app.py

import os
import sys
#import torch
import numpy as np 
import pandas as pd
import streamlit as st
#import esm

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
    """
)
st.error('WARNING! This app is currently under development and should not be used!')


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.
        """
    )

    st.image('figures/hyper-dti.png', caption='Overview of HyperPCM architecture.')


'''
def predict_dti():
    st.markdown('## Predict drug-target interaction')

    st.write('In the future this page can be used to predict interactions betweek a query drug compound and a query protein target by the HyperPCM mdoel.')
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown('### Drug')
        
        mol_col1, mol_col2 = st.columns(2)

        with mol_col1:
            smiles = st.text_input('Enter query SMILES', value='CC(=O)OC1=CC=CC=C1C(=O)O', placeholder='CC(=O)OC1=CC=CC=C1C(=O)O')
            if smiles:
                mol = Chem.MolFromSmiles(smiles)
                mol_img = Chem.Draw.MolToImage(mol)
                st.image(mol_img) #, width = 140)

        with mol_col2:
            selected_encoder = st.selectbox(
                'Select encoder',('None', 'CDDD', 'MolBERT', 'Dummy')
            )
            if smiles: 
                if selected_encoder == 'CDDD':
                    from cddd.inference import InferenceModel
                    CDDD_MODEL_DIR = 'src/encoders/cddd'
                    cddd_model = InferenceModel(CDDD_MODEL_DIR)
                    drug_embedding = cddd_model.seq_to_emb([smiles])
                    #from huggingface_hub import hf_hub_download
                    #precomputed_embs = f'{selected_encoder}_encoding.csv'
                    #REPO_ID = "emmas96/Lenselink"
                    #embs_path = hf_hub_download(REPO_ID, precomputed_embs)
                    #embs = pd.read_csv(embs_path)
                    #embedding = embs[smiles]
                elif selected_encoder == 'MolBERT':
                    from molbert.utils.featurizer.molbert_featurizer import MolBertFeaturizer
                    from huggingface_hub import hf_hub_download
                    CDDD_MODEL_DIR = 'encoders/molbert/last.ckpt'
                    REPO_ID = "emmas96/hyperpcm"
                    checkpoint_path = hf_hub_download(REPO_ID, MOLBERT_MODEL_DIR)
                    molbert_model = MolBertFeaturizer(checkpoint_path, max_seq_len=500, embedding_type='average-1-cat-pooled')
                    drug_embedding = molbert_model.transform([smiles])
                elif selected_encoder == 'Dummy':
                    drug_embedding = [0,1,2,3,4,5]
                else: 
                    drug_embedding = None
                    st.image('figures/molecule_encoder.png')
                    st.warning('Choose encoder above...')
                
                if drug_embedding is not None:
                    st.image('figures/molecule_encoder_done.png')
                    st.success('Encoding complete.')

    with col2:
        st.markdown('### Target')

        prot_col1, prot_col2 = st.columns(2)

        with prot_col1:
            sequence = st.text_input('Enter query amino-acid sequence', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
        
            if sequence == 'HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA':
                st.image('figures/ex_protein.jpeg')
            elif sequence:
                st.error('Visualization comming soon...')

        with prot_col2:
            selected_encoder = st.selectbox(
                'Select encoder for protein target',('None', 'SeqVec', 'UniRep', 'ESM-1b', 'ProtT5')
            )
        
            if sequence: 
                if selected_encoder == 'SeqVec':
                    with st.spinner('Encoding in progress...'):
                        from bio_embeddings.embed import SeqVecEmbedder
                        encoder = SeqVecEmbedder()
                        embeddings = encoder.embed_batch([sequence])
                        for emb in embeddings:
                            prot_embedding = encoder.reduce_per_protein(emb)
                            break
                elif selected_encoder == 'UniRep':
                    with st.spinner('Encoding in progress...'):
                        from jax_unirep.utils import load_params
                        params = load_params() 
                        from jax_unirep.featurize import get_reps                               
                        embedding, h_final, c_final = get_reps([sequence])
                        prot_embedding = embedding.mean(axis=0)
                elif selected_encoder == 'ESM-1b':
                    with st.spinner('Encoding in progress...'):
                        from bio_embeddings.embed import ESM1bEmbedder
                        encoder = ESM1bEmbedder()
                        embeddings = encoder.embed_batch([sequence])
                        for emb in embeddings:
                            prot_embedding = encoder.reduce_per_protein(emb)
                            break
                elif selected_encoder == 'ProtT5':
                    with st.spinner('Encoding in progress...'):
                        from bio_embeddings.embed import ProtTransT5XLU50Embedder
                        encoder = ProtTransT5XLU50Embedder()
                        embeddings = encoder.embed_batch([sequence])
                        for emb in embeddings:
                            prot_embedding = encoder.reduce_per_protein(emb)
                            break
                else: 
                    prot_embedding = None
                    st.image('figures/protein_encoder.png')
                    st.warning('Choose encoder above...')
                    
                if prot_embedding is not None:
                    st.image('figures/protein_encoder_done.png')
                    st.success('Encoding complete.')

    if drug_embedding is None or prot_embedding is None:
        st.warning('Waiting for both drug and target embeddings to be computed...')
    else:
        st.markdown('### Inference')
    
        import time
        progress_text = "HyperPCM predicts the interaction between the query drug compound toward the query protein target. Please wait."
        my_bar = st.progress(0, text=progress_text)
        for i in range(100):
            time.sleep(0.1)
            my_bar.progress(i + 1, text=progress_text)
        my_bar.progress(100, text="HyperPCM predicts the interaction between the query drug compound toward the query protein target. Done.")
            
        st.markdown('### Interaction')
        st.write('HyperPCM predicts an activity of xxx pChEMBL.')
'''


def retrieval():
    st.markdown('## Retrieve top-k most active drug compounds')

    st.write('In the furute this page will retrieve the top-k drug compounds that are predicted to have the highest activity toward the given protein target from either the Lenselink or Davis datasets.')

    st.markdown('### Target')

    st.write(f'The top-{selected_k} most active drug coupounds from {selected_dataset} predicted by HyperPCM are: ')
    dummy_smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O', 'COc1cc(C=O)ccc1O', 'CC(=O)Nc1ccc(O)cc1', 'CC(=O)Nc1ccc(OS(=O)(=O)O)cc1', 'CC(=O)Nc1ccc(O[C@@H]2O[C@H](C(=O)O)[C@@H](O)[C@H](O)[C@H]2O)cc1']
    cols = st.columns(5)
    for j, col in enumerate(cols):
        with col:
            for i in range(int(selected_k/5)):
                mol = Chem.MolFromSmiles(dummy_smiles[j])
                mol_img = Chem.Draw.MolToImage(mol)
                st.image(mol_img) 

    '''
    col1, col2, col3, col4 = st.columns(4)
    with col2:
        sequence = st.text_input('Enter query amino-acid sequence', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
        if sequence == 'HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA':
            st.image('figures/ex_protein.jpeg')
        elif sequence:
            st.error('Visualization coming soon...')
    
    with col3:
        selected_encoder = st.selectbox(
                'Select encoder for protein target',('SeqVec', 'None')
            )
        if sequence:
            if selected_encoder == 'SeqVec':
                st.image('figures/protein_encoder_done.png')
                with st.spinner('Encoding in progress...'):
                    from bio_embeddings.embed import SeqVecEmbedder
                    encoder = SeqVecEmbedder()
                    embeddings = encoder.embed_batch([sequence])
                    for emb in embeddings:
                        prot_embedding = encoder.reduce_per_protein(emb)
                        break
                st.success('Encoding complete.')
            else: 
                prot_embedding = None
                st.image('figures/protein_encoder.png')
                st.warning('Choose encoder above...')

    if prot_embedding is not None:
        st.markdown('### Inference')
        
        import time
        progress_text = "HyperPCM predicts the QSAR model for the query protein target. Please wait."
        my_bar = st.progress(0, text=progress_text)
        for i in range(100):
            time.sleep(0.1)
            my_bar.progress(i + 1, text=progress_text)
        my_bar.progress(100, text="HyperPCM predicts the QSAR model for the query protein target. Done.")
        
        st.markdown('### Retrieval')
        
        col1, col2 = st.columns(2)
        with col1:
            selected_dataset = st.selectbox(
                    'Select dataset from which the drug compounds should be retrieved',('Lenselink', 'Davis')
                )
        with col2:
            selected_k = st.selectbox(
                    'Select the top-k number of drug compounds to retrieve',(5, 10, 15, 20)
                )
        
        st.write(f'The top-{selected_k} most active drug coupounds from {selected_dataset} predicted by HyperPCM are: ')
        dummy_smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O', 'COc1cc(C=O)ccc1O', 'CC(=O)Nc1ccc(O)cc1', 'CC(=O)Nc1ccc(OS(=O)(=O)O)cc1', 'CC(=O)Nc1ccc(O[C@@H]2O[C@H](C(=O)O)[C@@H](O)[C@H](O)[C@H]2O)cc1']
        cols = st.columns(5)
        for j, col in enumerate(cols):
            with col:
                for i in range(int(selected_k/5)):
                    mol = Chem.MolFromSmiles(dummy_smiles[j])
                    mol_img = Chem.Draw.MolToImage(mol)
                    st.image(mol_img) 
    '''

'''
def display_protein():
    st.markdown('## Display protein structure')
    st.write('In the future this page will display the ESM predicted sequence of a protein target.')

    st.markdown('### Target')
    sequence = st.text_input('Enter the amino-acid sequence of the query protein target', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
    
    if sequence:

        st.image('figures/ex_protein.jpeg')
        
        model = esm.pretrained.esmfold_v1()
        model = model.eval().cuda()
        
        with torch.no_grad():
            output = model.infer_pdb(sequence)
            st.write(output)
        
        with open("result.pdb", "w") as f:
            f.write(output)
        
        
        struct = bsio.load_structure("result.pdb", extra_fields=["b_factor"])
        print(struct.b_factor.mean())


    """
        model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
        batch_converter = alphabet.get_batch_converter()
        batch_labels, batch_strs, batch_tokens = batch_converter([("protein1", sequence),])
        
        # Extract per-residue representations (on CPU)
        with torch.no_grad():
            results = model(batch_tokens, repr_layers=[12], return_contacts=True)
        token_representations = results["representations"][12]
    
        token_list = token_representations.tolist()[0][0][0]
        
        client = Client(url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"])
        
        result = client.fetch("SELECT seq, distance('topK=500')(representations, " + str(token_list) + ')'+ "as dist FROM default.esm_protein_indexer_768")
        
        result_temp_seq = []
        
        for i in result:
            # result_temp_coords = i['seq']
            result_temp_seq.append(i['seq'])
        
        result_temp_seq = list(set(result_temp_seq))
    
        if st.button(result_temp_seq[0]):
            print(result_temp_seq[0])
        elif st.button(result_temp_seq[1]):
            print(result_temp_seq[1])
        elif st.button(result_temp_seq[2]):
            print(result_temp_seq[2])
        elif st.button(result_temp_seq[3]):
            print(result_temp_seq[3])
        elif st.button(result_temp_seq[4]):
            print(result_temp_seq[4])
    
        start[2] = st.pyplot(visualize_3D_Coordinates(result_temp_coords).figure)
        
        headers = {
            'Content-Type': 'application/x-www-form-urlencoded',
            }
        response = requests.post('https://api.esmatlas.com/foldSequence/v1/pdb/', headers=headers, data=sequence)
        name = sequence[:3] + sequence[-3:]
        pdb_string = response.content.decode('utf-8')
        with open('predicted.pdb', 'w') as f:
            f.write(pdb_string)
        struct = bsio.load_structure('predicted.pdb', extra_fields=["b_factor"])
        b_value = round(struct.b_factor.mean(), 4)
        render_mol(pdb_string)
        if residues_marker:
            start[3] = showmol(render_pdb_resn(viewer = render_pdb(id = id_PDB),resn_lst = [residues_marker]))
        else:
            start[3] = showmol(render_pdb(id = id_PDB))
        st.session_state['xq'] = st.session_state.model
    
    # example proteins ["HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA"], ["AHKLFIGGLPNYLNDDQVKELLTSFGPLKAFNLVKDSATGLSKGYAFCEYVDINVTDQAIAGLNGMQLGDKKLLVQRASVGAKNA"]
    """

def display_context():
    st.markdown('## Display context')
    st.write('In the future this page will visualize the context module for a given protein, i.e., show important features and highly ranked / related proteins from the context.')
'''

def references():
    st.markdown(
        '''
        ## References

        Schmidhuber, J., “Learning to control fast-weight memories: An alternative to dynamic recurrent networks.” Neural Computation, 1992.

        Davis, M. I., et al. "Comprehensive analysis of kinase inhibitor selectivity." Nature Biotechnology 29.11 (2011): 1046-1051.
        
        Ha, D., et al. “HyperNetworks”. ICLR, 2017.
        
        Lenselink, E. B., et al. "Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set." Journal of Cheminformatics 9.1 (2017): 1-14.

        Alley, E. C., et al. "Unified rational protein engineering with sequence-based deep representation learning." Nature Methods 16.12 (2019): 1315-1322.
        
        Chang, O., et al., “Principled weight initialization for hypernetworks.” ICLR, 2019.

        Heinzinger, M., et al. "Modeling aspects of the language of life through transfer-learning protein sequences." BMC Bioinformatics 20.1 (2019): 1-17.
        
        Winter, R., et al. "Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations." Chemical Science 10.6 (2019): 1692-1701.
        
        Fabian, B., et al. "Molecular representation learning with language models and domain-relevant auxiliary tasks." Workshop for ML4Molecules (2020).
    
        Elnaggar, A., et al. "ProtTrans: Toward understanding the language of life through self-supervised learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2021): 7112–7127.
        
        Rives, A., et al. "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences." Proceedings of the National Academy of Sciences 118.15 (2021): e2016239118.
        
        Kim, P. T., et al. "Unsupervised Representation Learning for Proteochemometric Modeling." International Journal of Molecular Sciences 22.23 (2021): 12882.
        
        Schimunek, J., et al., “Context-enriched molecule representations improve few-shot drug discovery.” ICLR, 2023.

        '''
    )

page_names_to_func = {
    'About': about_page,
    #'Predict DTI': predict_dti,
    'Retrieve Top-k': retrieval,
    #'Display Protein': display_protein,
    #'Display Context': display_context,
    #'References': references
}

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