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backup_app_21Dec2022.txt
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import streamlit as st
import os
import pickle
import pandas as pd
from PIL import Image
from rdkit import Chem
from rdkit.Chem import AllChem, Draw
from padelpy import padeldescriptor
# Page configuration
st.set_page_config(
page_title='PARP1pred',
page_icon='💊',
initial_sidebar_state='expanded')
# Session state
if 'smiles_input' not in st.session_state:
st.session_state.smiles_input = ''
# Utilities
if os.path.isfile('molecule.smi'):
os.remove('molecule.smi')
def insert_example_smiles():
st.session_state.smiles_input = 'O=C(c1cc(Cc2n[nH]c(=O)c3ccccc23)ccc1F)N1CCN(C(=O)C2CC2)CC1'
def clear_smiles():
st.session_state.smiles_input = ''
# The App
st.title('💊 PARP1pred app')
tab1,tab2,tab3,tab4,tab5,tab6,tab7 = st.tabs(['Main', 'About', 'What is PARP1?', 'Dataset', 'Model performance', 'Python libraries', 'Citing us'])
with tab1:
if st.session_state.smiles_input == '':
st.info('PARP1pred allow users to predict whether a query molecule is active/inactive towards the PARP1 target protein.')
st.subheader('Enter SMILES notation')
smiles_txt = st.text_input('Enter SMILES notation', st.session_state.smiles_input)
st.session_state.smiles_input = smiles_txt
st.button('Example input', on_click=insert_example_smiles)
else:
st.subheader('⚛️ Input molecule:')
with st.expander('Show SMILES', expanded=True):
#st.write('**SMILES**')
st.text(st.session_state.smiles_input)
with st.expander('Show chemical structures', expanded=True):
#st.write('**Chemical structure**')
smi = Chem.MolFromSmiles(st.session_state.smiles_input)
Chem.Draw.MolToFile(smi, 'molecule.png', width=900)
mol_image = Image.open('molecule.png')
st.image(mol_image)
# Input SMILES saved to file
f = open('molecule.smi', 'w')
f.write(f'{st.session_state.smiles_input}\tmol_001')
f.close()
# Compute PADEL descriptors
if st.session_state.smiles_input != '':
st.subheader('🔢 Descriptors')
if os.path.isfile('molecule.smi'):
padeldescriptor(mol_dir='molecule.smi',
d_file='descriptors.csv',
descriptortypes='data/PubchemFingerprinter.xml',
detectaromaticity=True,
standardizenitro=True,
standardizetautomers=True,
threads=2,
removesalt=True,
log=True,
fingerprints=True)
descriptors = pd.read_csv('descriptors.csv')
descriptors.drop('Name', axis=1, inplace=True)
with st.expander('Show full set of descriptors as calculated for query molecule'):
#st.write('**Full set of descriptors (calculated for query molecule)**')
st.write(descriptors)
st.write(descriptors.shape)
# Load descriptor subset used in trained model
if st.session_state.smiles_input != '':
model = pickle.load(open('data/oversampling_PubChem_RandomForestClassifier.pkl', 'rb'))
pubchem_subset = model.feature_names_in_
query_desc_1 = descriptors.columns.difference(pubchem_subset)
query_desc_2 = descriptors.drop(query_desc_1, axis=1)
with st.expander('Show subset of descriptors as used in trained model'):
#st.write('**Subset of descriptors (used in trained model)**')
st.write(query_desc_2)
st.write(query_desc_2.shape)
# Read in saved classification model
if st.session_state.smiles_input != '':
st.subheader('🤖 Predictions')
pred = int(model.predict(query_desc_2))
if pred == 0:
st.error('Inactive')
if pred == 1:
st.success('Active')
st.button('Restart', on_click=clear_smiles)
with tab2:
coverimage = Image.open('PARP1pred.jpg')
st.image(coverimage)
with tab3:
st.header('What is PARP1?')
st.write('Poly (ADP-ribose) polymerase-1 (PARP-1) is an enzyme that catalyzes the ADP-ribosylation of a specific protein and plays a vital role in DNA repair. It has become an attractive target as inhibition of PARP-1 causes a toxic accumulation of DNA double strand breaks in cancer cells, particularly those with BRCA1/2 deficiency, which are found in breast, ovarian, prostate, and pancreatic cancers.')
with tab4:
st.header('Dataset')
st.write('In our work, we retrieved a human PARP-1 biological dataset from the ChEMBL database. The data was curated, and we received a non-redundant set of 2,018 PARP-1 inhibitors, which were divided into 1,720 active and 298 inactive compounds.')
with tab5:
st.header('Model performance')
st.write('We selected PubChem as a molecular fingerprint and used a random forest with an oversampling approach to construct the best model. The Matthews correlation coefficients in training, cross-validation, and test sets were 1.00, 0.96, and 0.74, respectively.')
with tab6:
st.header('Python libraries')
st.markdown('''
This app is based on the following Python libraries:
- `streamlit`
- `pandas`
- `rdkit`
- `padelpy`
''')
with tab7:
st.markdown('T. Lerksuthirat, S. Chitphuk, W. Stitchantrakul, D. Dejsuphong, A.A. Malik, C. Nantasenamat, PARP1PRED: A web server for screening the bioactivity of inhibitors against DNA repair enzyme PARP-1, ***EXCLI Journal*** (2023) DOI: https://doi.org/10.17179/excli2022-5602.')