ddi-predictor-nb.ipynb

{"cells":[{"cell_type":"code","execution_count":8,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-09-10T06:06:35.405681Z","iopub.status.busy":"2023-09-10T06:06:35.405322Z","iopub.status.idle":"2023-09-10T06:06:35.414946Z","shell.execute_reply":"2023-09-10T06:06:35.413955Z","shell.execute_reply.started":"2023-09-10T06:06:35.405651Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/ddi-dataset/Neuron_input.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T06:23:52.957496Z","iopub.status.busy":"2023-09-10T06:23:52.957118Z","iopub.status.idle":"2023-09-10T06:57:51.446890Z","shell.execute_reply":"2023-09-10T06:57:51.445863Z","shell.execute_reply.started":"2023-09-10T06:23:52.957464Z"},"trusted":true},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from sklearn.model_selection import train_test_split\n","\n","# Load your dataset and preprocess as needed\n","drugpairs = pd.read_csv(\"Neuron_input.csv\")\n","\n","# Replace output 86 with 0 to align with class indexing from 0 to 85\n","drugpairs['Y'] = drugpairs['Y'].replace(86, 0)\n","\n","# Extract features and labels\n","features = drugpairs.drop(['Drug1_ID', 'Drug1', 'Drug2_ID', 'Drug2', 'Y'], axis=1)\n","X = np.array(features)\n","y = drugpairs['Y']\n","\n","# Normalize the features\n","X = tf.keras.utils.normalize(X, axis=1)\n","\n","# Split the dataset into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,, random_state=42)\n","\n","# Build the neural network model (Model 1)\n","model = keras.Sequential([\n","    keras.layers.Dense(100, input_shape=(X_train.shape[1],), activation='relu'),\n","    keras.layers.Dense(100, activation='relu'),\n","    keras.layers.Dense(100, activation='tanh'),\n","    keras.layers.Dropout(0.2),\n","    keras.layers.Dense(100, activation='relu'),\n","    keras.layers.Dense(100, activation='tanh'),\n","    keras.layers.Dropout(0.2),\n","    keras.layers.Dense(86, activation='softmax')  # Output layer with 86 neurons (class labels)\n","])\n","\n","# Compile the model\n","model.compile(\n","    optimizer='adam',\n","    loss='sparse_categorical_crossentropy',\n","    metrics=['accuracy']\n",")\n","\n","# Train the model\n","history = model.fit(\n","    X_train, y_train,\n","    epochs=100,\n","    validation_data=(X_test, y_test)\n",")\n","\n","# Evaluate the model on the test set\n","test_loss, test_accuracy = model.evaluate(X_test, y_test)\n","print(f\"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T08:03:16.832987Z","iopub.status.busy":"2023-09-10T08:03:16.832594Z","iopub.status.idle":"2023-09-10T08:03:45.771839Z","shell.execute_reply":"2023-09-10T08:03:45.770432Z","shell.execute_reply.started":"2023-09-10T08:03:16.832956Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting rdkit-pypi\n","  Downloading rdkit_pypi-2022.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.4 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m36.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rdkit-pypi) (1.23.5)\n","Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from rdkit-pypi) (9.5.0)\n","Installing collected packages: rdkit-pypi\n","Successfully installed rdkit-pypi-2022.9.5\n","Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.23.5)\n"]}],"source":["!pip install rdkit-pypi\n","!pip install numpy\n"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T09:38:21.900931Z","iopub.status.busy":"2023-09-10T09:38:21.900585Z","iopub.status.idle":"2023-09-10T09:38:38.056852Z","shell.execute_reply":"2023-09-10T09:38:38.055379Z","shell.execute_reply.started":"2023-09-10T09:38:21.900903Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting rdkit\n","  Downloading rdkit-2023.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rdkit) (1.23.5)\n","Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from rdkit) (9.5.0)\n","Installing collected packages: rdkit\n","Successfully installed rdkit-2023.3.3\n"]}],"source":["!pip install rdkit"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T10:05:38.819506Z","iopub.status.busy":"2023-09-10T10:05:38.819103Z","iopub.status.idle":"2023-09-10T10:05:54.008231Z","shell.execute_reply":"2023-09-10T10:05:54.006983Z","shell.execute_reply.started":"2023-09-10T10:05:38.819467Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting pubchempy\n","  Downloading PubChemPy-1.0.4.tar.gz (29 kB)\n","  Preparing metadata (setup.py) ... \u001b[?25ldone\n","\u001b[?25hBuilding wheels for collected packages: pubchempy\n","  Building wheel for pubchempy (setup.py) ... \u001b[?25ldone\n","\u001b[?25h  Created wheel for pubchempy: filename=PubChemPy-1.0.4-py3-none-any.whl size=13820 sha256=495c83ce5277b30b415de47766f88cbf6bdcd1951f02eda4f11a2631dae795a5\n","  Stored in directory: /root/.cache/pip/wheels/90/7c/45/18a0671e3c3316966ef7ed9ad2b3f3300a7e41d3421a44e799\n","Successfully built pubchempy\n","Installing collected packages: pubchempy\n","Successfully installed pubchempy-1.0.4\n"]}],"source":["! pip install pubchempy"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T09:59:50.160663Z","iopub.status.busy":"2023-09-10T09:59:50.160271Z","iopub.status.idle":"2023-09-10T09:59:50.450056Z","shell.execute_reply":"2023-09-10T09:59:50.449111Z","shell.execute_reply.started":"2023-09-10T09:59:50.160630Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 76ms/step\n","Predicted Class Label: 60\n","Predicted Class Label: 60\n","Description: Drug1 may increase the hyponatremic activities of Drug2.\n"]}],"source":["import numpy as np\n","from tensorflow import keras\n","from rdkit import Chem\n","from rdkit.Chem import DataStructs\n","from rdkit.Chem.Fingerprints import FingerprintMols\n","\n","# Define a function to compute the Tanimoto similarity between two molecules\n","def compute_similarity(smiles1, smiles2):\n","    mol1 = Chem.MolFromSmiles(smiles1)\n","    mol2 = Chem.MolFromSmiles(smiles2)\n","\n","    if mol1 is None or mol2 is None:\n","        return 0.0  # Handle invalid input\n","\n","    fp1 = FingerprintMols.FingerprintMol(mol1)\n","    fp2 = FingerprintMols.FingerprintMol(mol2)\n","    \n","    return DataStructs.TanimotoSimilarity(fp1, fp2)\n","\n","# Example drug SMILES notations\n","drug1_smiles = r'CN(C)CCOC(C)(C1=CC=CC=C1)C1=CC=CC=N1'\n","drug2_smiles = r'CC(N)CC1=CC=CC=C1'\n","\n","# Compute the structural similarity score between the drug pair\n","similarity_score = compute_similarity(drug1_smiles, drug2_smiles)\n","\n","# Create an SSP feature vector (100 components as per your description)\n","ssp_feature_vector = np.array([similarity_score] * 100)\n","\n","# Load the saved model\n","loaded_model = keras.models.load_model(\"/kaggle/input/ddi-model/ddi_model.h5\")\n","\n","# Prepare input data for prediction (reshape the feature vector)\n","input_data = ssp_feature_vector.reshape(1, -1)\n","\n","# Make predictions using the loaded model\n","predictions = loaded_model.predict(input_data)\n","\n","# Find the class label with the highest probability\n","predicted_class = np.argmax(predictions)\n","\n","# Output the predicted class label\n","print(\"Predicted Class Label:\", predicted_class)\n","\n","# Dictionary mapping predicted class labels to descriptions\n","class_description_mapping = {\n","    1: \"Drug1 can cause an increase in the absorption of Drug2 resulting in an increased serum concentration and potentially a worsening of adverse effects.\",\n","    2: \"Drug1 can cause a decrease in the absorption of Drug2 resulting in a reduced serum concentration and potentially a decrease in efficacy.\",\n","    3: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","    4: \"The bioavailability of Drug2 can be decreased when combined with Drug1.\",\n","    5: \"Drug1 may decrease the vasoconstricting activities of Drug2.\",\n","    6: \"Drug1 may increase the anticoagulant activities of Drug2.\",\n","    7: \"Drug1 may increase the ototoxic activities of Drug2.\",\n","    8: \"The therapeutic efficacy of Drug2 can be increased when used in combination with Drug1.\",\n","    9: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","    10: \"The risk or severity of hypertension can be increased when Drug2 is combined with Drug1.\",\n","    11: \"The serum concentration of the active metabolites of Drug2 can be reduced when Drug2 is used in combination with Drug1 resulting in a loss in efficacy.\",\n","    12: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","    13: \"The absorption of Drug2 can be decreased when combined with Drug1.\",\n","    14: \"Drug1 may decrease the bronchodilatory activities of Drug2.\",\n","    15: \"Drug1 may decrease the bioavailability of Drug2.\",\n","    16: \"Drug1 may increase the central neurotoxic activities of Drug2.\",\n","    17: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","    18: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","    19: \"Drug1 may increase the vasoconstricting activities of Drug2.\",\n","    20: \"Drug1 may increase the QTc-prolonging activities of Drug2.\",\n","    21: \"Drug1 may increase the neuromuscular blocking activities of Drug2.\",\n","    22: \"Drug1 may increase the adverse neuromuscular activities of Drug2.\",\n","    23: \"Drug1 may increase the stimulatory activities of Drug2.\",\n","    24: \"Drug1 may decrease effectiveness of Drug2 as a diagnostic agent.\",\n","    25: \"Drug1 may increase the atrioventricular blocking (AV block) activities of Drug2.\",\n","    26: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","    27: \"Drug1 may increase the neuroexcitatory activities of Drug2.\",\n","    28: \"Drug1 may increase the dermatologic adverse activities of Drug2.\",\n","    29: \"Drug1 may decrease the diuretic activities of Drug2.\",\n","    30: \"Drug1 may increase the orthostatic hypotensive activities of Drug2.\",\n","    31: \"The risk or severity of hypertension can be increased when Drug1 is combined with Drug2.\",\n","    32: \"The risk or severity of QTc prolongation can be increased when Drug1 is combined with Drug2.\",\n","    33: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","    34: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","    35: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","    36: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","    37: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","    38: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","    39: \"Drug1 may increase the constipating activities of Drug2.\",\n","    40: \"Drug1 may increase the respiratory depressant activities of Drug2.\",\n","    41: \"Drug1 may increase the hypotensive and central nervous system depressant (CNS depressant) activities of Drug2.\",\n","    42: \"The risk or severity of hyperkalemia can be increased when Drug1 is combined with Drug2.\",\n","    43: \"The protein binding of Drug2 can be decreased when combined with Drug1.\",\n","    44: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","    45: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","    46: \"Drug1 may increase the bronchoconstrictory activities of Drug2.\",\n","    47: \"The metabolism of Drug2 can be decreased when combined with Drug1.\",\n","    48: \"Drug1 may increase the myopathic rhabdomyolysis activities of Drug2.\",\n","    49: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","    50: \"The risk or severity of heart failure can be increased when Drug2 is combined with Drug1.\",\n","    51: \"Drug1 may increase the hypercalcemic activities of Drug2.\",\n","    52: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","    53: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","    54: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","    55: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","    56: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","    57: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","    58: \"Drug1 may decrease the cardiotoxic activities of Drug2.\",\n","    59: \"Drug1 may increase the ulcerogenic activities of Drug2.\",\n","    60: \"Drug1 may increase the hyponatremic activities of Drug2.\",\n","    61: \"Drug1 may decrease the sedative activities of Drug2.\",\n","    62: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","    63: \"Drug1 may increase the myelosuppressive activities of Drug2.\",\n","    64: \"Drug1 may increase the hypoglycemic activities of Drug2.\",\n","    65: \"Drug1 may increase the excretion rate of Drug2 which could result in a lower serum level and potentially a reduction in efficacy.\",\n","    66: \"The risk or severity of bleeding can be increased when Drug1 is combined with Drug2.\",\n","    67: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","    68: \"Drug1 may increase the analgesic activities of Drug2.\",\n","    69: \"Drug1 may increase the analgesic activities of Drug2.\",\n","    70: \"The therapeutic efficacy of Drug2 can be decreased when used in combination with Drug1.\",\n","    71: \"Drug1 may increase the hypertensive activities of Drug2.\",\n","    72: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","    73: \"The serum concentration of Drug2 can be increased when it is combined with Drug1.\",\n","    74: \"Drug1 may increase the fluid retaining activities of Drug2.\",\n","    75: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","    76: \"Drug1 may decrease the sedative activities of Drug2.\",\n","    77: \"The serum concentration of the active metabolites of Drug2 can be increased when Drug2 is used in combination with Drug1.\",\n","    78: \"Drug1 may increase the hyperglycemic activities of Drug2.\",\n","    79: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","    80: \"Drug1 may increase the hepatotoxic activities of Drug2.\",\n","    81: \"Drug1 may increase the thrombogenic activities of Drug2.\",\n","    82: \"Drug1 may increase the arrhythmogenic activities of Drug2.\",\n","    83: \"Drug1 may increase the vasopressor activities of Drug2.\",\n","    84: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","    85: \"Drug1 may increase the tachycardic activities of Drug2.\",\n","    86: \"The risk of a hypersensitivity reaction to Drug2 is increased when it is combined with Drug1.\"\n","}\n","\n","# Check if the predicted class label exists in the mapping dictionary\n","if predicted_class in class_description_mapping:\n","    description = class_description_mapping[predicted_class]\n","    print(\"Predicted Class Label:\", predicted_class)\n","    print(\"Description:\", description)\n","else:\n","    print(\"Predicted Class Label not found in the mapping.\")\n","\n","# Output will display the predicted class label and its description\n"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T07:52:09.954377Z","iopub.status.busy":"2023-09-10T07:52:09.953993Z","iopub.status.idle":"2023-09-10T07:53:17.362726Z","shell.execute_reply":"2023-09-10T07:53:17.360900Z","shell.execute_reply.started":"2023-09-10T07:52:09.954346Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["  adding: kaggle/working/ (stored 0%)\n","  adding: kaggle/working/.virtual_documents/ (stored 0%)\n","  adding: kaggle/working/random_forest_model.joblib (deflated 97%)\n","  adding: kaggle/working/ddi_model.h5 (deflated 13%)\n","  adding: kaggle/working/Drug_Drug_Interaction/ (stored 0%)\n","  adding: kaggle/working/Drug_Drug_Interaction/DDI_Tuning/ (stored 0%)\n","  adding: kaggle/working/Drug_Drug_Interaction/DDI_Tuning/trial_4/ (stored 0%)\n","  adding: 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/kaggle/working"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T07:53:22.901110Z","iopub.status.busy":"2023-09-10T07:53:22.900270Z","iopub.status.idle":"2023-09-10T07:53:24.362968Z","shell.execute_reply":"2023-09-10T07:53:24.361636Z","shell.execute_reply.started":"2023-09-10T07:53:22.901059Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Drug_Drug_Interaction  ddi_model.h5  file.zip  random_forest_model.joblib\n"]}],"source":["!ls"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T07:53:28.167781Z","iopub.status.busy":"2023-09-10T07:53:28.166715Z","iopub.status.idle":"2023-09-10T07:53:28.175288Z","shell.execute_reply":"2023-09-10T07:53:28.174180Z","shell.execute_reply.started":"2023-09-10T07:53:28.167742Z"},"trusted":true},"outputs":[{"data":{"text/html":["<a href='file.zip' target='_blank'>file.zip</a><br>"],"text/plain":["/kaggle/working/file.zip"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["from IPython.display import FileLink\n","FileLink(r'file.zip')"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T10:09:26.088331Z","iopub.status.busy":"2023-09-10T10:09:26.087809Z","iopub.status.idle":"2023-09-10T10:09:26.999191Z","shell.execute_reply":"2023-09-10T10:09:26.998167Z","shell.execute_reply.started":"2023-09-10T10:09:26.088285Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 128ms/step\n","Predicted Class Label: 60\n","Description: Drug1 may increase the hyponatremic activities of Drug2.\n"]}],"source":["import numpy as np\n","from tensorflow import keras\n","from rdkit import Chem\n","from rdkit.Chem import DataStructs\n","from rdkit.Chem.Fingerprints import FingerprintMols\n","import pubchempy as pcp\n","\n","# Define a function to compute the Tanimoto similarity between two molecules\n","def compute_similarity(smiles1, smiles2):\n","    mol1 = Chem.MolFromSmiles(smiles1)\n","    mol2 = Chem.MolFromSmiles(smiles2)\n","\n","    if mol1 is None or mol2 is None:\n","        return 0.0  # Handle invalid input\n","\n","    fp1 = FingerprintMols.FingerprintMol(mol1)\n","    fp2 = FingerprintMols.FingerprintMol(mol2)\n","    \n","    return DataStructs.TanimotoSimilarity(fp1, fp2)\n","\n","# Define a function to get SMILES notation for a drug name\n","def get_smiles_for_drug(drug_name):\n","    try:\n","        # Search PubChem for the drug by name\n","        compounds = pcp.get_compounds(drug_name, 'name')\n","        \n","        if compounds:\n","            # Take the first result (you may want to handle multiple results differently)\n","            compound = compounds[0]\n","            \n","            # Check if SMILES notation is available\n","            if compound.isomeric_smiles:\n","                return compound.isomeric_smiles\n","            else:\n","                return \"SMILES notation not found for this drug.\"\n","        else:\n","            return \"Drug not found in PubChem database.\"\n","    \n","    except Exception as e:\n","        return str(e)\n","\n","# Function to predict DDI for a pair of drugs\n","def predict_ddi_for_drugs(drug1_name, drug2_name, model_path):\n","    # Get SMILES notations for the drugs\n","    drug1_smiles = get_smiles_for_drug(drug1_name)\n","    drug2_smiles = get_smiles_for_drug(drug2_name)\n","    \n","    if \"SMILES notation not found\" in (drug1_smiles, drug2_smiles):\n","        return \"SMILES notation not found for one or both drugs.\"\n","    \n","    # Compute the structural similarity score between the drug pair\n","    similarity_score = compute_similarity(drug1_smiles, drug2_smiles)\n","\n","    # Create an SSP feature vector (100 components as per your description)\n","    ssp_feature_vector = np.array([similarity_score] * 100)\n","\n","    # Load the saved model\n","    loaded_model = keras.models.load_model(model_path)\n","\n","    # Prepare input data for prediction (reshape the feature vector)\n","    input_data = ssp_feature_vector.reshape(1, -1)\n","\n","    # Make predictions using the loaded model\n","    predictions = loaded_model.predict(input_data)\n","\n","    # Find the class label with the highest probability\n","    predicted_class = np.argmax(predictions)\n","\n","    # Dictionary mapping predicted class labels to descriptions\n","    class_description_mapping = {\n","        1: \"Drug1 can cause an increase in the absorption of Drug2 resulting in an increased serum concentration and potentially a worsening of adverse effects.\",\n","        2: \"Drug1 can cause a decrease in the absorption of Drug2 resulting in a reduced serum concentration and potentially a decrease in efficacy.\",\n","        3: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        4: \"The bioavailability of Drug2 can be decreased when combined with Drug1.\",\n","        5: \"Drug1 may decrease the vasoconstricting activities of Drug2.\",\n","        6: \"Drug1 may increase the anticoagulant activities of Drug2.\",\n","        7: \"Drug1 may increase the ototoxic activities of Drug2.\",\n","        8: \"The therapeutic efficacy of Drug2 can be increased when used in combination with Drug1.\",\n","        9: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        10: \"The risk or severity of hypertension can be increased when Drug2 is combined with Drug1.\",\n","        11: \"The serum concentration of the active metabolites of Drug2 can be reduced when Drug2 is used in combination with Drug1 resulting in a loss in efficacy.\",\n","        12: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        13: \"The absorption of Drug2 can be decreased when combined with Drug1.\",\n","        14: \"Drug1 may decrease the bronchodilatory activities of Drug2.\",\n","        15: \"Drug1 may decrease the bioavailability of Drug2.\",\n","        16: \"Drug1 may increase the central neurotoxic activities of Drug2.\",\n","        17: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        18: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        19: \"Drug1 may increase the vasoconstricting activities of Drug2.\",\n","        20: \"Drug1 may increase the QTc-prolonging activities of Drug2.\",\n","        21: \"Drug1 may increase the neuromuscular blocking activities of Drug2.\",\n","        22: \"Drug1 may increase the adverse neuromuscular activities of Drug2.\",\n","        23: \"Drug1 may increase the stimulatory activities of Drug2.\",\n","        24: \"Drug1 may decrease effectiveness of Drug2 as a diagnostic agent.\",\n","        25: \"Drug1 may increase the atrioventricular blocking (AV block) activities of Drug2.\",\n","        26: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        27: \"Drug1 may increase the neuroexcitatory activities of Drug2.\",\n","        28: \"Drug1 may increase the dermatologic adverse activities of Drug2.\",\n","        29: \"Drug1 may decrease the diuretic activities of Drug2.\",\n","        30: \"Drug1 may increase the orthostatic hypotensive activities of Drug2.\",\n","        31: \"The risk or severity of hypertension can be increased when Drug1 is combined with Drug2.\",\n","        32: \"The risk or severity of QTc prolongation can be increased when Drug1 is combined with Drug2.\",\n","        33: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        34: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        35: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        36: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        37: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        38: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        39: \"Drug1 may increase the constipating activities of Drug2.\",\n","        40: \"Drug1 may increase the respiratory depressant activities of Drug2.\",\n","        41: \"Drug1 may increase the hypotensive and central nervous system depressant (CNS depressant) activities of Drug2.\",\n","        42: \"The risk or severity of hyperkalemia can be increased when Drug1 is combined with Drug2.\",\n","        43: \"The protein binding of Drug2 can be decreased when combined with Drug1.\",\n","        44: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        45: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        46: \"Drug1 may increase the bronchoconstrictory activities of Drug2.\",\n","        47: \"The metabolism of Drug2 can be decreased when combined with Drug1.\",\n","        48: \"Drug1 may increase the myopathic rhabdomyolysis activities of Drug2.\",\n","        49: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        50: \"The risk or severity of heart failure can be increased when Drug2 is combined with Drug1.\",\n","        51: \"Drug1 may increase the hypercalcemic activities of Drug2.\",\n","        52: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        53: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        54: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        55: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        56: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        57: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        58: \"Drug1 may decrease the cardiotoxic activities of Drug2.\",\n","        59: \"Drug1 may increase the ulcerogenic activities of Drug2.\",\n","        60: \"Drug1 may increase the hyponatremic activities of Drug2.\",\n","        61: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        62: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        63: \"Drug1 may increase the myelosuppressive activities of Drug2.\",\n","        64: \"Drug1 may increase the hypoglycemic activities of Drug2.\",\n","        65: \"Drug1 may increase the excretion rate of Drug2 which could result in a lower serum level and potentially a reduction in efficacy.\",\n","        66: \"The risk or severity of bleeding can be increased when Drug1 is combined with Drug2.\",\n","        67: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        68: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        69: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        70: \"The therapeutic efficacy of Drug2 can be decreased when used in combination with Drug1.\",\n","        71: \"Drug1 may increase the hypertensive activities of Drug2.\",\n","        72: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        73: \"The serum concentration of Drug2 can be increased when it is combined with Drug1.\",\n","        74: \"Drug1 may increase the fluid retaining activities of Drug2.\",\n","        75: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        76: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        77: \"The serum concentration of the active metabolites of Drug2 can be increased when Drug2 is used in combination with Drug1.\",\n","        78: \"Drug1 may increase the hyperglycemic activities of Drug2.\",\n","        79: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        80: \"Drug1 may increase the hepatotoxic activities of Drug2.\",\n","        81: \"Drug1 may increase the thrombogenic activities of Drug2.\",\n","        82: \"Drug1 may increase the arrhythmogenic activities of Drug2.\",\n","        83: \"Drug1 may increase the vasopressor activities of Drug2.\",\n","        84: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        85: \"Drug1 may increase the tachycardic activities of Drug2.\",\n","        86: \"The risk of a hypersensitivity reaction to Drug2 is increased when it is combined with Drug1.\"\n","    }\n","\n","    # Check if the predicted class label exists in the mapping dictionary\n","    if predicted_class in class_description_mapping:\n","        description = class_description_mapping[predicted_class]\n","    else:\n","        description = \"Predicted Class Label not found in the mapping.\"\n","\n","    return f\"Predicted Class Label: {predicted_class}\\nDescription: {description}\"\n","\n","# Example drug names\n","drug1_name = \"Aspirin\"\n","drug2_name = \"Ibuprofen\"\n","\n","# Path to the saved model\n","model_path = \"/kaggle/input/ddi-model/ddi_model.h5\"\n","\n","# Make predictions for Drug-Drug Interaction\n","result = predict_ddi_for_drugs(drug1_name, drug2_name, model_path)\n","\n","# Output the result\n","print(result)\n"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T10:14:28.754045Z","iopub.status.busy":"2023-09-10T10:14:28.753598Z","iopub.status.idle":"2023-09-10T10:14:29.876844Z","shell.execute_reply":"2023-09-10T10:14:29.875908Z","shell.execute_reply.started":"2023-09-10T10:14:28.754011Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 75ms/step\n","Drug 1: Aspirin (SMILES: CC(=O)OC1=CC=CC=C1C(=O)O, Molecular Formula: C9H8O4)\n","Drug 2: Ibuprofen (SMILES: CC(C)CC1=CC=C(C=C1)C(C)C(=O)O, Molecular Formula: C13H18O2)\n","Predicted Class Label: 60\n","Description: Drug1 may increase the hyponatremic activities of Drug2.\n"]},{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","from tensorflow import keras\n","from rdkit import Chem\n","from rdkit.Chem import DataStructs\n","from rdkit.Chem.Fingerprints import FingerprintMols\n","from rdkit.Chem import Draw\n","import pubchempy as pcp\n","import matplotlib.pyplot as plt\n","\n","# Define a function to compute the Tanimoto similarity between two molecules\n","def compute_similarity(smiles1, smiles2):\n","    mol1 = Chem.MolFromSmiles(smiles1)\n","    mol2 = Chem.MolFromSmiles(smiles2)\n","\n","    if mol1 is None or mol2 is None:\n","        return 0.0  # Handle invalid input\n","\n","    fp1 = FingerprintMols.FingerprintMol(mol1)\n","    fp2 = FingerprintMols.FingerprintMol(mol2)\n","    \n","    return DataStructs.TanimotoSimilarity(fp1, fp2)\n","\n","# Define a function to get SMILES notation for a drug name\n","def get_smiles_for_drug(drug_name):\n","    try:\n","        # Search PubChem for the drug by name\n","        compounds = pcp.get_compounds(drug_name, 'name')\n","        \n","        if compounds:\n","            # Take the first result (you may want to handle multiple results differently)\n","            compound = compounds[0]\n","            \n","            # Check if SMILES notation is available\n","            if compound.isomeric_smiles:\n","                return compound.isomeric_smiles, compound.molecular_formula\n","            else:\n","                return \"SMILES notation not found for this drug.\", None\n","        else:\n","            return \"Drug not found in PubChem database.\", None\n","    \n","    except Exception as e:\n","        return str(e), None\n","\n","# Function to predict DDI for a pair of drugs\n","def predict_ddi_for_drugs(drug1_name, drug2_name, model_path):\n","    # Get SMILES notations and molecular formulas for the drugs\n","    drug1_smiles, drug1_formula = get_smiles_for_drug(drug1_name)\n","    drug2_smiles, drug2_formula = get_smiles_for_drug(drug2_name)\n","    \n","    if \"SMILES notation not found\" in (drug1_smiles, drug2_smiles):\n","        return \"SMILES notation not found for one or both drugs.\", None, None\n","    \n","    # Compute the structural similarity score between the drug pair\n","    similarity_score = compute_similarity(drug1_smiles, drug2_smiles)\n","\n","    # Create an SSP feature vector (100 components as per your description)\n","    ssp_feature_vector = np.array([similarity_score] * 100)\n","\n","    # Load the saved model\n","    loaded_model = keras.models.load_model(model_path)\n","\n","    # Prepare input data for prediction (reshape the feature vector)\n","    input_data = ssp_feature_vector.reshape(1, -1)\n","\n","    # Make predictions using the loaded model\n","    predictions = loaded_model.predict(input_data)\n","\n","    # Find the class label with the highest probability\n","    predicted_class = np.argmax(predictions)\n","\n","    # Dictionary mapping predicted class labels to descriptions\n","    class_description_mapping = {\n","        1: \"Drug1 can cause an increase in the absorption of Drug2 resulting in an increased serum concentration and potentially a worsening of adverse effects.\",\n","        2: \"Drug1 can cause a decrease in the absorption of Drug2 resulting in a reduced serum concentration and potentially a decrease in efficacy.\",\n","        3: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        4: \"The bioavailability of Drug2 can be decreased when combined with Drug1.\",\n","        5: \"Drug1 may decrease the vasoconstricting activities of Drug2.\",\n","        6: \"Drug1 may increase the anticoagulant activities of Drug2.\",\n","        7: \"Drug1 may increase the ototoxic activities of Drug2.\",\n","        8: \"The therapeutic efficacy of Drug2 can be increased when used in combination with Drug1.\",\n","        9: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        10: \"The risk or severity of hypertension can be increased when Drug2 is combined with Drug1.\",\n","        11: \"The serum concentration of the active metabolites of Drug2 can be reduced when Drug2 is used in combination with Drug1 resulting in a loss in efficacy.\",\n","        12: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        13: \"The absorption of Drug2 can be decreased when combined with Drug1.\",\n","        14: \"Drug1 may decrease the bronchodilatory activities of Drug2.\",\n","        15: \"Drug1 may decrease the bioavailability of Drug2.\",\n","        16: \"Drug1 may increase the central neurotoxic activities of Drug2.\",\n","        17: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        18: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        19: \"Drug1 may increase the vasoconstricting activities of Drug2.\",\n","        20: \"Drug1 may increase the QTc-prolonging activities of Drug2.\",\n","        21: \"Drug1 may increase the neuromuscular blocking activities of Drug2.\",\n","        22: \"Drug1 may increase the adverse neuromuscular activities of Drug2.\",\n","        23: \"Drug1 may increase the stimulatory activities of Drug2.\",\n","        24: \"Drug1 may decrease effectiveness of Drug2 as a diagnostic agent.\",\n","        25: \"Drug1 may increase the atrioventricular blocking (AV block) activities of Drug2.\",\n","        26: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        27: \"Drug1 may increase the neuroexcitatory activities of Drug2.\",\n","        28: \"Drug1 may increase the dermatologic adverse activities of Drug2.\",\n","        29: \"Drug1 may decrease the diuretic activities of Drug2.\",\n","        30: \"Drug1 may increase the orthostatic hypotensive activities of Drug2.\",\n","        31: \"The risk or severity of hypertension can be increased when Drug1 is combined with Drug2.\",\n","        32: \"The risk or severity of QTc prolongation can be increased when Drug1 is combined with Drug2.\",\n","        33: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        34: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        35: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        36: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        37: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        38: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        39: \"Drug1 may increase the constipating activities of Drug2.\",\n","        40: \"Drug1 may increase the respiratory depressant activities of Drug2.\",\n","        41: \"Drug1 may increase the hypotensive and central nervous system depressant (CNS depressant) activities of Drug2.\",\n","        42: \"The risk or severity of hyperkalemia can be increased when Drug1 is combined with Drug2.\",\n","        43: \"The protein binding of Drug2 can be decreased when combined with Drug1.\",\n","        44: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        45: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        46: \"Drug1 may increase the bronchoconstrictory activities of Drug2.\",\n","        47: \"The metabolism of Drug2 can be decreased when combined with Drug1.\",\n","        48: \"Drug1 may increase the myopathic rhabdomyolysis activities of Drug2.\",\n","        49: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        50: \"The risk or severity of heart failure can be increased when Drug2 is combined with Drug1.\",\n","        51: \"Drug1 may increase the hypercalcemic activities of Drug2.\",\n","        52: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        53: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        54: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        55: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        56: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        57: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        58: \"Drug1 may decrease the cardiotoxic activities of Drug2.\",\n","        59: \"Drug1 may increase the ulcerogenic activities of Drug2.\",\n","        60: \"Drug1 may increase the hyponatremic activities of Drug2.\",\n","        61: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        62: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        63: \"Drug1 may increase the myelosuppressive activities of Drug2.\",\n","        64: \"Drug1 may increase the hypoglycemic activities of Drug2.\",\n","        65: \"Drug1 may increase the excretion rate of Drug2 which could result in a lower serum level and potentially a reduction in efficacy.\",\n","        66: \"The risk or severity of bleeding can be increased when Drug1 is combined with Drug2.\",\n","        67: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        68: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        69: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        70: \"The therapeutic efficacy of Drug2 can be decreased when used in combination with Drug1.\",\n","        71: \"Drug1 may increase the hypertensive activities of Drug2.\",\n","        72: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        73: \"The serum concentration of Drug2 can be increased when it is combined with Drug1.\",\n","        74: \"Drug1 may increase the fluid retaining activities of Drug2.\",\n","        75: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        76: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        77: \"The serum concentration of the active metabolites of Drug2 can be increased when Drug2 is used in combination with Drug1.\",\n","        78: \"Drug1 may increase the hyperglycemic activities of Drug2.\",\n","        79: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        80: \"Drug1 may increase the hepatotoxic activities of Drug2.\",\n","        81: \"Drug1 may increase the thrombogenic activities of Drug2.\",\n","        82: \"Drug1 may increase the arrhythmogenic activities of Drug2.\",\n","        83: \"Drug1 may increase the vasopressor activities of Drug2.\",\n","        84: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        85: \"Drug1 may increase the tachycardic activities of Drug2.\",\n","        86: \"The risk of a hypersensitivity reaction to Drug2 is increased when it is combined with Drug1.\"\n","    }\n","\n","    # Check if the predicted class label exists in the mapping dictionary\n","    if predicted_class in class_description_mapping:\n","        description = class_description_mapping[predicted_class]\n","    else:\n","        description = \"Predicted Class Label not found in the mapping.\"\n","\n","    # Generate molecular diagrams (if molecular formulas are available)\n","    drug1_molecule = None\n","    drug2_molecule = None\n","    if drug1_smiles:\n","        drug1_molecule = Chem.MolFromSmiles(drug1_smiles)\n","    if drug2_smiles:\n","        drug2_molecule = Chem.MolFromSmiles(drug2_smiles)\n","\n","    # Save molecular diagrams as images\n","    drug1_image_path = \"drug1_molecule.png\"\n","    drug2_image_path = \"drug2_molecule.png\"\n","    if drug1_molecule:\n","        Draw.MolToImage(drug1_molecule, size=(300, 300)).save(drug1_image_path)\n","    if drug2_molecule:\n","        Draw.MolToImage(drug2_molecule, size=(300, 300)).save(drug2_image_path)\n","\n","    return f\"Drug 1: {drug1_name} (SMILES: {drug1_smiles}, Molecular Formula: {drug1_formula})\\n\" \\\n","           f\"Drug 2: {drug2_name} (SMILES: {drug2_smiles}, Molecular Formula: {drug2_formula})\\n\" \\\n","           f\"Predicted Class Label: {predicted_class}\\n\" \\\n","           f\"Description: {description}\", drug1_image_path, drug2_image_path\n","\n","# Example drug names\n","drug1_name = \"Aspirin\"\n","drug2_name = \"Ibuprofen\"\n","\n","# Path to the saved model\n","model_path = \"/kaggle/input/ddi-model/ddi_model.h5\"\n","\n","# Make predictions for Drug-Drug Interaction\n","result, drug1_image_path, drug2_image_path = predict_ddi_for_drugs(drug1_name, drug2_name, model_path)\n","\n","# Output the result\n","print(result)\n","\n","# Display molecular diagrams (if available)\n","if drug1_image_path:\n","    drug1_image = plt.imread(drug1_image_path)\n","    plt.imshow(drug1_image)\n","    plt.axis('off')\n","    plt.title(f\"Molecular Diagram of {drug1_name}\")\n","    plt.show()\n","\n","if drug2_image_path:\n","    drug2_image = plt.imread(drug2_image_path)\n","    plt.imshow(drug2_image)\n","    plt.axis('off')\n","    plt.title(f\"Molecular Diagram of {drug2_name}\")\n","    plt.show()\n"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T10:19:11.863572Z","iopub.status.busy":"2023-09-10T10:19:11.863157Z","iopub.status.idle":"2023-09-10T10:19:12.823874Z","shell.execute_reply":"2023-09-10T10:19:12.822941Z","shell.execute_reply.started":"2023-09-10T10:19:11.863537Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["2023-09-10 10:19:12.817 \n","  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n","  command:\n","\n","    streamlit run /opt/conda/lib/python3.10/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"]}],"source":["import streamlit as st\n","import numpy as np\n","from tensorflow import keras\n","from rdkit import Chem\n","from rdkit.Chem import DataStructs\n","from rdkit.Chem.Fingerprints import FingerprintMols\n","from rdkit.Chem import Draw\n","import pubchempy as pcp\n","import matplotlib.pyplot as plt\n","\n","# Define a function to compute the Tanimoto similarity between two molecules\n","def compute_similarity(smiles1, smiles2):\n","    mol1 = Chem.MolFromSmiles(smiles1)\n","    mol2 = Chem.MolFromSmiles(smiles2)\n","\n","    if mol1 is None or mol2 is None:\n","        return 0.0  # Handle invalid input\n","\n","    fp1 = FingerprintMols.FingerprintMol(mol1)\n","    fp2 = FingerprintMols.FingerprintMol(mol2)\n","\n","    return DataStructs.TanimotoSimilarity(fp1, fp2)\n","\n","# Define a function to get SMILES notation for a drug name\n","def get_smiles_for_drug(drug_name):\n","    try:\n","        # Search PubChem for the drug by name\n","        compounds = pcp.get_compounds(drug_name, 'name')\n","\n","        if compounds:\n","            # Take the first result (you may want to handle multiple results differently)\n","            compound = compounds[0]\n","\n","            # Check if SMILES notation is available\n","            if compound.isomeric_smiles:\n","                return compound.isomeric_smiles, compound.molecular_formula\n","            else:\n","                return \"SMILES notation not found for this drug.\", None\n","        else:\n","            return \"Drug not found in PubChem database.\", None\n","\n","    except Exception as e:\n","        return str(e), None\n","\n","# Function to predict DDI for a pair of drugs\n","def predict_ddi_for_drugs(drug1_name, drug2_name, model_path):\n","    # Get SMILES notations and molecular formulas for the drugs\n","    drug1_smiles, drug1_formula = get_smiles_for_drug(drug1_name)\n","    drug2_smiles, drug2_formula = get_smiles_for_drug(drug2_name)\n","\n","    if \"SMILES notation not found\" in (drug1_smiles, drug2_smiles):\n","        return \"SMILES notation not found for one or both drugs.\", None, None\n","\n","    # Compute the structural similarity score between the drug pair\n","    similarity_score = compute_similarity(drug1_smiles, drug2_smiles)\n","\n","    # Create an SSP feature vector (100 components as per your description)\n","    ssp_feature_vector = np.array([similarity_score] * 100)\n","\n","    # Load the saved model\n","    loaded_model = keras.models.load_model(model_path)\n","\n","    # Prepare input data for prediction (reshape the feature vector)\n","    input_data = ssp_feature_vector.reshape(1, -1)\n","\n","    # Make predictions using the loaded model\n","    predictions = loaded_model.predict(input_data)\n","\n","    # Find the class label with the highest probability\n","    predicted_class = np.argmax(predictions)\n","\n","    # Dictionary mapping predicted class labels to descriptions\n","    class_description_mapping = {\n","        1: \"Drug1 can cause an increase in the absorption of Drug2 resulting in an increased serum concentration and potentially a worsening of adverse effects.\",\n","        2: \"Drug1 can cause a decrease in the absorption of Drug2 resulting in a reduced serum concentration and potentially a decrease in efficacy.\",\n","        3: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        4: \"The bioavailability of Drug2 can be decreased when combined with Drug1.\",\n","        5: \"Drug1 may decrease the vasoconstricting activities of Drug2.\",\n","        6: \"Drug1 may increase the anticoagulant activities of Drug2.\",\n","        7: \"Drug1 may increase the ototoxic activities of Drug2.\",\n","        8: \"The therapeutic efficacy of Drug2 can be increased when used in combination with Drug1.\",\n","        9: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        10: \"The risk or severity of hypertension can be increased when Drug2 is combined with Drug1.\",\n","        11: \"The serum concentration of the active metabolites of Drug2 can be reduced when Drug2 is used in combination with Drug1 resulting in a loss in efficacy.\",\n","        12: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        13: \"The absorption of Drug2 can be decreased when combined with Drug1.\",\n","        14: \"Drug1 may decrease the bronchodilatory activities of Drug2.\",\n","        15: \"Drug1 may decrease the bioavailability of Drug2.\",\n","        16: \"Drug1 may increase the central neurotoxic activities of Drug2.\",\n","        17: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        18: \"Drug1 may increase the cardiotoxic activities of Drug2.\",\n","        19: \"Drug1 may increase the vasoconstricting activities of Drug2.\",\n","        20: \"Drug1 may increase the QTc-prolonging activities of Drug2.\",\n","        21: \"Drug1 may increase the neuromuscular blocking activities of Drug2.\",\n","        22: \"Drug1 may increase the adverse neuromuscular activities of Drug2.\",\n","        23: \"Drug1 may increase the stimulatory activities of Drug2.\",\n","        24: \"Drug1 may decrease effectiveness of Drug2 as a diagnostic agent.\",\n","        25: \"Drug1 may increase the atrioventricular blocking (AV block) activities of Drug2.\",\n","        26: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        27: \"Drug1 may increase the neuroexcitatory activities of Drug2.\",\n","        28: \"Drug1 may increase the dermatologic adverse activities of Drug2.\",\n","        29: \"Drug1 may decrease the diuretic activities of Drug2.\",\n","        30: \"Drug1 may increase the orthostatic hypotensive activities of Drug2.\",\n","        31: \"The risk or severity of hypertension can be increased when Drug1 is combined with Drug2.\",\n","        32: \"The risk or severity of QTc prolongation can be increased when Drug1 is combined with Drug2.\",\n","        33: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        34: \"Drug1 may decrease the anticoagulant activities of Drug2.\",\n","        35: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        36: \"Drug1 may decrease the antiplatelet activities of Drug2.\",\n","        37: \"Drug1 may decrease the antihypertensive activities of Drug2.\",\n","        38: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        39: \"Drug1 may increase the constipating activities of Drug2.\",\n","        40: \"Drug1 may increase the respiratory depressant activities of Drug2.\",\n","        41: \"Drug1 may increase the hypotensive and central nervous system depressant (CNS depressant) activities of Drug2.\",\n","        42: \"The risk or severity of hyperkalemia can be increased when Drug1 is combined with Drug2.\",\n","        43: \"The protein binding of Drug2 can be decreased when combined with Drug1.\",\n","        44: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        45: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        46: \"Drug1 may increase the bronchoconstrictory activities of Drug2.\",\n","        47: \"The metabolism of Drug2 can be decreased when combined with Drug1.\",\n","        48: \"Drug1 may increase the myopathic rhabdomyolysis activities of Drug2.\",\n","        49: \"The risk or severity of adverse effects can be increased when Drug1 is combined with Drug2.\",\n","        50: \"The risk or severity of heart failure can be increased when Drug2 is combined with Drug1.\",\n","        51: \"Drug1 may increase the hypercalcemic activities of Drug2.\",\n","        52: \"Drug1 may decrease the analgesic activities of Drug2.\",\n","        53: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        54: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        55: \"Drug1 may decrease the effectiveness of Drug2 as a diagnostic agent.\",\n","        56: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        57: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        58: \"Drug1 may decrease the cardiotoxic activities of Drug2.\",\n","        59: \"Drug1 may increase the ulcerogenic activities of Drug2.\",\n","        60: \"Drug1 may increase the hyponatremic activities of Drug2.\",\n","        61: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        62: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        63: \"Drug1 may increase the myelosuppressive activities of Drug2.\",\n","        64: \"Drug1 may increase the hypoglycemic activities of Drug2.\",\n","        65: \"Drug1 may increase the excretion rate of Drug2 which could result in a lower serum level and potentially a reduction in efficacy.\",\n","        66: \"The risk or severity of bleeding can be increased when Drug1 is combined with Drug2.\",\n","        67: \"The risk or severity of hypotension can be increased when Drug1 is combined with Drug2.\",\n","        68: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        69: \"Drug1 may increase the analgesic activities of Drug2.\",\n","        70: \"The therapeutic efficacy of Drug2 can be decreased when used in combination with Drug1.\",\n","        71: \"Drug1 may increase the hypertensive activities of Drug2.\",\n","        72: \"Drug1 may decrease the excretion rate of Drug2 which could result in a higher serum level.\",\n","        73: \"The serum concentration of Drug2 can be increased when it is combined with Drug1.\",\n","        74: \"Drug1 may increase the fluid retaining activities of Drug2.\",\n","        75: \"The serum concentration of Drug2 can be decreased when it is combined with Drug1.\",\n","        76: \"Drug1 may decrease the sedative activities of Drug2.\",\n","        77: \"The serum concentration of the active metabolites of Drug2 can be increased when Drug2 is used in combination with Drug1.\",\n","        78: \"Drug1 may increase the hyperglycemic activities of Drug2.\",\n","        79: \"Drug1 may increase the central nervous system depressant (CNS depressant) and hypertensive activities of Drug2.\",\n","        80: \"Drug1 may increase the hepatotoxic activities of Drug2.\",\n","        81: \"Drug1 may increase the thrombogenic activities of Drug2.\",\n","        82: \"Drug1 may increase the arrhythmogenic activities of Drug2.\",\n","        83: \"Drug1 may increase the vasopressor activities of Drug2.\",\n","        84: \"Drug1 may increase the vasodilatory activities of Drug2.\",\n","        85: \"Drug1 may increase the tachycardic activities of Drug2.\",\n","        86: \"The risk of a hypersensitivity reaction to Drug2 is increased when it is combined with Drug1.\"\n","}\n","\n","    # Check if the predicted class label exists in the mapping dictionary\n","    if predicted_class in class_description_mapping:\n","        description = class_description_mapping[predicted_class]\n","    else:\n","        description = \"Predicted Class Label not found in the mapping.\"\n","\n","    # Generate molecular diagrams (if molecular formulas are available)\n","    drug1_molecule = None\n","    drug2_molecule = None\n","    if drug1_smiles:\n","        drug1_molecule = Chem.MolFromSmiles(drug1_smiles)\n","    if drug2_smiles:\n","        drug2_molecule = Chem.MolFromSmiles(drug2_smiles)\n","\n","    # Save molecular diagrams as images\n","    drug1_image_path = \"drug1_molecule.png\"\n","    drug2_image_path = \"drug2_molecule.png\"\n","    if drug1_molecule:\n","        Draw.MolToImage(drug1_molecule, size=(300, 300)).save(drug1_image_path)\n","    if drug2_molecule:\n","        Draw.MolToImage(drug2_molecule, size=(300, 300)).save(drug2_image_path)\n","\n","    return f\"Drug 1: {drug1_name} (SMILES: {drug1_smiles}, Molecular Formula: {drug1_formula})\\n\" \\\n","           f\"Drug 2: {drug2_name} (SMILES: {drug2_smiles}, Molecular Formula: {drug2_formula})\\n\" \\\n","           f\"Predicted Class Label: {predicted_class}\\n\" \\\n","           f\"Description: {description}\", drug1_image_path, drug2_image_path\n","\n","# Create a Streamlit web app\n","st.set_page_config(\n","    page_title=\"Drug-Drug Interaction Prediction\",\n","    page_icon=\"💊\",\n","    layout=\"wide\",\n","    initial_sidebar_state=\"expanded\",\n",")\n","\n","# Set the app title and description\n","st.title(\"Drug-Drug Interaction Prediction\")\n","st.write(\n","    \"This app predicts drug-drug interactions (DDIs) and provides information about the drugs involved, \"\n","    \"their SMILES notation, and a brief description of the interaction.\"\n",")\n","\n","# Create input fields for drug names\n","st.sidebar.header(\"Input Drugs\")\n","drug1_name = st.sidebar.text_input(\"Enter Drug 1 Name\", \"Aspirin\")\n","drug2_name = st.sidebar.text_input(\"Enter Drug 2 Name\", \"Ibuprofen\")\n","\n","# Create a button to trigger prediction\n","if st.sidebar.button(\"Predict DDI\"):\n","    # Path to the saved model\n","    model_path = \"ddi_model.h5\"\n","\n","    # Make predictions for Drug-Drug Interaction\n","    result, drug1_image_path, drug2_image_path = predict_ddi_for_drugs(drug1_name, drug2_name, model_path)\n","\n","    # Output the result\n","    st.write(result)\n","\n","    # Display molecular diagrams (if available)\n","    if drug1_image_path:\n","        drug1_image = plt.imread(drug1_image_path)\n","        st.image(drug1_image, caption=f\"Molecular Diagram of {drug1_name}\", use_column_width=True)\n","\n","    if drug2_image_path:\n","        drug2_image = plt.imread(drug2_image_path)\n","        st.image(drug2_image, caption=f\"Molecular Diagram of {drug2_name}\", use_column_width=True)\n"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2023-09-10T10:18:49.475973Z","iopub.status.busy":"2023-09-10T10:18:49.475601Z","iopub.status.idle":"2023-09-10T10:19:03.900204Z","shell.execute_reply":"2023-09-10T10:19:03.899022Z","shell.execute_reply.started":"2023-09-10T10:18:49.475927Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting streamlit\n","  Downloading streamlit-1.26.0-py2.py3-none-any.whl (8.1 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.1/8.1 MB\u001b[0m \u001b[31m49.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n","\u001b[?25hRequirement already satisfied: altair<6,>=4.0 in /opt/conda/lib/python3.10/site-packages (from streamlit) (5.1.1)\n","Requirement already satisfied: 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