First Commit

.gitattributes

@@ -1,35 +1,11 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
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-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+asset/data/b_receptor_v2.csv filter=lfs diff=lfs merge=lfs -text
+asset/data/t_receptor_v2.csv filter=lfs diff=lfs merge=lfs -text
+asset/model/allerginicity.h5 filter=lfs diff=lfs merge=lfs -text
+asset/model/antigenicity.h5 filter=lfs diff=lfs merge=lfs -text
+asset/model/BPepTree.joblib filter=lfs diff=lfs merge=lfs -text
+asset/model/BPepTree.pkl filter=lfs diff=lfs merge=lfs -text
+asset/model/TPepTree.joblib filter=lfs diff=lfs merge=lfs -text
+asset/model/TPepTree.pkl filter=lfs diff=lfs merge=lfs -text
+qiskit/_accelerate.pyd filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,2 @@
----
-license: mit
----
+# ReVa_AI_For_Vaccine_Design_In_Silico
+ 

ReVa2.spec

@@ -0,0 +1,50 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+block_cipher = None
+
+
+a = Analysis(
+    ['ReVa2', 'copy.spec'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=[],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='ReVa2',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='ReVa2',
+)

ReVa_version.rc

@@ -0,0 +1,6 @@
+1 VERSIONINFO
+FILEVERSION 0,0,0,1
+PRODUCTVERSION 0,0,0,1
+FILEDESCRIPTION "ReVa 0.0.0.1 Alpha Version"
+FILEOS VOS__WINDOWS32
+FILETYPE VFT_APP

asset/data/b_receptor_v2.csv

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+size 36424608

asset/data/t_receptor_v2.csv

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+size 78076738

asset/json/header.json

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+{
+    "res1" : ["Amino Acid", "Predictions", "Probabilities"],
+    "res2" : ["Peptide", "Label"],
+    "res3" : ["Peptide", "Allergenicity", "Toxin", "Antigenicity", "Hydrophobicity", "Kolaskar Antigenicity", "Tangonkar Antigenicity", "Emini Surface Accessibility", "Similarity"],
+    "res4" : ["Peptide", "Allergenicity", "Toxin", "Antigenicity"],
+    "physicochemical" : ["Peptide", "Instability", "Aliphatic", "GRAVY", "Extinction", "Half Life(Mamalia)", "Formula","C", "H", "N", "O", "S", "Theoretical pI", "mol weight"],
+    "classical_docking_ff" : ["Ligand", "Receptor", "Receptor id", "Attractive", "Repulsive", "VDW LJ Force", "Coulomb Energy","Force Field"],
+    "classical_docking_ff_adjuvant" : ["Ligand", "Receptor", "Receptor id","Adjuvant CID","Adjuvant IsoSMILES", "Attractive", "Repulsive", "VDW LJ Force", "Coulomb Energy","Force Field"]
+}

asset/label/BPepTree_label.json

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+{".": 0, "E": 1}

asset/label/BPepTree_label_quantum.json

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+{".": 0, "E": 1}

asset/label/TPepTree_label.json

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+{".": 0, "E": 1}

asset/label/TPepTree_label_quantum.json

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+{".": 0, "E": 1}

asset/label/allergenicity_label_mapping.json

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+{"allergens": 0, "non-allergens": 1}

asset/label/allergenicity_label_mapping_quantum.json

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+{"allergens": 0, "non-allergens": 1}

asset/label/antigenicity_label_mapping.json

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+{"antigen": 0, "non-antigen": 1}

asset/label/antigenicity_label_mapping_quantum.json

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+{"antigen": 0, "non-antigen": 1}

asset/label/toxin_label_mapping.json

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+{"positive": 0, "non-toxin": 1}

asset/label/toxin_label_mapping_quantum.json

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+{"positive": 0, "non-toxin": 1}

asset/model/BPepDL_label.json

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asset/model/BPepTree.joblib

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asset/model/BPepTree.pkl

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+size 44776949

asset/model/Quantum Model/test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "  from qiskit.algorithms.optimizers import COBYLA\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import pickle\n",
+    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
+    "import pandas as pd\n",
+    "from qiskit.algorithms.optimizers import COBYLA\n",
+    "from qiskit.circuit.library import TwoLocal, ZZFeatureMap\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit_machine_learning.algorithms import VQC\n",
+    "from qiskit_machine_learning.datasets import ad_hoc_data\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import os\n",
+    "import qiskit\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Estimator, Session\n",
+    "\n",
+    "algorithm_globals.random_seed = 42\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IBM_API = \"d2f89283f9e773299d233cd7f60acf9bbe14813dbf8ce91a5e565c4836b1bce22b77879962c09def698596101c0b294f7300118d952c4ad85281f7d2e2931e7a\"\n",
+    "# provider = IBMProvider(token=IBM_API)\n",
+    "# backend = provider.get_backend(\"ibmq_qasm_simulator\")\n",
+    "# quantum_instance = qiskit.utils.QuantumInstance(backend, shots=1024)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service = QiskitRuntimeService(token=IBM_API, channel=\"ibm_quantum\")\n",
+    "backend = service.backend(\"ibm_brisbane\")\n",
+    "estimator, sampler = Estimator(session=Session(service=service, backend=backend)), Sampler(session=Session(service=service, backend=backend))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_name_model = \"allergen_vqc_model\"\n",
+    "vqc = VQC.load(template_name_model)\n",
+    "vqc.neural_network.sampler = sampler\n",
+    "# vqc = VQC(feature_map=vqc.feature_map, ansatz=vqc.ansatz, optimizer=vqc.optimizer, sampler=sampler).load(template_name_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     »\n",
+      "q_0: »\n",
+      "     »\n",
+      "q_1: »\n",
+      "     »\n",
+      "«     ┌──────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
+      "«q_0: ┤0                                                                                                 ├\n",
+      "«     │  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",
+      "«q_1: ┤1                                                                                                 ├\n",
+      "«     └──────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vqc.ansatz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     ┌──────────────────────────┐\n",
+      "q_0: ┤0                         ├\n",
+      "     │  ZZFeatureMap(x[0],x[1]) │\n",
+      "q_1: ┤1                         ├\n",
+      "     └──────────────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vqc.feature_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     ┌──────────────────────────┐»\n",
+      "q_0: ┤0                         ├»\n",
+      "     │  ZZFeatureMap(x[0],x[1]) │»\n",
+      "q_1: ┤1                         ├»\n",
+      "     └──────────────────────────┘»\n",
+      "«     ┌──────────────────────────────────────────────────────────────────────────────────────────────────┐\n",
+      "«q_0: ┤0                                                                                                 ├\n",
+      "«     │  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",
+      "«q_1: ┤1                                                                                                 ├\n",
+      "«     └──────────────────────────────────────────────────────────────────────────────────────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vqc.circuit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(1)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = vqc.predict([[0.21412, 0.5125214124]])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'circuit' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\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",
+      "\u001b[1;31mNameError\u001b[0m: name 'circuit' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(session=session)\n",
+    "    job = estimator.run(circuit, observable)\n",
+    "    result = job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'qiskit-terra': '0.25.1', 'qiskit': '0.44.1', 'qiskit-aer': '0.12.2', 'qiskit-ignis': None, 'qiskit-ibmq-provider': '0.20.2', 'qiskit-nature': None, 'qiskit-finance': None, 'qiskit-optimization': None, 'qiskit-machine-learning': '0.6.1'}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qiskit.__qiskit_version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(a)"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

asset/model/TPepDL_label.json

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+{".": 0, "E": 1}

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asset/model/allerginicity.h5

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datas.py

@@ -0,0 +1,34 @@
+import os
+
+def process_path(input_path):
+    # Mendapatkan direktori dan nama file dari path
+    path_elements = input_path.split('\\')
+    directory = '\\'.join(path_elements[:-1])
+    filename = path_elements[-1]
+    if '.' in filename:
+        input_path = input_path.replace(f'\\{filename}','')
+        return input_path
+    else:
+        return input_path
+
+def generate_datas(direktori_input):
+    datas = []
+    for root, dirs, files in os.walk(direktori_input):
+        for file in files:
+            file_path = os.path.join(root, file)
+            relative_path = os.path.relpath(file_path, direktori_input)
+            datas.append((file_path, process_path(file_path)))
+
+    return datas
+    
+def write_to_file(datas, output_file):
+    with open(output_file, 'w') as file:
+        for data in datas:
+            file.write(f"('{data[0]}', '{data[1]}'),\n")
+            
+# Contoh penggunaan
+direktori_input = 'qiskit'
+output_file = 'qiskit.txt'
+
+result_datas = generate_datas(direktori_input)
+write_to_file(result_datas, output_file)

datas.txt

@@ -0,0 +1,76 @@
+('qiskit_machine_learning\exceptions.py', 'qiskit_machine_learning'),
+('qiskit_machine_learning\optionals.py', 'qiskit_machine_learning'),
+('qiskit_machine_learning\py.typed', 'qiskit_machine_learning'),
+('qiskit_machine_learning\version.py', 'qiskit_machine_learning'),
+('qiskit_machine_learning\VERSION.txt', 'qiskit_machine_learning'),
+('qiskit_machine_learning\__init__.py', 'qiskit_machine_learning'),
+('qiskit_machine_learning\algorithms\objective_functions.py', 'qiskit_machine_learning\algorithms'),
+('qiskit_machine_learning\algorithms\serializable_model.py', 'qiskit_machine_learning\algorithms'),
+('qiskit_machine_learning\algorithms\trainable_model.py', 'qiskit_machine_learning\algorithms'),
+('qiskit_machine_learning\algorithms\__init__.py', 'qiskit_machine_learning\algorithms'),
+('qiskit_machine_learning\algorithms\classifiers\neural_network_classifier.py', 'qiskit_machine_learning\algorithms\classifiers'),
+('qiskit_machine_learning\algorithms\classifiers\pegasos_qsvc.py', 'qiskit_machine_learning\algorithms\classifiers'),
+('qiskit_machine_learning\algorithms\classifiers\qsvc.py', 'qiskit_machine_learning\algorithms\classifiers'),
+('qiskit_machine_learning\algorithms\classifiers\vqc.py', 'qiskit_machine_learning\algorithms\classifiers'),
+('qiskit_machine_learning\algorithms\classifiers\__init__.py', 'qiskit_machine_learning\algorithms\classifiers'),
+('qiskit_machine_learning\algorithms\classifiers\__pycache__\neural_network_classifier.cpython-311.pyc', 'qiskit_machine_learning\algorithms\classifiers\__pycache__'),
+('qiskit_machine_learning\algorithms\classifiers\__pycache__\pegasos_qsvc.cpython-311.pyc', 'qiskit_machine_learning\algorithms\classifiers\__pycache__'),
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+('qiskit_machine_learning\circuit\library\qnn_circuit.py', 'qiskit_machine_learning\circuit\library'),
+('qiskit_machine_learning\circuit\library\raw_feature_vector.py', 'qiskit_machine_learning\circuit\library'),
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+('qiskit_machine_learning\connectors\torch_connector.py', 'qiskit_machine_learning\connectors'),
+('qiskit_machine_learning\connectors\__init__.py', 'qiskit_machine_learning\connectors'),
+('qiskit_machine_learning\datasets\ad_hoc.py', 'qiskit_machine_learning\datasets'),
+('qiskit_machine_learning\datasets\__init__.py', 'qiskit_machine_learning\datasets'),
+('qiskit_machine_learning\kernels\base_kernel.py', 'qiskit_machine_learning\kernels'),
+('qiskit_machine_learning\kernels\fidelity_quantum_kernel.py', 'qiskit_machine_learning\kernels'),
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+('qiskit_machine_learning\kernels\__init__.py', 'qiskit_machine_learning\kernels'),
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+('qiskit_machine_learning\neural_networks\neural_network.py', 'qiskit_machine_learning\neural_networks'),
+('qiskit_machine_learning\neural_networks\sampler_qnn.py', 'qiskit_machine_learning\neural_networks'),
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+('qiskit_machine_learning\utils\adjust_num_qubits.py', 'qiskit_machine_learning\utils'),
+('qiskit_machine_learning\utils\__init__.py', 'qiskit_machine_learning\utils'),
+('qiskit_machine_learning\utils\loss_functions\kernel_loss_functions.py', 'qiskit_machine_learning\utils\loss_functions'),
+('qiskit_machine_learning\utils\loss_functions\loss_functions.py', 'qiskit_machine_learning\utils\loss_functions'),
+('qiskit_machine_learning\utils\loss_functions\__init__.py', 'qiskit_machine_learning\utils\loss_functions'),
+('qiskit_machine_learning\utils\loss_functions\__pycache__\kernel_loss_functions.cpython-311.pyc', 'qiskit_machine_learning\utils\loss_functions\__pycache__'),
+('qiskit_machine_learning\utils\loss_functions\__pycache__\loss_functions.cpython-311.pyc', 'qiskit_machine_learning\utils\loss_functions\__pycache__'),
+('qiskit_machine_learning\utils\loss_functions\__pycache__\__init__.cpython-311.pyc', 'qiskit_machine_learning\utils\loss_functions\__pycache__'),
+('qiskit_machine_learning\utils\__pycache__\adjust_num_qubits.cpython-311.pyc', 'qiskit_machine_learning\utils\__pycache__'),
+('qiskit_machine_learning\utils\__pycache__\__init__.cpython-311.pyc', 'qiskit_machine_learning\utils\__pycache__'),

library.txt

@@ -0,0 +1,14 @@
+PyQt5
+tensorflow
+numpy
+scikit-learn==1.2.2
+openpyxl
+torch
+pillow
+qiskit-machine-learning
+qiskit
+scipy
+rdkit
+biopython
+PyQt5
+pandas

manage.py

@@ -0,0 +1,10 @@
+import importlib
+import sys
+config = ["config", "controller", "view", "model"]
+for con in config:
+    sys.path.append(con)
+
+base_func = importlib.import_module("base_func")
+module = importlib.import_module("library")
+
+module.os.path.dirname(module.os.path.abspath(__file__))