Upload gradio_app.py

gradio_app.py

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+import gradio as gr
+from trainer import Trainer
+import PIL
+from PIL import Image
+import pandas as pd
+import random
+from rdkit import Chem
+from rdkit.Chem import Draw
+from rdkit.Chem.Draw import IPythonConsole
+import shutil
+
+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(),
+}
+
+
+
+def function(model_name: str, mol_num: int, seed: int) -> tuple[PIL.Image, pd.DataFrame, str]:
+    '''
+    Returns:
+    image, score_df, file path
+    '''
+    model_name = model_name.replace("DrugGEN-", "")
+
+    config = model_configs[model_name]
+    config.inference_sample_num = mol_num
+    config.seed = seed
+
+    trainer = Trainer(config)
+    scores = trainer.inference() # create scores_df out of this
+
+    score_df = pd.DataFrame(scores, index=[0])
+
+    output_file_path = f'experiments/inference/{model_name}/inference_drugs.txt'
+    import os
+    new_path = f'DrugGEN-{model_name}_denovo_mols.smi'
+    os.rename(output_file_path, new_path)
+    
+    with open(new_path) as f:
+        inference_drugs = f.read()
+    
+    generated_molecule_list = inference_drugs.split("\n")
+    
+    rng = random.Random(seed)
+    
+    selected_molecules = rng.choices(generated_molecule_list,k=12)
+    selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules]
+    
+    drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
+    drawOptions.prepareMolsBeforeDrawing = False
+    drawOptions.bondLineWidth = 0.5
+
+    molecule_image = Draw.MolsToGridImage(
+        selected_molecules,
+        molsPerRow=3,
+        subImgSize=(400, 400),
+        maxMols=len(selected_molecules),
+        # legends=None,
+        returnPNG=False,
+        drawOptions=drawOptions,
+        highlightAtomLists=None,
+        highlightBondLists=None,
+    )
+
+
+    return molecule_image, score_df, new_path
+
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
+            with gr.Row():
+                gr.Markdown("[![arXiv](https://img.shields.io/badge/arXiv-2302.07868-b31b1b.svg)](https://arxiv.org/abs/2302.07868)")
+                gr.Markdown("[![github-repository](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/HUBioDataLab/DrugGEN)")
+            
+            with gr.Accordion("Expand to display information about models", open=False):
+                gr.Markdown("""
+### 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.
+        """)
+            model_name = gr.Radio(
+                choices=("DrugGEN-Prot", "DrugGEN-CrossLoss", "DrugGEN-NoTarget"),
+                value="DrugGEN-Prot",
+                label="Select a model to make inference",
+                info=" DrugGEN-Prot and DrugGEN-CrossLoss models design molecules to target the AKT1 protein"
+            )
+
+            num_molecules = gr.Number(
+                label="Number of molecules to generate",
+                precision=0, # integer input
+                minimum=1,
+                value=1000,
+            )
+            seed_num = gr.Number(
+                label="RNG seed value (can be used for reproducibility):",
+                precision=0, # integer input
+                minimum=0,
+                value=42,
+            )
+
+            submit_button = gr.Button(
+                value="Start Generating"
+            )
+
+        with gr.Column(scale=2):
+            scores_df = gr.Dataframe(
+                label="Scores",
+            )
+            file_download = gr.File(
+                label="Click to download generated molecules",
+            )
+            image_output = gr.Image(
+                label="Structures of randomly selected 12 de novo molecules from the inference set:"
+            )
+            # ).style(
+            #     height=200*4,
+            #     width=200*3,
+            # )
+
+    submit_button.click(function, inputs=[model_name, num_molecules, seed_num], outputs=[image_output, scores_df, file_download], api_name="inference")
+
+demo.queue(concurrency_count=1)
+demo.launch()