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| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| from PIL import Image | |
| import base64 | |
| import pandas as pd | |
| import streamlit as st | |
| import pickle | |
| from rdkit import Chem | |
| from rdkit.Chem import AllChem | |
| from sklearn.ensemble import RandomForestRegressor | |
| import random | |
| import numpy as np | |
| from keras.wrappers.scikit_learn import KerasRegressor | |
| from sklearn.metrics import mean_squared_error | |
| import time | |
| import numpy | |
| from sklearn.model_selection import GridSearchCV | |
| import tensorflow | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| from tensorflow.keras.layers import Dropout | |
| def create_model(optimizer='RMSprop', learn_rate=0.1, momentum=0.4, activation='sigmoid', dropout_rate=0.0): | |
| keras_model = Sequential() | |
| keras_model.add(Dense(128, input_dim=train_encoded.shape[1], activation=activation)) | |
| keras_model.add(Dropout(dropout_rate)) | |
| keras_model.add(Dense(32, activation=activation)) | |
| keras_model.add(Dropout(dropout_rate)) | |
| keras_model.add(Dense(8,activation=activation)) | |
| keras_model.add(Dropout(dropout_rate)) | |
| keras_model.add(Dense(1,activation='linear')) | |
| keras_model.summary() | |
| keras_model.compile(loss='mean_squared_error', optimizer=optimizer) | |
| return keras_model | |
| def get_ecfc(smiles_list, radius=2, nBits=2048, useCounts=True): | |
| ecfp_fingerprints=[] | |
| erroneous_smiles=[] | |
| for smiles in smiles_list: | |
| mol=Chem.MolFromSmiles(smiles) | |
| if mol is None: | |
| ecfp_fingerprints.append([None]*nBits) | |
| erroneous_smiles.append(smiles) | |
| else: | |
| mol=Chem.AddHs(mol) | |
| if useCounts: | |
| ecfp_fingerprints.append(list(AllChem.GetHashedMorganFingerprint(mol, radius, nBits))) | |
| else: | |
| ecfp_fingerprints.append(list(AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits).ToBitString())) | |
| df_ecfp_fingerprints = pd.DataFrame(data = ecfp_fingerprints, index = smiles_list) | |
| if len(erroneous_smiles)>0: | |
| print("The following erroneous SMILES have been found in the data:\n{}.\nThe erroneous SMILES will be removed from the data.".format('\n'.join(map(str, erroneous_smiles)))) | |
| df_ecfp_fingerprints = df_ecfp_fingerprints.dropna(how='any') | |
| return df_ecfp_fingerprints | |
| import deepchem as dc | |
| from deepchem.models import GraphConvModel | |
| def generate(SMILES, verbose=False): | |
| featurizer = dc.feat.ConvMolFeaturizer() | |
| gcn = featurizer.featurize(SMILES) | |
| properties = [random.randint(-1,1)/100 for i in range(0,len(SMILES))] | |
| dataset = dc.data.NumpyDataset(X=gcn, y=np.array(properties)) | |
| return dataset | |
| st.write("""# Accelerated reaction energy prediction for redox batteries 🧪 """) | |
| st.write('By: [Alishba Imran](https://www.linkedin.com/in/alishba-imran-/)') | |
| about_part = st.expander("Learn More About Project", expanded=False) | |
| with about_part: | |
| st.write(''' | |
| #### About | |
| Redox flow batteries (RFB) are widely being explored as a class of electrochemical energy storage devices for large-scale energy storage applications. Redox flow batteries convert electrical energy to chemical energy via electrochemical reactions (through reversible oxidation and reduction) of compounds. | |
| To develop next-gen redox flow batteries with high cycle life and energy density, we need to speed up the discovery of electroactive materials with desired properties. This process can currently be very slow and expensive given how large and diverse the chemical space of the candidate compounds is. | |
| Using an attention-based graph convolutional neural network technique, I've developed a model that can take in reactants as SMILEs and predict the reaction energy in the redox reaction. | |
| A lot of this work was inspired and built on top of this [paper](https://chemrxiv.org/engage/chemrxiv/article-details/60c7575f469df44a40f45465). Feel free to give it a try and reach out for any feedback. Email: alishbai734@gmail.com. | |
| ''') | |
| st.write('**Insert your SMILES**') | |
| st.write('Type any SMILES used as a reactant in the redox reaction. This model will output the reaction energy.') | |
| SMILES_input = "Oc1cccc(c12)c(O)c(nn2)O\nc1cccc(c12)cc(nn2)O\nOc1c(O)ccc(c12)cc(nn2)O" | |
| SMILES = st.text_area('press ctrl+enter to run model!', SMILES_input, height=20) | |
| SMILES = SMILES.split('\n') | |
| SMILES = list(filter(None, SMILES)) | |
| if len(SMILES)>1000: | |
| SMILES=SMILES[0:1000] | |
| ecfc_encoder = get_ecfc(SMILES) | |
| generated_dataset = generate(SMILES) | |
| filename = 'final_models/transformers.pkl' | |
| infile = open(filename,'rb') | |
| transformers = pickle.load(infile) | |
| infile.close() | |
| model_dir = 'final_models/tf_chp_initial' | |
| gcne_model = dc.models.GraphConvModel(n_tasks=1, batch_size=100, mode='regression', dropout=0.25,model_dir= model_dir,random_seed=0) | |
| gcne_model.restore('final_models/tf_chp_initial/ckpt-94/ckpt-197') | |
| pred_gcne = gcne_model.predict(generated_dataset, transformers) | |
| from keras.models import model_from_json | |
| keras_final_model = model_from_json(open('./final_models/keras_final_model_architecture.json').read()) | |
| keras_final_model.load_weights('./final_models/keras_final_model_weights.h5') | |
| rf_final_model = pickle.load(open(r'./final_models/rf_final_model.txt', "rb")) | |
| pred_keras = keras_final_model.predict(ecfc_encoder) | |
| pred_rf = rf_final_model.predict(ecfc_encoder) | |
| pred_rf_r = pred_rf.reshape((len(pred_rf),1)) | |
| pred_consensus = (pred_keras + pred_gcne + pred_rf)/3 | |
| from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score | |
| test1_mae = [] | |
| test1_mae.append(0.00705) | |
| test1_mae.append(0.00416) | |
| test1_mae.append(0.0035) | |
| test2_mae = [] | |
| test2_mae.append(0.00589) | |
| test2_mae.append(0.00483) | |
| test2_mae.append(0.00799) | |
| weighted_pred_0_1_3=( np.power(2/(test1_mae[0]+test2_mae[0]),3) * pred_gcne + | |
| np.power(2/(test1_mae[1]+test2_mae[1]),3) * pred_keras + | |
| np.power(2/(test1_mae[2]+test2_mae[2]),3) * pred_rf_r ) / ( | |
| np.power(2/(test1_mae[0]+test2_mae[0]),3) + np.power(2/(test1_mae[1]+test2_mae[1]),3) + np.power(2/(test1_mae[2]+test2_mae[2]),3)) | |
| pred_weighted = (pred_gcne + pred_keras + pred_rf_r)/3 | |
| df_results = pd.DataFrame(SMILES, columns=['SMILES Reactant']) | |
| df_results["Predicted Reaction Energy"]= weighted_pred_0_1_3 | |
| df_results=df_results.round(6) | |
| st.header('Prediction of Reaction Energy for RFB') | |
| df_results | |