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import os
import sys
import numpy as np
import pandas as pd
import streamlit as st
from rdkit import Chem
from rdkit.Chem import Draw
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")
st.title('HyperDTI: Task-conditioned modeling of drug-target interactions.\n')
st.markdown('')
st.markdown(
"""
🧬 Github: [ml-jku/hyper-dti](https://https://github.com/ml-jku/hyper-dti) 📝 NeurIPS 2022 AI4Science workshop paper: [OpenReview](https://openreview.net/forum?id=dIX34JWnIAL)\n
"""
)
def about_page():
st.markdown(
"""
### About
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]()
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