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import os |
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import sys |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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from rdkit import Chem |
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from rdkit.Chem import Draw |
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sys.path.insert(0, os.path.abspath("src/")) |
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st.set_page_config(layout="wide") |
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basepath = os.path.dirname(__file__) |
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datapath = os.path.join(basepath, "data") |
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st.title('HyperDTI: Task-conditioned modeling of drug-target interactions.\n') |
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st.markdown('') |
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st.markdown( |
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""" |
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🧬 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 |
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""" |
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) |
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def about_page(): |
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st.markdown( |
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""" |
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### About |
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HyperNetworks have been established as an effective technique to achieve fast adaptation of parameters for |
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neural networks. Recently, HyperNetwork predictions conditioned on descriptors of tasks have improved |
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multi-task generalization in various domains, such as personalized federated learning and neural architecture |
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search. Especially powerful results were achieved in few- and zero-shot settings, attributed to the increased |
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information sharing by the HyperNetwork. With the rise of new diseases fast discovery of drugs is needed which |
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requires models that are able to generalize drug-target interaction predictions in low-data scenarios. |
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In this work, we propose the HyperPCM model, a task-conditioned HyperNetwork approach for the problem of |
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predicting drug-target interactions in drug discovery. Our model learns to generate a QSAR model specialized on |
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a given protein target. We demonstrate state-of-the-art performance over previous methods on multiple |
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well-known benchmarks, particularly in zero-shot settings for unseen protein targets. |
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""" |
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) |
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page_names_to_func = { |
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'About': about_page |
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} |
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selected_page = st.sidebar.selectbox('Choose function', page_names_to_func.keys()) |
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st.sidebar.markdown('') |
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page_names_to_func[selected_page]() |
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