m=\u001b[39msession)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/e%3A/Users/herutriana44/Documents/BioDataset/ReVa%28CLI%29/asset/model/Quantum%20Model/test.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m job \u001b[39m=\u001b[39m estimator\u001b[39m.\u001b[39mrun(circuit, observable)\n\u001b[0;32m <a href='vscode-notebook-cell:/e%3A/Users/herutriana44/Documents/BioDataset/ReVa%28CLI%29/asset/model/Quantum%20Model/test.ipynb#X11sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m result \u001b[39m=\u001b[39m job\u001b[39m.\u001b[39mresult()\n",
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+ "\u001b[1;31mNameError\u001b[0m: name 'circuit' is not defined"
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+ ]
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+ "with Session(service=service, backend=backend) as session:\n",
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+ " result = job.result()"
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+ import os
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+
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+ def process_path(input_path):
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+ # Mendapatkan direktori dan nama file dari path
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+ PyQt5
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manage.py ADDED
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+ for con in config:
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+ sys.path.append(con)
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+
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