app.py

import streamlit as st
import streamlit_ext as ste

from trainer import Trainer
import random
from rdkit.Chem import Draw
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
import io
from PIL import Image

class DrugGENConfig:
    submodel='CrossLoss'
    act='relu'
    z_dim=16
    max_atom=45
    lambda_gp=1
    dim=128
    depth=1
    heads=8
    dec_depth=1
    dec_heads=8
    dec_dim=128
    mlp_ratio=3
    warm_up_steps=0
    dis_select='mlp'
    init_type='normal'
    batch_size=128
    epoch=50
    g_lr=0.00001
    d_lr=0.00001
    g2_lr=0.00001
    d2_lr=0.00001
    dropout=0.
    dec_dropout=0.
    n_critic=1
    beta1=0.9
    beta2=0.999
    resume_iters=None
    clipping_value=2
    features=False
    test_iters=10_000
    num_test_epoch=30_000
    inference_sample_num=1000
    num_workers=1
    mode="inference"
    inference_iterations=100
    inf_batch_size=1
    protein_data_dir='data/akt'
    drug_index='data/drug_smiles.index'
    drug_data_dir='data/akt'
    mol_data_dir='data'
    log_dir='experiments/logs'
    model_save_dir='experiments/models'
    # inference_model=""
    sample_dir='experiments/samples'
    result_dir="experiments/tboard_output"
    dataset_file="chembl45_train.pt"
    drug_dataset_file="akt_train.pt"
    raw_file='data/chembl_train.smi'
    drug_raw_file="data/akt_train.smi"
    inf_dataset_file="chembl45_test.pt"
    inf_drug_dataset_file='akt_test.pt'
    inf_raw_file='data/chembl_test.smi'
    inf_drug_raw_file="data/akt_test.smi"
    log_sample_step=1000
    set_seed=True
    seed=1
    resume=False
    resume_epoch=None
    resume_iter=None
    resume_directory=None
    
class ProtConfig(DrugGENConfig):
    submodel="Prot"
    inference_model="experiments/models/Prot"

class CrossLossConfig(DrugGENConfig):
    submodel="CrossLoss"
    inference_model="experiments/models/CrossLoss"

class NoTargetConfig(DrugGENConfig):
    submodel="NoTarget"
    inference_model="experiments/models/NoTarget"


model_configs = {
    "Prot": ProtConfig(),
    "CrossLoss": CrossLossConfig(),
    "NoTarget": NoTargetConfig(),
}


with st.sidebar:
    st.title("DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
    st.write("[![arXiv](https://img.shields.io/badge/arXiv-2302.07868-b31b1b.svg)](https://arxiv.org/abs/2302.07868) [![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/DrugGEN)")

    with st.expander("Expand to display information about models"):
        st.write("""
### Model Variations
- **DrugGEN-Prot**: composed of two GANs, incorporates protein features to the transformer decoder module of GAN2 (together with the de novo molecules generated by GAN1) to direct the target centric molecule design.
- **DrugGEN-CrossLoss**: composed of one GAN, the input of the GAN1 generator is the real molecules dataset and the GAN1 discriminator compares the generated molecules with the real inhibitors of the given target.
- **DrugGEN-NoTarget**: composed of one GAN, focuses on learning the chemical properties from the ChEMBL training dataset, no target-specific generation.

        """)    

    with st.form("model_selection_from"):
        model_name = st.radio(
            'Select a model to make inference (DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein)',
            ('DrugGEN-Prot', 'DrugGEN-CrossLoss', 'DrugGEN-NoTarget')
        )

        model_name = model_name.replace("DrugGEN-", "")

        molecule_num_input = st.number_input('Number of molecules to generate', min_value=1, max_value=100_000, value=1000, step=1)

        seed_input = st.number_input("RNG seed value (can be used for reproducibility):", min_value=0, value=42, step=1)
        
        submitted = st.form_submit_button("Start Computing")



if submitted:
# if submitted or ("submitted" in st.session_state):
    # st.session_state["submitted"] = True
    config = model_configs[model_name]

    config.inference_sample_num = molecule_num_input
    config.seed = seed_input
    
    with st.spinner(f'Creating the trainer class instance for {model_name}...'):
        trainer = Trainer(config)
    with st.spinner(f'Running inference function of {model_name} (this may take a while) ...'):
        results = trainer.inference()
    st.success(f"Inference of {model_name} took {results['runtime']:.2f} seconds.")

    with st.expander("Expand to see the generation performance scores"):
        st.write("### Generation performance scores (novelty is calculated in comparison to the training dataset)")
        st.success(f"Validity: {results['fraction_valid']}")
        st.success(f"Uniqueness: {results['uniqueness']}")
        st.success(f"Novelty: {results['novelty']}")

    with open(f'experiments/inference/{model_name}/inference_drugs.txt') as f:
        inference_drugs = f.read()
    # st.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")
    ste.download_button(label="Click to download generated molecules", data=inference_drugs, file_name=f'DrugGEN-{model_name}_denovo_mols.smi', mime="text/plain")

    
    st.write("Structures of randomly selected 12 de novo molecules from the inference set:")
    # from rdkit.Chem import Draw
# img = Draw.MolsToGridImage(mol_list, molsPerRow=5, subImgSize=(250, 250), maxMols=num_mols,
                           # legends=None, useSVG=True)
    generated_molecule_list = inference_drugs.split("\n")

    selected_molecules = random.choices(generated_molecule_list,k=12)

    selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]
    # IPythonConsole.UninstallIPythonRenderer()
    drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
    drawOptions.prepareMolsBeforeDrawing = False
    drawOptions.bondLineWidth = 1.

    molecule_image = Draw.MolsToGridImage(
        selected_molecules,
        molsPerRow=3,
        subImgSize=(250, 250),
        maxMols=len(selected_molecules),
        # legends=None,
        returnPNG=False,
        # drawOptions=drawOptions,
        highlightAtomLists=None,
        highlightBondLists=None,
        
    )
    print(type(molecule_image))
    # print(type(molecule_image._data_and_metadata()))
    molecule_image.save("result_grid.png") 
    # png_data = io.BytesIO()
    # molecule_image.save(png_data, format='PNG')
    # png_data.seek(0)
    
    # Step 2: Read the PNG image data as a PIL image
    # pil_image = Image.open(png_data)
    # st.image(pil_image)
    st.image(molecule_image)

else:
    st.warning("Please select a model to make inference")