app.py

import os
import sys
import glob  # Needed?
import json  # needed?
import zipfile  # needed?
import numpy as np
import pandas as pd
import streamlit as st
from tqdm import tqdm
from itertools import chain     # needed?

import torch
from torch.utils.data import DataLoader

from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import AllChem
from rdkit.Chem import DataStructs

sys.path.insert(0, os.path.abspath("src/"))

st.set_page_config(layout="wide")

basepath = os.path.dirname(__file__)
datapath = os.path.join(basepath, "data")

device = "cuda" if torch.cuda.is_available() else "cpu"

st.title('HyperDTI: Task-conditioned modeling of drug-target interactions.')
st.markdown(
    """
    \n
    🧬 Generate a QSAR model for the protein target of interest, useful for high-throughput screening or drug repurposing.\n
    💻 Github: [ml-jku/hyper-dti](https://https://github.com/ml-jku/hyper-dti)\n
    📝 NeurIPS 2022 AI4Science workshop paper: [OpenReview](https://openreview.net/forum?id=dIX34JWnIAL)\n
    """
)

def about_page():
    st.markdown(
        """        
        HyperNetworks have been established as an effective technique to achieve fast adaptation of parameters for 
        neural networks. Recently, HyperNetwork predictions conditioned on descriptors of tasks have improved 
        multi-task generalization in various domains, such as personalized federated learning and neural architecture 
        search. Especially powerful results were achieved in few- and zero-shot settings, attributed to the increased 
        information sharing by the HyperNetwork. With the rise of new diseases fast discovery of drugs is needed which 
        requires models that are able to generalize drug-target interaction predictions in low-data scenarios. 
        
        In this work, we propose the HyperPCM model, a task-conditioned HyperNetwork approach for the problem of 
        predicting drug-target interactions in drug discovery. Our model learns to generate a QSAR model specialized on
        a given protein target. We demonstrate state-of-the-art performance over previous methods on multiple 
        well-known benchmarks, particularly in zero-shot settings for unseen protein targets.
        """
    )
    
# example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]

page_names_to_func = {
    'About': about_page
}

selected_page = st.sidebar.selectbox('Choose function', page_names_to_func.keys())
st.sidebar.markdown('')
page_names_to_func[selected_page]()