app.py

import argparse
import  os
from rdkit import Chem
import sys
import joblib
sys.modules['sklearn.externals.joblib'] = joblib
from sklearn.externals import joblib
import numpy as np
from rdkit.Chem import Descriptors
from rdkit.Chem import rdMolDescriptors
from xgboost.sklearn import XGBClassifier,XGBRegressor
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from rdkit.Chem import MACCSkeys
import torch.nn as nn
import lightgbm as lgb
from sklearn.ensemble import RandomForestRegressor
import wget
import warnings
import gradio as gr
warnings.filterwarnings("ignore")

Eluent_smiles=['CCCCCC','CC(OCC)=O','C(Cl)Cl','CO','CCOCC']
def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--file_path', type=str, default=os.getcwd()+'\TLC_dataset.xlsx', help='path of download dataset')
    parser.add_argument('--dipole_path', type=str, default=os.getcwd() + '\compound_list_带化合物分类.xlsx',
                        help='path of dipole file')
    parser.add_argument('--data_range', type=int, default=4944, help='utilized data range,robot:4114,manual:4458,new:4944')
    parser.add_argument('--automatic_divide', type=bool, default=False, help='automatically divide dataset by 80% train,10% validate and 10% test')
    parser.add_argument('--choose_total', type=int, default=387, help='train total num,robot:387,manual:530')
    parser.add_argument('--choose_train', type=int, default=308, help='train num,robot:387,manual:530')
    parser.add_argument('--choose_validate', type=int, default=38, help='validate num')
    parser.add_argument('--choose_test', type=int, default=38, help='test num')
    parser.add_argument('--seed', type=int, default=324, help='random seed for split dataset')
    parser.add_argument('--torch_seed', type=int, default=324, help='random seed for torch')
    parser.add_argument('--add_dipole', type=bool, default=True, help='add dipole into dataset')
    parser.add_argument('--add_molecular_descriptors', type=bool, default=True, help='add molecular_descriptors (分子量(MW)、拓扑极性表面积(TPSA)、可旋转键的个数(NROTB)、氢键供体个数(HBA)、氢键受体个数(HBD)、脂水分配系数值(LogP)) into dataset')
    parser.add_argument('--add_MACCkeys', type=bool, default=True,help='add MACCSkeys into dataset')
    parser.add_argument('--add_eluent_matrix', type=bool, default=True,help='add eluent matrix into dataset')
    parser.add_argument('--test_mode', type=str, default='robot', help='manual data or robot data or fix, costum test data')
    parser.add_argument('--use_model', type=str, default='Ensemble',help='the utilized model (XGB,LGB,ANN,RF,Ensemble,Bayesian)')
    parser.add_argument('--download_data', type=bool, default=False, help='use local dataset or download from dataset')
    parser.add_argument('--use_sigmoid', type=bool, default=True, help='use sigmoid')
    parser.add_argument('--shuffle_array', type=bool, default=True, help='shuffle_array')
    parser.add_argument('--characterization_mode', type=str, default='standard',
                        help='the characterization mode for the dataset, including standard, precise_TPSA, no_multi')

    #---------------parapmeters for plot---------------------
    parser.add_argument('--plot_col_num', type=int, default=4, help='The col_num in plot')
    parser.add_argument('--plot_row_num', type=int, default=4, help='The row_num in plot')
    parser.add_argument('--plot_importance_num', type=int, default=10, help='The max importance num in plot')
    #--------------parameters For LGB-------------------
    parser.add_argument('--LGB_max_depth', type=int, default=5, help='max_depth for LGB')
    parser.add_argument('--LGB_num_leaves', type=int, default=25, help='num_leaves for LGB')
    parser.add_argument('--LGB_learning_rate', type=float, default=0.007, help='learning_rate for LGB')
    parser.add_argument('--LGB_n_estimators', type=int, default=1000, help='n_estimators for LGB')
    parser.add_argument('--LGB_early_stopping_rounds', type=int, default=200, help='early_stopping_rounds for LGB')

    #---------------parameters for XGB-----------------------
    parser.add_argument('--XGB_n_estimators', type=int, default=200, help='n_estimators for XGB')
    parser.add_argument('--XGB_max_depth', type=int, default=3, help='max_depth for XGB')
    parser.add_argument('--XGB_learning_rate', type=float, default=0.1, help='learning_rate for XGB')

    #---------------parameters for RF------------------------
    parser.add_argument('--RF_n_estimators', type=int, default=1000, help='n_estimators for RF')
    parser.add_argument('--RF_random_state', type=int, default=1, help='random_state for RF')
    parser.add_argument('--RF_n_jobs', type=int, default=1, help='n_jobs for RF')

    #--------------parameters for ANN-----------------------
    parser.add_argument('--NN_hidden_neuron', type=int, default=128, help='hidden neurons for NN')
    parser.add_argument('--NN_optimizer', type=str, default='Adam', help='optimizer for NN (Adam,SGD,RMSprop)')
    parser.add_argument('--NN_lr', type=float, default=0.005, help='learning rate for NN')
    parser.add_argument('--NN_model_save_location', type=str, default=os.getcwd()+'\model_save_NN', help='learning rate for NN')
    parser.add_argument('--NN_max_epoch', type=int, default=5000, help='max training epoch for NN')
    parser.add_argument('--NN_add_sigmoid', type=bool, default=True, help='whether add sigmoid in NN')
    parser.add_argument('--NN_add_PINN', type=bool, default=False, help='whether add PINN in NN')
    parser.add_argument('--NN_epi', type=float, default=100.0, help='The coef of PINN Loss in NN')



    config = parser.parse_args()
    config.device = 'cpu'
    return config

class ANN(nn.Module):
    '''
    Construct artificial neural network
    '''
    def __init__(self, in_neuron, hidden_neuron, out_neuron,config):
        super(ANN, self).__init__()
        self.input_layer = nn.Linear(in_neuron, hidden_neuron)
        self.hidden_layer = nn.Linear(hidden_neuron, hidden_neuron)
        self.output_layer = nn.Linear(hidden_neuron, out_neuron)
        self.NN_add_sigmoid=config.NN_add_sigmoid


    def forward(self, x):
        x = self.input_layer(x)
        x = F.leaky_relu(x)
        x = self.hidden_layer(x)
        x = F.leaky_relu(x)
        x = self.hidden_layer(x)
        x = F.leaky_relu(x)
        x = self.hidden_layer(x)
        x = F.leaky_relu(x)
        x = self.output_layer(x)
        if self.NN_add_sigmoid==True:
            x = F.sigmoid(x)
        return x

class Model_ML():
    def __init__(self,config,X_test):
        super(Model_ML, self).__init__()
        self.X_test=X_test
        self.seed=config.seed
        self.torch_seed=config.seed
        self.config=config
        self.add_dipole = config.add_dipole
        self.add_molecular_descriptors = config.add_molecular_descriptors
        self.add_eluent_matrix=config.add_eluent_matrix
        self.use_sigmoid=config.use_sigmoid

        self.use_model=config.use_model
        self.LGB_max_depth=config.LGB_max_depth
        self.LGB_num_leaves=config.LGB_num_leaves
        self.LGB_learning_rate=config.LGB_learning_rate
        self.LGB_n_estimators=config.LGB_n_estimators
        self.LGB_early_stopping_rounds=config.LGB_early_stopping_rounds

        self.XGB_n_estimators=config.XGB_n_estimators
        self.XGB_max_depth = config.XGB_max_depth
        self.XGB_learning_rate = config.XGB_learning_rate

        self.RF_n_estimators=config.RF_n_estimators
        self.RF_random_state=config.RF_random_state
        self.RF_n_jobs=config.RF_n_jobs

        self.NN_hidden_neuron=config.NN_hidden_neuron
        self.NN_optimizer=config.NN_optimizer
        self.NN_lr= config.NN_lr
        self.NN_model_save_location=config.NN_model_save_location
        self.NN_max_epoch=config.NN_max_epoch
        self.NN_add_PINN=config.NN_add_PINN
        self.NN_epi=config.NN_epi
        self.device=config.device

        self.plot_row_num=config.plot_row_num
        self.plot_col_num=config.plot_col_num
        self.plot_importance_num=config.plot_importance_num



    def load_model(self):
            model_LGB = lgb.LGBMRegressor(objective='regression', max_depth=self.LGB_max_depth,
                                      num_leaves=self.LGB_num_leaves,
                                      learning_rate=self.LGB_learning_rate, n_estimators=self.LGB_n_estimators)
            model_XGB = XGBRegressor(seed=self.seed,
                                 n_estimators=self.XGB_n_estimators,
                                 max_depth=self.XGB_max_depth,
                                 eval_metric='rmse',
                                 learning_rate=self.XGB_learning_rate,
                                 min_child_weight=1,
                                 subsample=1,
                                 colsample_bytree=1,
                                 colsample_bylevel=1,
                                 gamma=0)

            model_RF = RandomForestRegressor(n_estimators=self.RF_n_estimators,
                                          criterion='mse',
                                          random_state=self.RF_random_state,
                                          n_jobs=self.RF_n_jobs)

            Net = ANN(self.X_test.shape[1], self.NN_hidden_neuron, 1, config=self.config).to(self.device)
            #model_LGB = joblib.load('model_LGB.pkl')
            wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_LGB.pkl')
            wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_XGB.pkl')
            wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_RF.pkl')
            wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_ANN.pkl')
            model_LGB = joblib.load('model_LGB.pkl')
            model_XGB = joblib.load('model_XGB.pkl')
            model_RF = joblib.load('model_RF.pkl')
            Net.load_state_dict(torch.load('model_ANN.pkl',map_location=torch.device('cpu')))
            return model_LGB,model_XGB,model_RF,Net

    def get_Rf(self):
        model_LGB, model_XGB, model_RF, model_ANN = Model_ML.load_model(self)

        X_test_ANN = Variable(torch.from_numpy(self.X_test.astype(np.float32)).to(self.device), requires_grad=True)
        y_pred_ANN = model_ANN(X_test_ANN).cpu().data.numpy()
        y_pred_ANN = y_pred_ANN.reshape(y_pred_ANN.shape[0], )


        y_pred_XGB = model_XGB.predict(self.X_test)
        if self.use_sigmoid == True:
            y_pred_XGB = 1 / (1 + np.exp(-y_pred_XGB))

        y_pred_LGB = model_LGB.predict(self.X_test)
        if self.use_sigmoid == True:
            y_pred_LGB = 1 / (1 + np.exp(-y_pred_LGB))

        y_pred_RF = model_RF.predict(self.X_test)
        if self.use_sigmoid == True:
            y_pred_RF = 1 / (1 + np.exp(-y_pred_RF))

        y_pred = (0.2 * y_pred_LGB + 0.2 * y_pred_XGB + 0.2 * y_pred_RF + 0.4 * y_pred_ANN)
        return y_pred

def get_descriptor(smiles,ratio):
    compound_mol = Chem.MolFromSmiles(smiles)
    descriptor=[]
    descriptor.append(Descriptors.ExactMolWt(compound_mol))
    descriptor.append(Chem.rdMolDescriptors.CalcTPSA(compound_mol))
    descriptor.append(Descriptors.NumRotatableBonds(compound_mol))  # Number of rotable bonds
    descriptor.append(Descriptors.NumHDonors(compound_mol))  # Number of H bond donors
    descriptor.append(Descriptors.NumHAcceptors(compound_mol)) # Number of H bond acceptors
    descriptor.append(Descriptors.MolLogP(compound_mol)) # LogP
    descriptor=np.array(descriptor)*ratio
    return descriptor

def get_eluent_descriptor(eluent):
    eluent=np.array(eluent)
    des = np.zeros([6,])
    for i in range(eluent.shape[0]):
        if eluent[i] != 0:
            e_descriptors = get_descriptor(Eluent_smiles[i], eluent[i])
            des+=e_descriptors
    return des

def get_data_from_smile(smile, eluent_list):
    compound_mol = Chem.MolFromSmiles(smile)
    Finger = MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(smile))
    fingerprint = np.array([x for x in Finger])
    compound_finger = fingerprint
    compound_MolWt = Descriptors.ExactMolWt(compound_mol)
    compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol)
    compound_nRotB = Descriptors.NumRotatableBonds(compound_mol)  # Number of rotable bonds
    compound_HBD = Descriptors.NumHDonors(compound_mol)  # Number of H bond donors
    compound_HBA = Descriptors.NumHAcceptors(compound_mol)  # Number of H bond acceptors
    compound_LogP = Descriptors.MolLogP(compound_mol)  # LogP
    X_test = np.zeros([1, 179])
    X_test[0, 0:167] = compound_finger
    X_test[0, 167:173] = 0
    X_test[0, 173:179] = [compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP]

    eluent_array = get_eluent_descriptor(eluent_list)
    eluent_array = np.array(eluent_array)
    X_test[0, 167:173] = eluent_array

    return X_test

def get_data_from_xlsx(file_name):
    file = pd.read_excel(file_name)
    smiles = file['SMILES'].values
    PEs = file['PE'].values
    EAs = file['EA'].values
    DCMs = file['DCM'].values
    MeOHs = file['MeOH'].values
    Et2Os = file['Et2O'].values
    X_test = np.zeros([len(smiles), 179])
    for i in range(len(smiles)):
        smile=smiles[i]
        eluent_sum = PEs[i] + EAs[i] + DCMs[i] + MeOHs[i] + Et2Os[i]
        if eluent_sum != 0:
            eluent_list = [PEs[i] / eluent_sum, EAs[i] / eluent_sum, DCMs[i] / eluent_sum, MeOHs[i] / eluent_sum, Et2Os[i] / eluent_sum]
        else:
            eluent_list = [0, 0, 0, 0, 0]
        compound_mol = Chem.MolFromSmiles(smile)
        Finger = MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(smile))
        fingerprint = np.array([x for x in Finger])
        compound_finger = fingerprint
        compound_MolWt = Descriptors.ExactMolWt(compound_mol)
        compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol)
        compound_nRotB = Descriptors.NumRotatableBonds(compound_mol)  # Number of rotable bonds
        compound_HBD = Descriptors.NumHDonors(compound_mol)  # Number of H bond donors
        compound_HBA = Descriptors.NumHAcceptors(compound_mol)  # Number of H bond acceptors
        compound_LogP = Descriptors.MolLogP(compound_mol)  # LogP

        X_test[i, 0:167] = compound_finger
        X_test[i, 167:173] = 0
        X_test[i, 173:179] = [compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP]

        eluent_array = get_eluent_descriptor(eluent_list)
        eluent_array = np.array(eluent_array)
        X_test[i, 167:173] = eluent_array
    return X_test
    
def predict_single(smile,PE,EA,DCM,MeOH,Et20):
    config = parse_args()
    config.add_dipole = False
    eluent_sum=PE+EA+DCM+MeOH+Et20
    if eluent_sum!=0:
        eluent_list=[PE/eluent_sum,EA/eluent_sum,DCM/eluent_sum,MeOH/eluent_sum,Et20/eluent_sum]
    else:
        eluent_list=[0,0,0,0,0]
    X_test=get_data_from_smile(smile,eluent_list)
    Model = Model_ML(config,X_test)
    Rf=Model.get_Rf()
    return Rf[0]

def predict_xlsx(file):
    file_name=file.name
    config = parse_args()
    config.add_dipole = False
    X_test = get_data_from_xlsx(file_name)
    Model = Model_ML(config, X_test)
    Rf = Model.get_Rf()
    return Rf

if __name__=='__main__':

    
    theme = gr.themes.Monochrome(
        primary_hue="indigo",
        secondary_hue="blue",
        neutral_hue="slate",
    )

    model_card = f"""
    ## Description\n
    It is a app for predicting Rf values of a compound under given eluents in TLC.\n
    input: smiles of one compound, such as CC(OCC)=O,   and the ratio of five solvents, example: 20 1 0 0 0 for PE:EA=20:1\n
    output: the predicted Rf value.\n\n
    ## Citation\n
    We would like appreciate if you use our software and give us credit in acknowledgement section of your paper：
    We used Rf prediction software in our synthesis work.[Citation1, Citation2]\n
    Citation1: H. Xu, J. Lin, Q. Liu, Y. Chen, J. Zhang, Y. Yang, M.C. Young, Y. Xu, D. Zhang, F. Mo
    High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques
    Chem (2022), 3202–3214, 10.1016/j.chempr.2022.08.008\n
    Citation2: https://huggingface.co/spaces/woshixuhao/Rf_compare
    """


    with gr.Blocks() as demo:
        gr.Markdown('''
                <div>
                <h1 style='text-align: center'>Rf prediction</h1>
                </div>
            ''')
        gr.Markdown(model_card)
        with gr.Tab("Single prediction"):
            gr.Interface(fn=predict_single, inputs=["text", "number","number","number","number","number"], outputs='number')
        with gr.Tab("xlsx prediction"):
            gr.Interface(fn=predict_xlsx, inputs=["file"], outputs="number")
       
    demo.launch()
    # smile='O=C(OC1C(OC(C)=O)C(OC(C)=O)C(OC(C)=O)C(COC(C)=O)O1)C'
    # eluent=[0,0.9,0,0,0]
    # print(predict_single(smile,1,0,0,0,0))