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import gradio as gr
from inference import Inference
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='DrugGEN'
inference_model="experiments/models/DrugGEN/"
sample_num=1000
inf_dataset_file="chembl45_test.pt"
inf_raw_file='data/chembl_test.smi'
inf_batch_size=1
mol_data_dir='data'
features=False
act='relu'
max_atom=45
dim=32
depth=1
heads=8
mlp_ratio=3
dropout=0.
log_sample_step=100
set_seed=True
seed=10
class NoTargetConfig(DrugGENConfig):
submodel="NoTarget"
dim=128
inference_model="experiments/models/NoTarget/"
model_configs = {
"DrugGEN": DrugGENConfig(),
"NoTarget": NoTargetConfig(),
}
def function(model_name: str, num_molecules: int, seed_num: int) -> tuple[PIL.Image, pd.DataFrame, str]:
'''
Returns:
image, score_df, file path
'''
config = model_configs[model_name]
config.sample_num = num_molecules
config.seed = seed_num
inferer = Inference(config)
scores = inferer.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'{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(config.seed)
if num_molecules > 12:
selected_molecules = rng.choices(generated_molecule_list, k=12)
else:
selected_molecules = rng.choices(generated_molecule_list, k=num_molecules)
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("[](https://arxiv.org/abs/2302.07868)")
gr.Markdown("[](https://github.com/HUBioDataLab/DrugGEN)")
with gr.Accordion("Expand to display information about models", open=False):
gr.Markdown("""
### Model Variations
- **DrugGEN** is the default model. The input of the generator is the real molecules (ChEMBL) dataset (to ease the learning process) and the discriminator compares the generated molecules with the real inhibitors of the given target protein.
- **NoTarget** is the non-target-specific version of DrugGEN. This model only focuses on learning the chemical properties from the ChEMBL training dataset.
""")
model_name = gr.Radio(
choices=("DrugGEN", "NoTarget"),
value="DrugGEN",
label="Select a model to make inference",
info=" DrugGEN model 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,
maximum=10_000,
)
val = random.randint(0, 10000)
seed_num = gr.Number(
label="RNG seed value (can be used for reproducibility):",
precision=0, # integer input
minimum=0,
value=val,
)
submit_button = gr.Button(
value="Start Generating"
)
with gr.Column(scale=2):
scores_df = gr.Dataframe(
label="Scores",
headers=["Runtime (seconds)", "Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)"]
)
file_download = gr.File(
label="Click to download generated molecules",
)
image_output = gr.Image(
label="Structures of randomly selected de novo molecules from the inference set:"
)
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.queue()
demo.launch()
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