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
import os
import time
class DrugGENConfig:
# Inference configuration
submodel='DrugGEN'
inference_model="/home/user/app/experiments/models/DrugGEN/"
sample_num=100
# Data configuration
inf_smiles='/home/user/app/data/chembl_test.smi'
train_smiles='/home/user/app/data/chembl_train.smi'
inf_batch_size=1
mol_data_dir='/home/user/app/data'
features=False
# Model configuration
act='relu'
max_atom=45
dim=128
depth=1
heads=8
mlp_ratio=3
dropout=0.
# Seed configuration
set_seed=True
seed=10
disable_correction=False
class DrugGENAKT1Config(DrugGENConfig):
submodel='DrugGEN'
inference_model="/home/user/app/experiments/models/DrugGEN-akt1/"
train_drug_smiles='/home/user/app/data/akt_train.smi'
max_atom=45
class DrugGENCDK2Config(DrugGENConfig):
submodel='DrugGEN'
inference_model="/home/user/app/experiments/models/DrugGEN-cdk2/"
train_drug_smiles='/home/user/app//data/cdk2_train.smi'
max_atom=38
class NoTargetConfig(DrugGENConfig):
submodel="NoTarget"
inference_model="/home/user/app/experiments/models/NoTarget/"
model_configs = {
"DrugGEN-AKT1": DrugGENAKT1Config(),
"DrugGEN-CDK2": DrugGENCDK2Config(),
"DrugGEN-NoTarget": NoTargetConfig(),
}
def function(model_name: str, num_molecules: int, seed_num: int):
'''
Returns:
image, metrics_df, file_path, basic_metrics, advanced_metrics
'''
if model_name == "DrugGEN-NoTarget":
model_name = "NoTarget"
config = model_configs[model_name]
config.sample_num = num_molecules
if config.sample_num > 250:
raise gr.Error("You have requested to generate more than the allowed limit of 250 molecules. Please reduce your request to 250 or fewer.")
if seed_num is None or seed_num.strip() == "":
config.seed = random.randint(0, 10000)
else:
try:
config.seed = int(seed_num)
except ValueError:
raise gr.Error("The seed must be an integer value!")
if model_name != "NoTarget":
model_name = "DrugGEN"
inferer = Inference(config)
start_time = time.time()
scores = inferer.inference() # This returns a DataFrame with specific columns
et = time.time() - start_time
score_df = pd.DataFrame({
"Runtime (seconds)": [et],
"Validity": [scores["validity"].iloc[0]],
"Uniqueness": [scores["uniqueness"].iloc[0]],
"Novelty (Train)": [scores["novelty"].iloc[0]],
"Novelty (Test)": [scores["novelty_test"].iloc[0]],
"Drug Novelty": [scores["drug_novelty"].iloc[0]],
"Max Length": [scores["max_len"].iloc[0]],
"Mean Atom Type": [scores["mean_atom_type"].iloc[0]],
"SNN ChEMBL": [scores["snn_chembl"].iloc[0]],
"SNN Drug": [scores["snn_drug"].iloc[0]],
"Internal Diversity": [scores["IntDiv"].iloc[0]],
"QED": [scores["qed"].iloc[0]],
"SA Score": [scores["sa"].iloc[0]]
})
# Create basic metrics dataframe
basic_metrics = pd.DataFrame({
"Validity": [scores["validity"].iloc[0]],
"Uniqueness": [scores["uniqueness"].iloc[0]],
"Novelty (Train)": [scores["novelty"].iloc[0]],
"Novelty (Test)": [scores["novelty_test"].iloc[0]],
"Drug Novelty": [scores["drug_novelty"].iloc[0]],
"Runtime (s)": [round(et, 2)]
})
# Create advanced metrics dataframe
advanced_metrics = pd.DataFrame({
"QED": [scores["qed"].iloc[0]],
"SA Score": [scores["sa"].iloc[0]],
"Internal Diversity": [scores["IntDiv"].iloc[0]],
"SNN ChEMBL": [scores["snn_chembl"].iloc[0]],
"SNN Drug": [scores["snn_drug"].iloc[0]],
"Max Length": [scores["max_len"].iloc[0]]
})
output_file_path = f'/home/user/app/experiments/inference/{model_name}/inference_drugs.txt'
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")[:-1]
rng = random.Random(config.seed)
if num_molecules > 12:
selected_molecules = rng.choices(generated_molecule_list, k=12)
else:
selected_molecules = generated_molecule_list
selected_molecules = [Chem.MolFromSmiles(mol) for mol in selected_molecules if Chem.MolFromSmiles(mol) is not None]
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, new_path, basic_metrics, advanced_metrics
with gr.Blocks(theme=gr.themes.Ocean()) as demo:
# Add custom CSS for styling
gr.HTML("""
""")
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")
gr.HTML("""
""")
with gr.Accordion("About DrugGEN Models", open=False):
gr.Markdown("""
## Model Variations
### DrugGEN-AKT1
This model is designed to generate molecules targeting the human AKT1 protein (UniProt ID: P31749).
### DrugGEN-CDK2
This model is designed to generate molecules targeting the human CDK2 protein (UniProt ID: P24941).
### DrugGEN-NoTarget
This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein. It's useful for:
- Exploring chemical space
- Generating diverse scaffolds
- Creating molecules with drug-like properties
For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
""")
with gr.Accordion("Understanding the Metrics", open=False):
gr.Markdown("""
## Evaluation Metrics
### Basic Metrics
- **Validity**: Percentage of generated molecules that are chemically valid
- **Uniqueness**: Percentage of unique molecules among valid ones
- **Runtime**: Time taken to generate the requested molecules
### Novelty Metrics
- **Novelty (Train)**: Percentage of molecules not found in the training set
- **Novelty (Test)**: Percentage of molecules not found in the test set
- **Drug Novelty**: Percentage of molecules not found in known inhibitors of the target protein
### Structural Metrics
- **Max Length**: Maximum component length in the generated molecules
- **Mean Atom Type**: Average distribution of atom types
- **Internal Diversity**: Diversity within the generated set (higher is more diverse)
### Drug-likeness Metrics
- **QED (Quantitative Estimate of Drug-likeness)**: Score from 0-1 measuring how drug-like a molecule is (higher is better)
- **SA Score (Synthetic Accessibility)**: Score from 1-10 indicating ease of synthesis (lower is easier)
### Similarity Metrics
- **SNN ChEMBL**: Similarity to ChEMBL molecules (higher means more similar to known drug-like compounds)
- **SNN Drug**: Similarity to known drugs (higher means more similar to approved drugs)
""")
model_name = gr.Radio(
choices=("DrugGEN-AKT1", "DrugGEN-CDK2", "DrugGEN-NoTarget"),
value="DrugGEN-AKT1",
label="Select Target Model",
info="Choose which protein target or general model to use for molecule generation"
)
num_molecules = gr.Slider(
minimum=10,
maximum=250,
value=100,
step=10,
label="Number of Molecules to Generate",
info="This space runs on a CPU, which may result in slower performance. Generating 200 molecules takes approximately 6 minutes. Therefore, We set a 250-molecule cap. On a GPU, the model can generate 10,000 molecules in the same amount of time. Please check our GitHub repo for running our models on GPU."
)
seed_num = gr.Textbox(
label="Random Seed (Optional)",
value="",
info="Set a specific seed for reproducible results, or leave empty for random generation"
)
submit_button = gr.Button(
value="Generate Molecules",
variant="primary",
size="lg"
)
with gr.Column(scale=2):
basic_metrics_df = gr.Dataframe(
headers=["Validity", "Uniqueness", "Novelty (Train)", "Novelty (Test)", "Novelty (Drug)", "Runtime (s)"],
elem_id="basic-metrics"
)
advanced_metrics_df = gr.Dataframe(
headers=["QED", "SA Score", "Internal Diversity", "SNN (ChEMBL)", "SNN (Drug)", "Max Length"],
elem_id="advanced-metrics"
)
file_download = gr.File(
label="Download All Generated Molecules (SMILES format)",
)
image_output = gr.Image(
label="Structures of Randomly Selected Generated Molecules",
elem_id="molecule_display"
)
gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
submit_button.click(
function,
inputs=[model_name, num_molecules, seed_num],
outputs=[
image_output,
file_download,
basic_metrics_df,
advanced_metrics_df
],
api_name="inference"
)
#demo.queue(concurrency_count=1)
demo.queue()
demo.launch()