EGFR_app.py

import streamlit as st
import pandas as pd
from PIL import Image
import subprocess
import os
import base64
import pickle

# Molecular descriptor calculator
def desc_calc():
    # Performs the descriptor calculation
    bashCommand = "java -Xms2G -Xmx2G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -2d -descriptortypes ./PaDEL-Descriptor/PubchemFingerprinter.xml -dir ./ -file descriptors_output.csv"
    process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
    output, error = process.communicate()
    os.remove('molecule.smi')

# File download
def filedownload(df):
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
    href = f'<a href="data:file/csv;base64,{b64}" download="prediction.csv">Download Predictions</a>'
    return href

# Model building
def build_model(input_data):
    # Reads in saved regression model
    load_model = pickle.load(open('ML_model_EGFR.pkl', 'rb'))
    # Apply model to make predictions
    prediction = load_model.predict(input_data)
    st.header('**Prediction output**')
    prediction_output = pd.Series(prediction, name='pIC50')
    molecule_name = pd.Series(load_data[1], name='molecule_name')
    df = pd.concat([molecule_name, prediction_output], axis=1)
    st.write(df)
    st.markdown(filedownload(df), unsafe_allow_html=True)

# Logo image
image = Image.open('App_Logo.jpg')

st.image(image, use_column_width=True)

# Page title
st.markdown("""
# Bioactivity Prediction App """)

# Page title
st.markdown("""
# EGFR Activity Prediction Tool

This app allows you to predict the pIC50 of novel small molecules against the `Epidermal growth factor receptor (EGFR)` enzyme. `EGFR` is a drug target for multiple cancers.

**Credits**
- App built in `Python` + `Streamlit` by [Dr. Ashish Gupta and Dr. Amarinder Singh Thind]
- Descriptor calculated using [PaDELpy module of Python]
---
""")

# Sidebar
with st.sidebar.header('1. Upload your data'):
    uploaded_file = st.sidebar.file_uploader("Upload your input file(The input file should contain SMILES notations for the small molecules)", type=['txt'])
    st.sidebar.markdown("""
[Example input file](https://github.com/amarinderthind/EGFR-ap/blob/main/Input_file_example.txt)
""")

if st.sidebar.button('Predict'):
    if uploaded_file is not None:
        load_data = pd.read_table(uploaded_file, sep=' ', header=None)
        load_data.to_csv('molecule.smi', sep='\t', header=False, index=False)

        st.header('**Original input data**')
        st.write(load_data)

        with st.spinner("Calculating descriptors..."):
            desc_calc()

        # Read in calculated descriptors and display the dataframe
        st.header('**Calculated molecular descriptors**')
        desc = pd.read_csv('descriptors_output.csv')
        st.write(desc)
        st.write(desc.shape)

        # Read descriptor list used in previously built model
        st.header('**Subset of descriptors from previously built models**')
        Xlist = list(pd.read_csv('descriptor_list.csv').columns)
        desc_subset = desc[Xlist]
        st.write(desc_subset)
        st.write(desc_subset.shape)

        # Apply trained model to make a prediction on query compounds
        build_model(desc_subset)
    else:
        st.warning('Please upload a file before predicting.')
else:
    st.info('Upload input data in the sidebar to start!')