!pip install DeepPurpose
!pip install gradio
!pip install rdkit-pypi
!pip install git+https://github.com/bp-kelley/descriptastorus 
!pip install pandas-flavor
import numpy as np
import pandas as pd
from DeepPurpose import utils
from DeepPurpose import DTI as models
import gradio


model_binding = models.model_pretrained(model = 'MPNN_CNN_BindingDB')
model_kiba = models.model_pretrained(model = 'MPNN_CNN_KIBA')
model_davis = models.model_pretrained(model = 'MPNN_CNN_DAVIS')

def DTI_pred(data, drug, target):
    if data == 'BindingDB':
        model = model_binding
    elif data == 'KIBA':
        model = model_kiba
    elif data == 'DAVIS':
        model = model_davis

    X_pred = utils.data_process(X_drug = [drug], X_target = [target], y = [0],
                                drug_encoding = 'MPNN', target_encoding = 'CNN', 
                                split_method='no_split')
    y_pred = model.predict(X_pred)
    return str(y_pred[0])

gradio.Interface(DTI_pred, 
                 [gradio.inputs.Dropdown(label = "Training Dataset", choices = ['BindingDB', 'DAVIS', 'KIBA']),
                  gradio.inputs.Textbox(lines = 5, label = "Drug SMILES"),
                  gradio.inputs.Textbox(lines = 5, label = "Target Amino Acid Sequence")], 
                 gradio.outputs.Textbox(label = "Predicted Affinity")).launch(share=True)
    

What is deep learning?

Deep learning is a part of machine learning, and this is a part of the AI ( artifitial intelligence).

With the dates of the amino acid letters and smiles we obtained