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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'rdkit'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [1], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrdkit\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Chem\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrdkit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mChem\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AllChem\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# from rdkit.Chem import Draw\u001b[39;00m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rdkit'"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from rdkit import Chem\n",
"from rdkit.Chem import AllChem\n",
"# from rdkit.Chem import Draw\n",
"from rdkit.Chem import rdChemReactions as Reactions\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from keras.preprocessing import sequence\n",
"from keras.utils import pad_sequences\n",
"import keras\n",
"from keras import backend as K\n",
"from keras.models import load_model\n",
"import argparse\n",
"import h5py\n",
"import pdb\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"seq_rdic = ['A', 'I', 'L', 'V', 'F', 'W', 'Y', 'N', 'C', 'Q', 'M','S', 'T', 'D', 'E', 'R', 'H', 'K', 'G', 'P', 'O', 'U', 'X', 'B', 'Z']\n",
"seq_dic = {w: i+1 for i, w in enumerate(seq_rdic)}\n",
"\n",
"\n",
"def encodeSeq(seq, seq_dic):\n",
" if pd.isnull(seq):\n",
" return [0]\n",
" else:\n",
" return [seq_dic[aa] for aa in seq]\n",
"\n",
"\n",
"def load_modelfile(model_string):\n",
"\tloaded_model = tf.keras.models.load_model(model_string)\n",
"\treturn loaded_model\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'load_modelfile' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [4], line 80\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prediction_vals[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 75\u001b[0m \u001b[38;5;66;03m# loaded_model = load_modelfile('./../CNN_results/model_final.model')\u001b[39;00m\n\u001b[1;32m 76\u001b[0m \n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;66;03m# kegg_df = KEGG_compound_read.reset_index()\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m loaded_model \u001b[38;5;241m=\u001b[39m load_modelfile(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./../CNN_results_split_final/Final_model.model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 81\u001b[0m KEGG_compound_read \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./../CNN_data/Final_test/kegg_compound.csv\u001b[39m\u001b[38;5;124m'\u001b[39m, index_col \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCompound_ID\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 82\u001b[0m kegg_df \u001b[38;5;241m=\u001b[39m KEGG_compound_read\u001b[38;5;241m.\u001b[39mreset_index()\n",
"\u001b[0;31mNameError\u001b[0m: name 'load_modelfile' is not defined"
]
}
],
"source": [
"\n",
"def prot_feature_gen_from_str_input(prot_input_str, prot_len=2500):\n",
" Prot_ID = prot_input_str.split(':')[0]\n",
" Prot_seq = prot_input_str.split(':')[1]\n",
" prot_dataframe = pd.DataFrame(\n",
" {'Protein_ID': Prot_ID, 'Sequence': Prot_seq}, index=[0])\n",
" prot_dataframe.set_index('Protein_ID')\n",
"\n",
" prot_dataframe[\"encoded_sequence\"] = prot_dataframe.Sequence.map(\n",
" lambda a: encodeSeq(a, seq_dic))\n",
" prot_feature = pad_sequences(\n",
" prot_dataframe[\"encoded_sequence\"].values, prot_len)\n",
"\n",
" return prot_feature, Prot_ID\n",
"\n",
"\n",
"def mol_feature_gen_from_str_input(mol_str, kegg_id_flag, kegg_df):\n",
"\n",
"\tif kegg_id_flag == 1:\n",
"\t\tKEGG_ID = mol_str\n",
"\t\tkegg_id_loc = kegg_df.index[kegg_df.Compound_ID == KEGG_ID][0]\n",
"\t\tKEGG_ID_info = kegg_df.loc[kegg_id_loc]\n",
"\t\tKEGG_ID_info_df = KEGG_ID_info.to_frame().T.set_index('Compound_ID')\n",
"\n",
"\t\tfinal_return = KEGG_ID_info_df\n",
"\t\tfinal_id = KEGG_ID\n",
"\n",
"\telse:\n",
"\t\ttry:\n",
"\t\t\tmol_ID = mol_str.split(':')[0]\n",
"\t\t\tmol_smiles = mol_str.split(':')[1]\n",
"\t\t\tmol = Chem.MolFromSmiles(mol_smiles)\n",
"\t\t\tfp1 = AllChem.GetMorganFingerprintAsBitVect(\n",
"\t\t\t mol, useChirality=True, radius=2, nBits=2048)\n",
"\t\t\tfp_list = list(np.array(fp1).astype(float))\n",
"\t\t\tfp_str = list(map(str, fp_list))\n",
"\t\t\tmol_fp = '\\t'.join(fp_str)\n",
"\n",
"\t\t\tmol_dict = {}\n",
"\t\t\tmol_dict['Compound_ID'] = mol_ID\n",
"\t\t\tmol_dict['Smiles'] = mol_smiles\n",
"\t\t\tmol_dict['morgan_fp_r2'] = mol_fp\n",
"\n",
"\t\t\tmol_info_df = pd.DataFrame(mol_dict, index=[0])\n",
"\t\t\tmol_info_df.set_index('Compound_ID')\n",
"\n",
"\t\t\tfinal_return = mol_info_df\n",
"\t\t\tfinal_id = mol_ID\n",
"\n",
"\t\texcept Exception as error:\n",
"\t\t\tprint('Something wrong with molecule input string...' + repr(error))\n",
"\n",
"\treturn final_return, final_id\n",
"\n",
"\n",
"def act_df_gen_mol_feature(mol_id, prot_id):\n",
"\tact_df = pd.DataFrame(\n",
"\t {'Protein_ID': prot_id, 'Compound_ID': mol_id}, index=[0])\n",
"\n",
"\treturn act_df\n",
"\n",
"\n",
"def compound_feature_gen_df_input(act_df, comp_df, comp_len=2048, comp_vec='morgan_fp_r2'):\n",
"\tact_df = pd.merge(act_df, comp_df, left_on='Compound_ID', right_index=True)\n",
"\tcomp_feature = np.stack(act_df[comp_vec].map(lambda fp: fp.split(\"\\t\")))\n",
"\tcomp_feature = comp_feature.astype('float')\n",
"\treturn comp_feature\n",
"\n",
"\n",
"def model_prediction(compound_feature, enz_feature, model):\n",
" prediction_vals = model.predict([compound_feature, enz_feature])\n",
"\n",
" return prediction_vals[0][0]\n",
"\n",
"\n",
"# loaded_model = load_modelfile('./../CNN_results/model_final.model')\n",
"\n",
"# KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')\n",
"# kegg_df = KEGG_compound_read.reset_index()\n",
"\n",
"loaded_model = load_modelfile('./../CNN_results_split_final/Final_model.model')\n",
"KEGG_compound_read = pd.read_csv('./../CNN_data/Final_test/kegg_compound.csv', index_col = 'Compound_ID')\n",
"kegg_df = KEGG_compound_read.reset_index()\n",
"\n",
"\n",
"# def img_to_bytes(img_path):\n",
"# img_bytes = Path(img_path).read_bytes()\n",
"# encoded = base64.b64encode(img_bytes).decode()\n",
"# return encoded\n",
"# # st.title('dGPredictor')\n",
"\n",
"# header_html = \"<img src='../figures/header.png'>\"\n",
"\n",
"# st.markdown(\n",
"# header_html, unsafe_allow_html=True,\n",
"# )\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error somewhere...NameError(\"name 'prot_feature_gen_from_str_input' is not defined\")\n"
]
},
{
"ename": "NameError",
"evalue": "name 'compound_feature1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn [3], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mError somewhere...\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mrepr\u001b[39m(e))\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mtype\u001b[39m(compound_feature1))\n",
"\u001b[0;31mNameError\u001b[0m: name 'compound_feature1' is not defined"
]
}
],
"source": [
"\n",
"enz_str =\"A0A4P8WFA8:MTKRVLVTGGAGFLGSHLCERLLSEGHEVICLDNFGSGRRKNIKEFEDHPSFKVNDRDVRISESLPSVDRIYHLASRASPADFTQFPVNIALANTQGTRRLLDQARACDARMVFASTSEVYGDPKVHPQPETYTGNVNIRGARGCYDESKRFGETLTVAYQRKYDVDARTVRIFNTYGPRMRPDDGRVVPTFVTQALRGDDLTIYGDGEQTRSFCYVDDLIEGLISLMRVDNPEHNVYNIGKENERTIKELAYEVLGLTDTESDIVYEPLPEDDPGQRRPDITRAKTELDWEPKISLREGLEDTITYFDN\"\n",
"\n",
"comp_str = 'C00149:O[C@@H](CC([O-])=O)C([O-])=O'\n",
"try:\n",
" prot_feature, prot_id = prot_feature_gen_from_str_input(enz_str)\n",
" kegg_id_flag = 0\n",
" comp_feature, comp_id = mol_feature_gen_from_str_input(comp_str, kegg_id_flag, kegg_df)\n",
"\n",
" act_dataframe = act_df_gen_mol_feature(comp_id, prot_id)\n",
" # pdb.set_trace()\n",
" compound_feature1 = compound_feature_gen_df_input(act_dataframe, comp_feature)\n",
"\n",
"except Exception as e:\n",
" print('Error somewhere...' + repr(e))\n",
"\n",
"print(type(compound_feature1))\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 223ms/step\n"
]
}
],
"source": [
"\n",
"EnzRankScore = model_prediction(compound_feature1, prot_feature, loaded_model)\n",
"es = EnzRankScore"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9315796"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|